Datasets:
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- .gitattributes +73 -0
- .gitignore +51 -0
- README.md +120 -0
- expression/SOURCES.md +459 -0
- expression/context_synonyms.tsv +449 -0
- expression/protein_abundance_mmu.tsv.gz +3 -0
- expression/reference_expression.tsv.gz +3 -0
- expression/reference_expression_mmu.tsv.gz +3 -0
- expression/reference_expression_toil.parquet +3 -0
- expression/toil_floors.tsv +89 -0
- expression/toil_floors_mmu.tsv +72 -0
- expression/toil_matrix.npz +3 -0
- expression/toil_matrix_mmu.npz +3 -0
- expression/tumor_expression_mmu.tsv.gz +3 -0
- hla_loh/SOURCES.md +134 -0
- hla_loh/do_to_tcga_barcode.tsv +712 -0
- hla_loh/escape_genes.parquet +3 -0
- hla_loh/gie_allele.parquet +3 -0
- hla_loh/gie_sample.parquet +3 -0
- hla_loh/harmonize.py +177 -0
- hla_loh/hla_typing_callers.parquet +3 -0
- hla_loh/perallele_tcga_loh.parquet +3 -0
- hla_loh/sample_metadata.parquet +3 -0
- homology/HOM_MouseHumanSequence.rpt.gz +3 -0
- homology/SOURCES.md +50 -0
- icb_cohorts/COHORTS.md +77 -0
- icb_cohorts/SOURCES.md +45 -0
- icb_cohorts/harmonize_typing.py +199 -0
- icb_cohorts/harmonized/manifest.tsv +7 -0
- icb_cohorts/harmonized/typing.parquet +3 -0
- immunogenicity/SOURCES.md +726 -0
- immunogenicity/chowell_iedb_full.tsv.gz +3 -0
- immunogenicity/chowell_iedb_full_matched.tsv.gz +3 -0
- immunogenicity/chowell_rebuilt.tsv.gz +3 -0
- immunogenicity/chowell_rebuilt_hla_matched.tsv.gz +3 -0
- immunogenicity/corpus_iedb_mhc2.log +24 -0
- immunogenicity/corpus_iedb_mhc2.parquet +3 -0
- immunogenicity/hla_pop_freqs.tsv.gz +3 -0
- immunogenicity/iedb_labeled.tsv.gz +3 -0
- immunogenicity/immunogenicity_legacy_arms.tsv.gz +3 -0
- immunogenicity/kesmir_rebuilt.tsv.gz +3 -0
- immunogenicity/kesmir_rebuilt_hla_matched.tsv.gz +3 -0
- immunogenicity/neoag_tcell_balanced_mhc2_human.tsv.gz +3 -0
- immunogenicity/neoag_tcell_balanced_mhc2_mouse.tsv.gz +3 -0
- immunogenicity/neoprecis_anchor_triplets.parquet +3 -0
- immunogenicity/pathogen_tcell_balanced_mhc1_human.tsv.gz +3 -0
- immunogenicity/pathogen_tcell_balanced_mhc1_mouse.tsv.gz +3 -0
- immunogenicity/pathogen_tcell_balanced_mhc2_human.tsv.gz +3 -0
- immunogenicity/pathogen_tcell_balanced_mhc2_mouse.tsv.gz +3 -0
- immunogenicity/pathogen_tcell_mhc1_human.tsv.gz +3 -0
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# Audio files - uncompressed
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# Binary/large deposit formats that the HuggingFace default set above does not cover. Without these
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# a future .xlsx supplement or an uncompressed .fasta lands as a PLAIN BLOB in a dataset repo --
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# the mirror-image of the bug that once let a shipped .fasta escape a *.fasta ignore rule by its
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# extension. Nothing tracked today is affected: 0 .xlsx and 0 bare .fasta are tracked.
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# *.tsv and *.csv are deliberately NOT here: five small tables are tracked as plain blobs on
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# purpose (context_synonyms.tsv, toil_floors{,_mmu}.tsv, harmonized/manifest.tsv, summary.tsv),
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# and an LFS pattern would convert a 402-byte manifest for nothing. Compressed *.tsv.gz is already
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# covered above.
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*.xlsx filter=lfs diff=lfs merge=lfs -text
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*.fasta filter=lfs diff=lfs merge=lfs -text
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dump/
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src/
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.DS_Store
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**/.DS_Store
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# raw downloads (never pushed)
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raw/
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# MixMHCpred3 comparison material (never pushed). Two of the four files are publisher
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# supplementary .xlsx — copyrighted, not ours to redistribute — and the directory's only
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# provenance is three bare URLs in benchmark/mixmhcpred3_ref.md, which does not meet the
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# SOURCES.md standard the rest of this repo now holds. Kept on disk, off the Hub.
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benchmark/
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private/
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# NB: tcga/ is PUBLIC (neoantigen predictions derived from open-access TCGA somatic +
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# expression data; per-barcode HLA/neoantigens as published by TCIA / Thorsson 2018).
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# internal working table, not published
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summary_all_datasets.tsv
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# whole-proteome window indexes: derived, rebuilt on demand by mhcmatch, never published.
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# Eight (species, length) cells were 17 GB -- 41% of this dataset -- against a 55 MB FASTA that
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# reproduces any one of them in seconds, and fetching one measured SLOWER than building it
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# (mouse L=9: 1.49 GB in 47.0 s unauthenticated, against 25.0 s to build). Withdrawn 2026-09-06.
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proteome_index/
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# ICB cohort assembly, staged 2026-09-10. Our harmonised tables under icb_cohorts/harmonized/
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# are published (typing.parquet and manifest.tsv -- our own six-allele genotypes over public
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# typings, and a source->count manifest). The publisher supplementary workbooks under
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# icb_cohorts/raw/ are copyrighted and not ours to redistribute; they stay ignored permanently.
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icb_cohorts/raw/
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# NB: hla_loh/ is PUBLIC and tracked from 2026-09-16. It holds per-allele HLA-I loss-of-
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# heterozygosity over TCGA/PCAWG and Hartwig, derived from open-access data exactly as tcga/ is:
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# ICGC donor ids and TCGA barcodes are public pseudonymous research identifiers, and PCAWG's
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# `donor_wgs_exclusion_white_gray` is its published QC whitelist flag. An earlier rule here called
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# the directory a re-identification risk; that was wrong and is withdrawn. What genuinely may not
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# ship is the three copyrighted Springer Nature workbooks, so they -- and the third-party PCAWG
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# sample sheet -- live in `raw/hla_loh/` under the `raw/` rule above, and `hla_loh/harmonize.py`
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# rebuilds the published parquets from them. Facts are not copyrightable; a publisher's workbook is.
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# The mimicry k-mer cache. mhcmatch WRITES this itself (`mimicry.py` under
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# $MHCMATCH_CALIBRATION_CACHE), keyed by a blake2b of the schema epoch, class, species, lengths and
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# each deposit's name and size -- so it is derived data that any consumer regenerates on demand, and
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# `docs/cli/environment.rst` says in as many words that it is safe to delete. 2.07 GB across 50
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# files (two cache cells, human and mouse), tracked and pushed with no SOURCES.md entry. Same call as the 17 GB `proteome_index/` withdrawal in 2f76f91, for the same
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# reason. NOTE: this rule stops it being re-added; the 50 files already tracked need a
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# `git rm --cached`, which is a push to the Hub and so is the author's to make.
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mimicry_refs/
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README.md
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---
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pretty_name: pMHC Data Compendium
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license: other
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license_name: mixed-upstream
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license_link: https://huggingface.co/datasets/isalgo/pmhc_data#licensing
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language:
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- en
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tags:
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- immunology
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- immunopeptidomics
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- neoantigen
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- mhc
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- hla
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- t-cell
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- cancer-vaccine
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- proteomics
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size_categories:
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- 10M<n<100M
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---
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# pMHC Data Compendium
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| 22 |
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| 23 |
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Harmonized peptide–MHC data for neoantigen scoring: presentation, immunogenicity labels,
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| 24 |
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self/foreign reference peptidomes, expression, and clinical-outcome cohorts. Human and mouse.
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| 25 |
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| 26 |
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Every table here is derived from public sources. Per-file provenance, filtering rules, verified
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| 27 |
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PubMed citations and row-level audits live in the `SOURCES.md` of each directory — that is the
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authoritative record, not this page.
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## Contents
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| 31 |
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| 32 |
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| Directory | What is in it |
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| 33 |
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|---|---|
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| 34 |
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| [`pmhc/`](pmhc) | Epitope–MHC binding records — the presentation panel (`pmhc_full`, and `pmhc_shortlist` at ≥2 references) |
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| 35 |
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| [`immunogenicity/`](immunogenicity) | T-cell immunogenicity corpora: immunogenic vs **self** ligands and vs **non-self**, each with an HLA-matched variant; labelled set; HLA population frequencies |
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| 36 |
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| [`neoantigens/`](neoantigens) | Tested and candidate neoantigens — minigene, epitope-level and per-mutation screens, human and mouse |
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| 37 |
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| [`ligandome/`](ligandome) | Foreign and tissue reference peptidomes — viral ligands, pan-viral ORFs, cancer-testis targets, per-tissue corpora |
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| 38 |
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| [`thymus/`](thymus) | Thymic self-peptidome and thymic expression, human and mouse — the central-tolerance reference |
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| 39 |
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| [`expression/`](expression) | Reference expression, human and mouse, plus a single-pipeline table and mouse protein abundance |
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| 40 |
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| [`proteome/`](proteome) | Human and mouse reference proteomes plus 16 bacterial/viral proteomes for mimicry scans |
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| 41 |
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| [`homology/`](homology) | Cross-species homology mappings |
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| 42 |
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| [`tcga/`](tcga) | Expressed mutant-peptide–HLA binders across 8,505 donors |
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| 43 |
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| [`hla_loh/`](hla_loh) | HLA-I loss of heterozygosity and genetic immune escape over 6,319 tumours, with three-caller typing concordance |
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| 44 |
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| [`icb_cohorts/`](icb_cohorts) | Harmonized checkpoint-blockade cohorts with outcome |
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| 45 |
+
| [`vaccines/`](vaccines) | Shipped personalized-vaccine cassettes and their immune monitoring |
|
| 46 |
+
|
| 47 |
+
Formats are mixed by design: gzipped TSV for the large harmonized tables, parquet where the schema
|
| 48 |
+
is wide or typed, `.npz` for dense matrices, gzipped FASTA for proteomes.
|
| 49 |
+
|
| 50 |
+
## Shared schema
|
| 51 |
+
|
| 52 |
+
The immunogenicity tables share one schema:
|
| 53 |
+
|
| 54 |
+
```
|
| 55 |
+
peptide, mhc_a, mhc_b, mhc_class, mhc_species, source_species, host_species,
|
| 56 |
+
assay_type, immunogenicity{1,0,null}, expression, affinity, affinity_wt,
|
| 57 |
+
agretopicity, foreignness, validated, dataset_origin, reference_id, n_references
|
| 58 |
+
```
|
| 59 |
+
|
| 60 |
+
plus `patient_id, cancer_type` on the neoantigen tables. **Canonical columns hold as-reported
|
| 61 |
+
values only**; anything predicted lives in a `pred_*` column, so `affinity` is always experimental.
|
| 62 |
+
`affinity` units differ by source and MHC alleles are as-reported.
|
| 63 |
+
|
| 64 |
+
## Two things to know before filtering
|
| 65 |
+
|
| 66 |
+
- **`neoantigens/neoag_tested.tsv.gz` contains mouse rows labelled as human.** 6,186 CEDAR rows
|
| 67 |
+
carry mouse MHC alleles (`H2-Db` 1,020, `H2-Kb` 932, `H2-b class I` 719, `H2-Kd` 525,
|
| 68 |
+
`H2-d class II` 505, …) while their `mhc_species` reads `HomoSapiens` and `host_species` reads
|
| 69 |
+
`human`. `SIINFEKL`, the canonical H-2Kb ovalbumin epitope, is among them. Filtering that table
|
| 70 |
+
on `mhc_species` therefore silently admits mouse pMHC. The defect is upstream and is left
|
| 71 |
+
unfixed; `neoantigens/neoag_tested_mmu.tsv.gz` is the correctly-labelled murine set.
|
| 72 |
+
- **`neoag_tested_{mmu,hsa}` overlap `neoag_tested.tsv.gz` by design** — all 2,273 peptides appear
|
| 73 |
+
in both, since both are IEDB-derived. Dedupe on peptide × MHC when joining.
|
| 74 |
+
|
| 75 |
+
## Loading
|
| 76 |
+
|
| 77 |
+
```python
|
| 78 |
+
from huggingface_hub import hf_hub_download
|
| 79 |
+
import pandas as pd
|
| 80 |
+
|
| 81 |
+
path = hf_hub_download("isalgo/pmhc_data", "pmhc/pmhc_shortlist.tsv.gz", repo_type="dataset")
|
| 82 |
+
df = pd.read_csv(path, sep="\t")
|
| 83 |
+
```
|
| 84 |
+
|
| 85 |
+
Grab one directory rather than the whole repo:
|
| 86 |
+
|
| 87 |
+
```bash
|
| 88 |
+
hf download isalgo/pmhc_data --repo-type dataset --include "neoantigens/*" --local-dir ./pmhc_data
|
| 89 |
+
```
|
| 90 |
+
|
| 91 |
+
[`mhcmatch`](https://pypi.org/project/mhcmatch/) consumes this compendium directly — `mhcmatch
|
| 92 |
+
bootstrap` stages the panel and reference tables from here.
|
| 93 |
+
|
| 94 |
+
## Upstream sources
|
| 95 |
+
|
| 96 |
+
Which table came from which, and under what filtering, is in the `SOURCES.md` of each directory.
|
| 97 |
+
|
| 98 |
+
| | |
|
| 99 |
+
|---|---|
|
| 100 |
+
| IEDB | https://www.iedb.org |
|
| 101 |
+
| CEDAR | https://cedar.iedb.org |
|
| 102 |
+
| HLA Ligand Atlas | https://hla-ligand-atlas.org |
|
| 103 |
+
| UniProt | https://www.uniprot.org |
|
| 104 |
+
| GTEx | https://gtexportal.org |
|
| 105 |
+
| Human Protein Atlas | https://www.proteinatlas.org |
|
| 106 |
+
| FANTOM5 | https://fantom.gsc.riken.jp/5/ |
|
| 107 |
+
| UCSC Xena | https://xenabrowser.net |
|
| 108 |
+
| GDC / TCGA | https://portal.gdc.cancer.gov |
|
| 109 |
+
| ICGC / PCAWG | https://dcc.icgc.org |
|
| 110 |
+
| Hartwig Medical Foundation | https://www.hartwigmedicalfoundation.nl |
|
| 111 |
+
| Allele Frequency Net Database | https://www.allelefrequencies.net |
|
| 112 |
+
| PRIDE | https://www.ebi.ac.uk/pride/ |
|
| 113 |
+
| MassIVE | https://massive.ucsd.edu |
|
| 114 |
+
| GEO | https://www.ncbi.nlm.nih.gov/geo/ |
|
| 115 |
+
|
| 116 |
+
## Licensing
|
| 117 |
+
|
| 118 |
+
Each upstream source carries its own terms; this compendium redistributes derived, harmonized
|
| 119 |
+
tables and does not relicense them. Check the `SOURCES.md` of the directory you are using before
|
| 120 |
+
redistributing, and cite the primary publications recorded there rather than this repository.
|
expression/SOURCES.md
ADDED
|
@@ -0,0 +1,459 @@
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|
|
|
|
|
| 1 |
+
# `expression/` — sources
|
| 2 |
+
|
| 3 |
+
Two human tables. `reference_expression.tsv.gz` holds three references that are **never merged** — different measurements, different units, and
|
| 4 |
+
a gene-keyed table and a peptide-keyed one are not the same object.
|
| 5 |
+
|
| 6 |
+
| | |
|
| 7 |
+
|---|---|
|
| 8 |
+
| file | `reference_expression.tsv.gz` |
|
| 9 |
+
| rows | 6,681,814 |
|
| 10 |
+
| schema | `key`, `key_type`, `source`, `context`, `median_tpm`, `q25_tpm`, `q75_tpm`, `n` |
|
| 11 |
+
| provenance | **experimental** (all three sources are measured RNA-seq); the per-context median/IQR/`n` are **computed** summaries of it |
|
| 12 |
+
|
| 13 |
+
| `source` | `key_type` | rows | contexts | what a row is |
|
| 14 |
+
|---|---|--:|--:|---|
|
| 15 |
+
| `gtex` | `gene` | 3,955,284 | 53 | a gene's expression across normal tissue — the **safety** read |
|
| 16 |
+
| `hpa_consensus` | `gene` | 1,025,751 | 51 | the same read over HPA's tissue panel — **the only one that contains thymus** |
|
| 17 |
+
| `tcga` | `peptide` | 1,700,779 | 19 | a mutant peptide's expression by tumour type — the **target** read |
|
| 18 |
+
|
| 19 |
+
Total 6,681,814 rows.
|
| 20 |
+
|
| 21 |
+
`key_type` is the join key and it differs by source on purpose: GTEx and HPA answer "is this gene on
|
| 22 |
+
in normal tissue", TCGA answers "is this neoantigen transcribed in this tumour". A caller that treats
|
| 23 |
+
them as one column is asking a question none of them answers. GTEx and HPA are both gene-keyed but
|
| 24 |
+
are still not interchangeable — see the units caveat in the HPA section below.
|
| 25 |
+
|
| 26 |
+
Read through `mhcmatch.expression` (`lookup`, `tissues`, `tumor_types`, `safety_profile`), which
|
| 27 |
+
keeps them apart. Fetched by `mhcmatch bootstrap --reference` (~115 MB).
|
| 28 |
+
|
| 29 |
+
---
|
| 30 |
+
|
| 31 |
+
## `reference_expression_toil.tsv.gz` — GTEx and TCGA on one pipeline, in one unit
|
| 32 |
+
|
| 33 |
+
Added 2026-08-26. **This is the table to use when a normal tissue and a tumour type have to be
|
| 34 |
+
compared, or when one supplies a scale for the other.**
|
| 35 |
+
|
| 36 |
+
| | |
|
| 37 |
+
|---|---|
|
| 38 |
+
| rows | **5,037,966** |
|
| 39 |
+
| schema | `key`, `key_type`, `source`, `context`, `median_tpm`, `q25_tpm`, `q75_tpm`, `n`, `unit` |
|
| 40 |
+
| keys | 58,581 gene symbols (`key_type = gene` on every row) |
|
| 41 |
+
| `source` / contexts | `toil_gtex` — 53 GTEx tissues, 3,104,793 rows · `toil_tcga` — 33 TCGA studies, 1,933,173 rows |
|
| 42 |
+
| `unit` | `TPM` on every row |
|
| 43 |
+
| size | 38.6 MB |
|
| 44 |
+
| provenance | **experimental** — RSEM TPM from RNA-seq; the per-context median/IQR/`n` are **computed** summaries |
|
| 45 |
+
| built by | `bench/neoag/expression_toil.py` in `2026-mhcmatch-benchmark` |
|
| 46 |
+
|
| 47 |
+
**Why it exists.** `reference_expression.tsv.gz` carries GTEx in **TPM** and TCGA in **RSEM
|
| 48 |
+
upper-quartile normalised counts**, both written into a column called `median_tpm`. They are each
|
| 49 |
+
correct for their own question, but they cannot be put on one axis: a scale taken from one and
|
| 50 |
+
divided into an abundance from the other is a units error no caller can detect, because the column
|
| 51 |
+
name says TPM in both cases. This table removes the problem at the source rather than by converting
|
| 52 |
+
one into the other — a conversion that is not available from the deposited matrix, since RSEM
|
| 53 |
+
normalised counts are proportional to abundance x effective length and the effective lengths are not
|
| 54 |
+
published with it.
|
| 55 |
+
|
| 56 |
+
**Origin.** The UCSC Xena / Toil recompute, which put GTEx, TCGA and TARGET through a **single RSEM
|
| 57 |
+
pipeline against one annotation (GENCODE v23)** and publishes gene-level TPM for all of them, so a
|
| 58 |
+
GTEx tissue median and a TCGA study median differ only in the samples that went in.
|
| 59 |
+
|
| 60 |
+
| input | file | bytes |
|
| 61 |
+
|---|---|--:|
|
| 62 |
+
| expression matrix | `TcgaTargetGtex_rsem_gene_tpm.gz` | 1,323,254,426 |
|
| 63 |
+
| sample phenotype | `TcgaTargetGTEX_phenotype.txt.gz` | 135,753 |
|
| 64 |
+
| gene annotation | `probeMap/gencode.v23.annotation.gene.probemap` | 3,244,244 |
|
| 65 |
+
|
| 66 |
+
```
|
| 67 |
+
base=https://toil-xena-hub.s3.us-east-1.amazonaws.com/download
|
| 68 |
+
curl -L -o xena/TcgaTargetGtex_rsem_gene_tpm.gz $base/TcgaTargetGtex_rsem_gene_tpm.gz
|
| 69 |
+
curl -L -o xena/TcgaTargetGTEX_phenotype.txt.gz $base/TcgaTargetGTEX_phenotype.txt.gz
|
| 70 |
+
curl -L -o xena/gencode.v23.annotation.gene.probemap \\
|
| 71 |
+
"$base/probeMap%2Fgencode.v23.annotation.gene.probemap"
|
| 72 |
+
python bench/neoag/expression_toil.py --out expression/reference_expression_toil.parquet
|
| 73 |
+
```
|
| 74 |
+
|
| 75 |
+
> Vivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J, Novak A, Pfeil J, Narkizian J, Deran AD,
|
| 76 |
+
> Musselman-Brown A, Schmidt H, Amstutz P, Craft B, Goldman M, Rosenbloom K, Cline M, O'Connor B,
|
| 77 |
+
> Hanna M, Birger C, Kent WJ, Patterson DA, Joseph AD, Zhu J, Zaranek S, Getz G, Haussler D,
|
| 78 |
+
> Paten B. **Toil enables reproducible, open source, big biomedical data analyses.**
|
| 79 |
+
> *Nat Biotechnol* 2017;35(4):314-316.
|
| 80 |
+
> PMID [28398314](https://pubmed.ncbi.nlm.nih.gov/28398314/) ·
|
| 81 |
+
> PMC5546205 · doi:[10.1038/nbt.3772](https://doi.org/10.1038/nbt.3772)
|
| 82 |
+
|
| 83 |
+
> Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D,
|
| 84 |
+
> Brooks AN, Zhu J, Haussler D. **Visualizing and interpreting cancer genomics data via the Xena
|
| 85 |
+
> platform.** *Nat Biotechnol* 2020;38(6):675-678.
|
| 86 |
+
> PMID [32444850](https://pubmed.ncbi.nlm.nih.gov/32444850/) ·
|
| 87 |
+
> PMC7386072 · doi:[10.1038/s41587-020-0546-8](https://doi.org/10.1038/s41587-020-0546-8)
|
| 88 |
+
|
| 89 |
+
**The TCGA study code is joined through the barcode, never through the disease name.** Xena labels a
|
| 90 |
+
TCGA sample with a disease name (`Uveal Melanoma`); the rest of this compendium keys on the study
|
| 91 |
+
abbreviation (`UVM`). A hand-written name->code crosswalk can be silently wrong, so the code is taken
|
| 92 |
+
from PanCanAtlas `merged_sample_quality_annotations.tsv` on the 15-character sample barcode, which is
|
| 93 |
+
exact. **17,474 of 19,131 matrix columns are used**: 9,616 TCGA tumour aliquots and 7,862 GTEx
|
| 94 |
+
samples. TARGET (734 columns) is dropped -- paediatric, and none of its diseases has a matched normal
|
| 95 |
+
here -- as are TCGA adjacent normals and aliquots the source flags `Do_not_use`.
|
| 96 |
+
|
| 97 |
+
**Upstream values are `log2(TPM + 0.001)` and are inverted on build**, so what is stored is TPM.
|
| 98 |
+
|
| 99 |
+
**Caveats.**
|
| 100 |
+
- **The GTEx contexts are Toil-era, not v11.** `reference_expression.tsv.gz` is GTEx v11; this is the
|
| 101 |
+
earlier release Toil reprocessed, so the two GTEx halves are neither the same samples nor the same
|
| 102 |
+
depth. They are also not spelled the same: Toil writes `Skin - Sun Exposed (Lower Leg)` and GTEx
|
| 103 |
+
v11 writes `Skin - Sun Exposed (Lower leg)`. **A context string cannot be carried from one table
|
| 104 |
+
to the other without checking.**
|
| 105 |
+
- **Three tissues are thin and are kept with their `n` rather than dropped**: Bladder (9),
|
| 106 |
+
Cervix - Ectocervix (6), Fallopian Tube (5). They are the matched normals of BLCA, OV and CESC, so
|
| 107 |
+
dropping them would remove the safety read for three tumour types. `Cervix - Endocervix` has fewer
|
| 108 |
+
than five samples and is absent; CESC still resolves through Ectocervix. Every TCGA study clears 36.
|
| 109 |
+
- **353 of 58,581 symbols carry more than one Ensembl gene id** (0.6 %). The row kept per
|
| 110 |
+
(symbol, context) is the one with the **highest median**, not a sum across loci: a lookup asks
|
| 111 |
+
whether a gene is on in this context, and the dominant locus answers it without inventing a total
|
| 112 |
+
across paralogues. 164,862 duplicate rows were collapsed this way.
|
| 113 |
+
- `n` is a **sample** count here, as in the GTEx half, and not the transcript count the mouse table's
|
| 114 |
+
`n` is.
|
| 115 |
+
|
| 116 |
+
**What a floor built from it looks like**, as the 25th percentile of non-zero gene medians:
|
| 117 |
+
TCGA runs 0.1200 (KICH) to 0.3700 (LAML) TPM, a **3.08x** spread over 33 studies; GTEx runs 0.1000
|
| 118 |
+
(Whole Blood) to 0.4000 (Testis), **4.00x** over 53 tissues. Pooled, TCGA is 0.1800 TPM over 882,959
|
| 119 |
+
non-zero gene rows and GTEx 0.2700 over 1,402,705. **A tumour's own floor is consistently below its
|
| 120 |
+
matched normal's** -- SKCM 0.1600 against skin 0.3050 (0.52x), BLCA 0.1700 against bladder 0.3600
|
| 121 |
+
(0.47x), LUAD 0.2000 against lung 0.3500 (0.57x), BRCA 0.1900 against breast 0.3000 (0.63x).
|
| 122 |
+
|
| 123 |
+
**Format: parquet, and the record is not in the bootstrap set.** As gzipped TSV this table was
|
| 124 |
+
70.5 MB and 0.5 s to read with polars (5.2 s with the standard library); dictionary-encoded and
|
| 125 |
+
zstd-compressed it is **38.6 MB and 0.04 s**. On a link measured here at 154 kB/s that is three and
|
| 126 |
+
a half minutes of download saved, which is the difference between a bootstrap that finishes and one
|
| 127 |
+
that looks like it has hung. `mhcmatch bootstrap --reference` stages only the three companions
|
| 128 |
+
below — 6.6 MB between them, carrying the same numbers — and never this file.
|
| 129 |
+
|
| 130 |
+
**Label arrays in the `.npz` are fixed-width unicode, not `dtype=object`.** An object array is
|
| 131 |
+
pickled, and `np.load` needs `allow_pickle=True` to read it back; a pickled array in a file fetched
|
| 132 |
+
over the network executes on load. The deposit is written so that flag is never needed.
|
| 133 |
+
|
| 134 |
+
### Its three companions, and why scoring never reads the table itself
|
| 135 |
+
|
| 136 |
+
Added 2026-08-26 beside it. `reference_expression_toil.tsv.gz` is the record; these are what a
|
| 137 |
+
program actually opens.
|
| 138 |
+
|
| 139 |
+
| file | size | what it is |
|
| 140 |
+
|---|--:|---|
|
| 141 |
+
| `toil_matrix.npz` | 6,632,665 B | the whole table as a dense **58,581 gene x 86 context float32 matrix**, contexts keyed `"<source>|<context>"` |
|
| 142 |
+
| `toil_floors.tsv` | 8,365 B | 88 rows -- each context's abundance floor at q05, q10, q25, plus a pooled row per cohort |
|
| 143 |
+
| `context_synonyms.tsv` | 29,334 B | 448 rows, 184 aliases -- how a free-text origin becomes a context |
|
| 144 |
+
|
| 145 |
+
**The matrix exists so a lookup does not cost a whole table.** Answering *what is this gene in this
|
| 146 |
+
tumour type* from the TSV means building a five-million-entry dictionary of dictionaries: measured,
|
| 147 |
+
**5.20 s and 3,168 MB resident**. The matrix is **0.05 s and 29 MB** -- 100x the speed on 1/109 of
|
| 148 |
+
the memory -- and every lookup a resolution chain makes is one array index. Both return the same
|
| 149 |
+
floor to four decimals, which is how the matrix is checked against the table it came from. A gene absent from a context is `0.0` and not `nan`: every gene here was
|
| 150 |
+
measured in every context that cleared the sample threshold, so absence is silence, and silence is a
|
| 151 |
+
measurement. Whether a *gene* is in the reference at all is a separate question and is reported
|
| 152 |
+
separately -- silence and ignorance must not collapse into one number.
|
| 153 |
+
|
| 154 |
+
**`context_synonyms.tsv` is derived, not hand-written.** Long format, one target per row, so it
|
| 155 |
+
sorts and joins like data rather than holding lists in cells:
|
| 156 |
+
|
| 157 |
+
| alias_kind | where the aliases come from | rows |
|
| 158 |
+
|---|---|--:|
|
| 159 |
+
| `code` | the 33 TCGA studies in the matrix | 83 |
|
| 160 |
+
| `site` | Xena `_primary_site`, joined to the PanCanAtlas study code on the 15-character barcode | 218 |
|
| 161 |
+
| `disease` | Xena `detailed_category`, joined the same way | 83 |
|
| 162 |
+
| `gtex` | the 53 GTEx contexts in the matrix | 64 |
|
| 163 |
+
|
| 164 |
+
**One alias resolves to two things and each is its own row**, which is why the alias count and the
|
| 165 |
+
row count differ: 150 rows carry `target_kind = tcga_code` and 298 carry `gtex_context`, so
|
| 166 |
+
`hepatocellular` names both `LIHC` and `Liver`. 152 of the 448 rows are `derived:*` and 296 are
|
| 167 |
+
`curated:*`, and the `provenance` column says which on every row. The curated majority is the
|
| 168 |
+
matched-normal half and the adjectival forms a free-text origin actually arrives in
|
| 169 |
+
(`cervical`, `pancreatic`, `uterine corpus`); the derived half is what the matrix and the barcode
|
| 170 |
+
join supply on their own. Hand-writing this would have been wrong in the places nobody checks: **an organ is
|
| 171 |
+
more than one study more often than not** -- `Lung` is `LUAD` *and* `LUSC`, `Kidney` is three -- and a
|
| 172 |
+
crosswalk that picks one is silently wrong for the rest. The tumour-to-matched-normal half is carried
|
| 173 |
+
from `mhcmatch.expression.TUMOR_TISSUE` against the spellings this matrix uses, because Toil writes
|
| 174 |
+
`Brain - Frontal Cortex (Ba9)` where GTEx v11 writes `(BA9)` and an exact match drops it.
|
| 175 |
+
|
| 176 |
+
An alias that does not resolve **raises**. Falling back to the pooled reference would return a
|
| 177 |
+
plausible number computed from the wrong distribution with nothing downstream able to tell.
|
| 178 |
+
|
| 179 |
+
**A note on what these floors are for.** `c` is a property of a transcriptome, and the numerator is
|
| 180 |
+
one candidate's abundance. Where the numerator is not in reference TPM the two stop cancelling, and
|
| 181 |
+
the obvious repair -- a median of ratios against the reference -- **must not be estimated from a
|
| 182 |
+
candidate list**. A mutation reaches such a list only if it was called, and only where the gene was
|
| 183 |
+
seen in RNA, so the ratio measures that conditioning instead of the library: on three screens whose
|
| 184 |
+
columns are all deposited as TPM it returns 1.78, 2.18 and 3.15, never below 1. `mhcmatch` gates it
|
| 185 |
+
on coverage of the reference transcriptome for that reason, and a candidate list cannot clear it.
|
| 186 |
+
|
| 187 |
+
---
|
| 188 |
+
|
| 189 |
+
## Mouse — `reference_expression_mmu.tsv.gz` and `protein_abundance_mmu.tsv.gz`
|
| 190 |
+
|
| 191 |
+
Added 2026-08-21. The compendium's normal-tissue safety read was GTEx, and GTEx is human, so a mouse
|
| 192 |
+
epitope had no reference to be scored against. These two files are the mouse counterpart.
|
| 193 |
+
|
| 194 |
+
**They are two files, not one, and that is deliberate.** The human table's value column is literally
|
| 195 |
+
`median_tpm`. RNA and protein are different measurements in different units, and writing a SILAC
|
| 196 |
+
abundance into a `_tpm` column would be a units lie no caller could detect.
|
| 197 |
+
|
| 198 |
+
### `reference_expression_mmu.tsv.gz` — the GTEx analogue
|
| 199 |
+
|
| 200 |
+
| | |
|
| 201 |
+
|---|---|
|
| 202 |
+
| rows | **659,050** |
|
| 203 |
+
| schema | `key`, `key_type`, `source`, `context`, `median_tpm`, `q25_tpm`, `q75_tpm`, `n` — **column for column identical to the human file** |
|
| 204 |
+
| keys | 18,830 gene symbols (`key_type = gene`) |
|
| 205 |
+
| contexts | **35 adult tissues** |
|
| 206 |
+
| `source` | `fantom5_mouse` |
|
| 207 |
+
| provenance | **experimental** — CAGE tag counts; the per-gene median across transcripts and the quantile summary are **computed** |
|
| 208 |
+
|
| 209 |
+
**Origin.** EBI Expression Atlas [E-MTAB-3579](https://www.ebi.ac.uk/gxa/experiments/E-MTAB-3579),
|
| 210 |
+
the RIKEN FANTOM5 mouse CAGE atlas. `q25_tpm` / `median_tpm` / `q75_tpm` are taken from the Atlas'
|
| 211 |
+
own five-number summary per gene × group; they are not re-derived here.
|
| 212 |
+
|
| 213 |
+
> Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S,
|
| 214 |
+
> Hori F, Ishikawa-Kato S, Mungall CJ, Arner E, Baillie JK, Bertin N, Bono H, de Hoon M, Diehl AD,
|
| 215 |
+
> Dimont E, Freeman TC, Fujieda K, Hide W, Kaliyaperumal R, Katayama T, Lassmann T, Meehan TF,
|
| 216 |
+
> Nishikata K, Ono H, Rehli M, Sandelin A, Schultes EA, 't Hoen PAC, Tatum Z, Thompson M,
|
| 217 |
+
> Toyoda T, Wright DW, Daub CO, Itoh M, Carninci P, Hayashizaki Y, Forrest ARR, Kawaji H.
|
| 218 |
+
> **Gateways to the FANTOM5 promoter level mammalian expression atlas.**
|
| 219 |
+
> *Genome Biol* 2015;16:22.
|
| 220 |
+
> PMID [25723102](https://pubmed.ncbi.nlm.nih.gov/25723102/) ·
|
| 221 |
+
> doi:[10.1186/s13059-014-0560-6](https://doi.org/10.1186/s13059-014-0560-6)
|
| 222 |
+
|
| 223 |
+
**Only plain `adult` groups are kept** — 35 of the deposit's 170. GTEx is adult normal tissue;
|
| 224 |
+
folding an E14 embryo, a pregnancy timepoint or a lactating mammary gland into a "is this gene on in
|
| 225 |
+
normal tissue" safety read answers a different question. The other stages are in the source and are
|
| 226 |
+
recoverable by re-running without the filter.
|
| 227 |
+
|
| 228 |
+
**A parsing trap worth recording.** Atlas group labels are two `;`-separated parts and **the order
|
| 229 |
+
is not fixed**: 136 of the 170 are stage-first (`adult; thymus`) and 32 are tissue-first
|
| 230 |
+
(`adrenal gland; adult`). Taking either position blindly mislabels a third of the table — the first
|
| 231 |
+
build of this file produced contexts named `adult` and `whole body` that way. The stage is matched by
|
| 232 |
+
vocabulary wherever it sits, and the tissue is whatever remains.
|
| 233 |
+
|
| 234 |
+
**Checked against known biology, not assumed.** Of 35 tissues, thymus ranks **first** for `Prss16`
|
| 235 |
+
(31 TPM), `Psmb11` (88), `Dntt` (961), `Rag1`, `Cd8a` (131) and `Lck` (12) — thymic epithelium and
|
| 236 |
+
thymocyte genes. As a negative control `Cela1` peaks in pancreas at 225,010 TPM against 0.3 in
|
| 237 |
+
thymus.
|
| 238 |
+
|
| 239 |
+
**Caveats.**
|
| 240 |
+
- **`n` is 1 for every row.** FANTOM5 provides one library per adult tissue, so `q25`/`q75` describe
|
| 241 |
+
the spread across transcripts of a gene, **not across animals**. GTEx `n` runs to several hundred
|
| 242 |
+
donors. Do not read the two `n` columns as the same quantity.
|
| 243 |
+
- **`Aire` is 0 TPM in thymus here, and that is a detection floor, not biology.** Aire is expressed
|
| 244 |
+
in a rare mTEC subset; bulk CAGE at this depth cannot see it. Any promiscuous-expression question
|
| 245 |
+
needs sorted mTEC data, not this file.
|
| 246 |
+
- CAGE measures 5′-capped tag density. It is reported as TPM and behaves like expression, but it is
|
| 247 |
+
not RNA-seq TPM and the two are not numerically interchangeable across sources.
|
| 248 |
+
|
| 249 |
+
### `tumor_expression_mmu.tsv.gz` — the mouse TUMOUR read, and why it is a separate file
|
| 250 |
+
|
| 251 |
+
Added 2026-09-04. `reference_expression_mmu.tsv.gz` answers the *safety* half of the expression
|
| 252 |
+
question -- is this gene on in normal tissue -- and there was no counterpart for the *ranking* half.
|
| 253 |
+
Mouse has no TCGA, so a mouse neoantigen's own abundance fell back to the gene's normal-tissue
|
| 254 |
+
median, which is the number `expr_norm` already carries. Measured on the 923 class-I rows of the
|
| 255 |
+
IEDB mouse neoantigen deposit, `expr_lvl` and `expr_norm` came out **identical on 100 %** of the
|
| 256 |
+
rows lacking a deposited abundance: two fitted terms on one column.
|
| 257 |
+
|
| 258 |
+
| | |
|
| 259 |
+
|---|---|
|
| 260 |
+
| rows | **149,640** |
|
| 261 |
+
| schema | `key`, `key_type`, `source`, `context`, `median_tpm`, `q25_tpm`, `q75_tpm`, `n` — **column for column identical to the two files above** |
|
| 262 |
+
| keys | 24,940 gene symbols (`key_type = gene`) |
|
| 263 |
+
| contexts | **6 syngeneic tumour models**: B16F10, CT26, E0771, LLC, MC38, Panc02 |
|
| 264 |
+
| `source` | `gse245293_syngeneic` |
|
| 265 |
+
| `n` | **3 on every row** — three biological replicates per model, so `q25`/`q75` are a spread across *animals*, unlike the FANTOM5 file's spread across transcripts |
|
| 266 |
+
| provenance | **experimental** — RNA-seq counts; the length normalisation, the per-model quantiles and the gene-level aggregation are **computed** |
|
| 267 |
+
|
| 268 |
+
**Origin.** NCBI GEO [GSE245293](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE245293),
|
| 269 |
+
supplementary file `GSE245293_Normalized_count_table_tumor.txt.gz` (2.9 MB), staged at
|
| 270 |
+
`raw/expression_mouse/`. 23 samples in the series; the 18 `*_tumor_rep*` columns are used and the
|
| 271 |
+
8 CAF (cancer-associated fibroblast) columns are **not** -- those are stroma, not the tumour
|
| 272 |
+
transcriptome, and folding them in would answer a different question.
|
| 273 |
+
|
| 274 |
+
curl -O https://ftp.ncbi.nlm.nih.gov/geo/series/GSE245nnn/GSE245293/suppl/GSE245293_Normalized_count_table_tumor.txt.gz
|
| 275 |
+
python src/build_tumor_expression_mmu.py # in ~/vcs/projects/public_expression_peptidomes
|
| 276 |
+
|
| 277 |
+
> Comparison of syngeneic tumor models reveals different tumor microenvironments with distinct
|
| 278 |
+
> subsets of cancer-associated fibroblasts polarized by infiltrating T cells. GEO GSE245293,
|
| 279 |
+
> deposited 2023-10-13, released 2024-01-29. *Mus musculus*, expression profiling by high
|
| 280 |
+
> throughput sequencing.
|
| 281 |
+
|
| 282 |
+
**Units are computed and the file says so.** The deposit is **normalised counts** with a `Length`
|
| 283 |
+
column, not TPM. Length-normalising each sample and rescaling to a million gives a TPM-like
|
| 284 |
+
quantity comparable *within* this table; it is **not** interchangeable with FANTOM5 CAGE TPM or
|
| 285 |
+
with GTEx RSEM TPM. `mhcmatch.rank.expr_level` is unit-free when its floor is a quantile of the
|
| 286 |
+
same column, which is exactly why a floor for these contexts must be taken from this file.
|
| 287 |
+
|
| 288 |
+
**Checked against known biology, not assumed.** Three independent melanocytic markers separate the
|
| 289 |
+
melanoma line by three orders of magnitude: `Tyr` **398.9**, `Dct` **4,611.8** and `Pmel`
|
| 290 |
+
**4,636.7** TPM in B16F10 against ≤ 4.3 in all five other models. They would all move together if
|
| 291 |
+
the sample-to-model mapping were wrong.
|
| 292 |
+
|
| 293 |
+
**Colon markers were tried and do not work here; do not re-try them.** `Muc2` reads ~0 in every
|
| 294 |
+
model -- CT26 is an undifferentiated colon carcinoma and makes no goblet-cell mucin -- and
|
| 295 |
+
`Cdx1`/`Cdx2` do not separate CT26 and MC38 from the rest (Cdx2 is highest in Panc02, 8.8 against
|
| 296 |
+
CT26's 1.4). The expectations were wrong, not the data.
|
| 297 |
+
|
| 298 |
+
**Coverage of the IEDB mouse neoantigen deposit**, joining on model × gene symbol: **347 of 968
|
| 299 |
+
class-I rows (36 %)** and **304 of 522 class-II rows (58 %)**. The class-II figure is the reason
|
| 300 |
+
this file exists -- that arm previously had a deposited abundance on 13 % of its rows.
|
| 301 |
+
|
| 302 |
+
**Two models it does not carry** that the neoantigen deposit names often: `P815` (89 rows) and the
|
| 303 |
+
`T3`/`CMS5`/`Meth A` fibrosarcomas (150 rows between them). Those still take the pooled floor.
|
| 304 |
+
|
| 305 |
+
### `PRJNA1271699` — located, not fetched
|
| 306 |
+
|
| 307 |
+
[PRJNA1271699](https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1271699), *"Mouse syngeneic model
|
| 308 |
+
RNAseq sequencing"* (CrownBio), submitted 2025-06-04: **120 runs over 12 models × 10 replicates**
|
| 309 |
+
-- A20, B16BL6, B16F10, CT26, EMT6, H22, Hepa16, LL2, MC38, Pan02, RM1, Renca -- paired-end
|
| 310 |
+
Illumina NovaSeq 6000, **418 GB** of raw reads. It would cover Renca and EMT6, which nothing here
|
| 311 |
+
does.
|
| 312 |
+
|
| 313 |
+
**NCBI carries raw reads only; there is no processed matrix.** The BioProject abstract advertises
|
| 314 |
+
an interactive resource at `https://muscreen.crownbio.com/syngeneic_omics`, and **as of 2026-09-04
|
| 315 |
+
that URL is dead** -- `/syngeneic_omics` returns 404 and the host root returns a bare Apache
|
| 316 |
+
default page (403). So the processed tables the abstract promises are not currently retrievable,
|
| 317 |
+
and using this project means downloading 418 GB and running an alignment pipeline. Recorded here
|
| 318 |
+
so the cost is known rather than rediscovered.
|
| 319 |
+
|
| 320 |
+
**This is the source to re-process if these models are wanted here** -- it is the one that covers
|
| 321 |
+
P815, Renca and EMT6, which `tumor_expression_mmu.tsv.gz` does not.
|
| 322 |
+
|
| 323 |
+
# metadata only, no reads:
|
| 324 |
+
curl -sS "https://trace.ncbi.nlm.nih.gov/Traces/sra-db-be/runinfo?acc=PRJNA1271699"
|
| 325 |
+
|
| 326 |
+
### `toil_matrix_mmu.npz` + `toil_floors_mmu.tsv` — the harmonised mouse compendium
|
| 327 |
+
|
| 328 |
+
Added 2026-09-04. The mouse counterpart of `toil_matrix.npz` / `toil_floors.tsv`, same array names
|
| 329 |
+
and the same `source|context` key shape, so code written against the human pair reads this one.
|
| 330 |
+
|
| 331 |
+
| | |
|
| 332 |
+
|---|---|
|
| 333 |
+
| shape | **26,737 genes x 68 contexts**, dense float32 |
|
| 334 |
+
| `genes` | official NCBI symbol, synonyms resolved via Ensembl release-100 (= GENCODE M25) and NCBI Gene |
|
| 335 |
+
| `contexts` | `fantom5_mouse\|<tissue>` x 35 normal, `gse245293\|<model>` x 6 tumour, `prjna1271699+\|<model>` x 27 tumour |
|
| 336 |
+
| `values` | linear TPM-like, **harmonised across sources**; `NaN` = the gene is absent from that source, which is not the same as measured at zero |
|
| 337 |
+
| `n_samples` | 3 for GSE245293 (replicate **mean**), 1 for FANTOM5, 0 where the source reports none |
|
| 338 |
+
| `toil_floors_mmu.tsv` | 71 rows -- each context's floor at q05/q10/q25, plus a pooled row per source, same seven columns as the human file |
|
| 339 |
+
|
| 340 |
+
**Sources.** FANTOM5 mouse CAGE (EBI E-MTAB-3579); GEO **GSE245293** (6 syngeneic models x 3
|
| 341 |
+
replicates); and the CrownBio syngeneic panel, labelled `prjna1271699+` for NCBI BioProject
|
| 342 |
+
**PRJNA1271699** plus the further models that panel carries beyond the 12 the accession lists.
|
| 343 |
+
GSE245293 wins the five models both cover. The 27 filled models include **P815, Renca and EMT6**,
|
| 344 |
+
which no other deposit here reaches.
|
| 345 |
+
|
| 346 |
+
**Harmonised, and here is what that means.** The three sources sat on three scales -- pooled mean
|
| 347 |
+
`log2(1 + TPM)` of 1.721, 2.513 and 2.059. Each source's non-zero values are quantile-mapped onto
|
| 348 |
+
the mean of the three sources' quantile functions; **per source, not per context**, so
|
| 349 |
+
between-context structure survives. Zero maps to zero. The map is monotone and its grid spans each
|
| 350 |
+
source's support, so **every within-context rank is preserved exactly** -- which is what
|
| 351 |
+
`mhcmatch.rank.expr_level` consumes. Non-zero spread falls 0.577 -> 0.087 log2 units, and the
|
| 352 |
+
pooled q25 floor agrees to 0.714--0.717 across all three.
|
| 353 |
+
|
| 354 |
+
Two things it does not do. It cannot correct a **gene-specific** platform effect: `Actb` reads 3,032
|
| 355 |
+
in B16F10 and 10.4 in FANTOM5 thymus after harmonisation, because CAGE at that promoter genuinely
|
| 356 |
+
ranks it mid-distribution. And it cannot correct the **detection-rate** difference (65.2 / 78.1 /
|
| 357 |
+
65.0 % non-zero), because a monotone map that respects zero cannot move it.
|
| 358 |
+
|
| 359 |
+
**Absolute values are no longer any one source's own number.** That is the trade: comparability
|
| 360 |
+
across all 68 contexts, at the cost of each source's internal scale. The unharmonised per-source
|
| 361 |
+
tables are not deposited; `tumor_expression_mmu.tsv.gz` and `reference_expression_mmu.tsv.gz` above
|
| 362 |
+
remain the unharmonised reads of two of the three sources.
|
| 363 |
+
|
| 364 |
+
Built by `src/build_deposit_mmu.py` in `~/vcs/projects/public_expression_peptidomes`; the protocol
|
| 365 |
+
and its numbers are `results/expression_harmonization.md` there.
|
| 366 |
+
|
| 367 |
+
**For comparison, the human pair needed none of this.** Same measurement on `toil_matrix.npz`:
|
| 368 |
+
`toil_gtex` and `toil_tcga` pooled means differ by **0.011** log2 units, against 0.565 for the
|
| 369 |
+
mouse CAGE-vs-RNA-seq seam. Both human halves went through one Toil RSEM recompute, and it worked.
|
| 370 |
+
|
| 371 |
+
### `protein_abundance_mmu.tsv.gz` — the protein-level read
|
| 372 |
+
|
| 373 |
+
| | |
|
| 374 |
+
|---|---|
|
| 375 |
+
| rows | **133,848** |
|
| 376 |
+
| schema | `key`, `key_type`, `source`, `context`, `abundance`, `unit`, `n` |
|
| 377 |
+
| keys | 5,148 gene symbols |
|
| 378 |
+
| contexts | **26 tissues**, including thymus |
|
| 379 |
+
| `source` / `unit` | `geiger_silac_mouse` / `ppb_ibaq` |
|
| 380 |
+
| provenance | **experimental** — SILAC-normalised MS abundance |
|
| 381 |
+
|
| 382 |
+
**Origin.** EBI Expression Atlas [E-PROT-11](https://www.ebi.ac.uk/gxa/experiments/E-PROT-11).
|
| 383 |
+
|
| 384 |
+
> Geiger T, Velic A, Macek B, Lundberg E, Kampf C, Nagaraj N, Uhlen M, Cox J, Mann M.
|
| 385 |
+
> **Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse.**
|
| 386 |
+
> *Mol Cell Proteomics* 2013;12(6):1709–1722.
|
| 387 |
+
> PMID [23436904](https://pubmed.ncbi.nlm.nih.gov/23436904/) ·
|
| 388 |
+
> doi:[10.1074/mcp.M112.024919](https://doi.org/10.1074/mcp.M112.024919)
|
| 389 |
+
|
| 390 |
+
**Why keep a protein table at all.** A peptide can only be presented if the protein was translated,
|
| 391 |
+
so for immunopeptidome work protein abundance is the more direct evidence and RNA is a proxy. It is
|
| 392 |
+
much shallower — 5,148 genes against 18,830 — so it complements the RNA table rather than replacing
|
| 393 |
+
it.
|
| 394 |
+
|
| 395 |
+
### Why not E-PROT-13
|
| 396 |
+
|
| 397 |
+
[E-PROT-13](https://www.ebi.ac.uk/gxa/experiments/E-PROT-13) (Huttlin's mouse phosphorylation and
|
| 398 |
+
expression atlas) was the first candidate considered. It covers **9 tissues and does not include
|
| 399 |
+
thymus**, so it cannot serve either purpose here. Of the **65 mouse baseline experiments in
|
| 400 |
+
Expression Atlas, only six contain thymus**; E-MTAB-3579 (35 adult tissues) and E-PROT-11 (26) are
|
| 401 |
+
the only two of those with broad tissue coverage.
|
| 402 |
+
|
| 403 |
+
### Regenerate
|
| 404 |
+
|
| 405 |
+
cd ~/vcs/projects/public_expression_peptidomes && ./src/build_expression_mmu.py
|
| 406 |
+
|
| 407 |
+
---
|
| 408 |
+
|
| 409 |
+
## Human — the `hpa_consensus` arm, and why GTEx alone was not enough
|
| 410 |
+
|
| 411 |
+
Added 2026-08-21, in the same pass that built the mouse references.
|
| 412 |
+
|
| 413 |
+
**GTEx has no thymus.** Its 53 contexts contain no thymus, no lymph node and no bone marrow — adult
|
| 414 |
+
thymus involutes and is not routinely sampled, so the normal-tissue safety read was blind to the one
|
| 415 |
+
tissue central tolerance actually happens in. Expression Atlas is no better: of its **123 human
|
| 416 |
+
baseline experiments only three carry any thymus group**, and all three are compromised for this
|
| 417 |
+
purpose — E-MTAB-3871 is fetal tissue from fetuses with congenital defects, E-MTAB-3827 is sorted
|
| 418 |
+
thymocyte subsets rather than tissue, and E-MTAB-9641 is thymic epithelium cultured on a 3T3-J2
|
| 419 |
+
feeder layer.
|
| 420 |
+
|
| 421 |
+
HPA samples thymus directly, so its consensus tissue set is what fills the hole.
|
| 422 |
+
|
| 423 |
+
| | |
|
| 424 |
+
|---|---|
|
| 425 |
+
| rows | **1,025,751** |
|
| 426 |
+
| keys | 20,151 gene symbols (`key_type = gene`) |
|
| 427 |
+
| contexts | **51 tissues, including thymus** |
|
| 428 |
+
| `source` | `hpa_consensus` |
|
| 429 |
+
| provenance | **experimental** — RNA-seq; the HPA consensus normalisation is **computed** |
|
| 430 |
+
|
| 431 |
+
**Origin.** [`rna_tissue_consensus.tsv.zip`](https://www.proteinatlas.org/download/tsv/rna_tissue_consensus.tsv.zip)
|
| 432 |
+
from the Human Protein Atlas.
|
| 433 |
+
|
| 434 |
+
> Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C,
|
| 435 |
+
> Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J,
|
| 436 |
+
> Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J,
|
| 437 |
+
> Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M,
|
| 438 |
+
> von Heijne G, Nielsen J, Pontén F.
|
| 439 |
+
> **Tissue-based map of the human proteome.**
|
| 440 |
+
> *Science* 2015;347(6220):1260419.
|
| 441 |
+
> PMID [25613900](https://pubmed.ncbi.nlm.nih.gov/25613900/) ·
|
| 442 |
+
> doi:[10.1126/science.1260419](https://doi.org/10.1126/science.1260419)
|
| 443 |
+
|
| 444 |
+
**It is added as a third `source`, not merged into GTEx and not replacing it.** The schema was
|
| 445 |
+
designed with a `source` column for exactly this; every existing `gtex` and `tcga` row is untouched.
|
| 446 |
+
|
| 447 |
+
**Checked against known biology.** Of 51 tissues, thymus ranks **first** for `PRSS16` (770 nTPM),
|
| 448 |
+
`PSMB11` (28.5), `DNTT` (306), `CD1A` (296), `RAG1` (368), `CCL25` (718), `CD3E` (596) and `LCK`
|
| 449 |
+
(326). Negative controls behave: `ALB` peaks in liver at 198,524 nTPM against 0.3 in thymus, `INS` in
|
| 450 |
+
pancreas, `MYH7` in skeletal muscle.
|
| 451 |
+
|
| 452 |
+
**Caveats.**
|
| 453 |
+
- **nTPM is not GTEx TPM.** Both are TPM-family units but the normalisation differs, so compare
|
| 454 |
+
*within* a `source`, not across. This is what the `source` column is for.
|
| 455 |
+
- `n` is 1 — HPA consensus publishes one value per gene × tissue, so `q25_tpm` and `q75_tpm` equal
|
| 456 |
+
`median_tpm` and carry no spread. GTEx `n` runs to several hundred donors; the two `n` columns are
|
| 457 |
+
not the same quantity.
|
| 458 |
+
- **`AIRE` is detectable here (1.2 nTPM, rank 2 of 51) but reads 0 in the mouse CAGE atlas.** That is
|
| 459 |
+
a difference in assay depth, not in biology — do not read it as a species difference.
|
expression/context_synonyms.tsv
ADDED
|
@@ -0,0 +1,449 @@
|
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| 1 |
+
alias alias_kind target_kind target provenance
|
| 2 |
+
ACC code gtex_context Adrenal Gland curated:TUMOR_TISSUE
|
| 3 |
+
ACC code tcga_code ACC derived:toil_matrix
|
| 4 |
+
Acute Myeloid Leukemia disease gtex_context Whole Blood curated:TUMOR_TISSUE
|
| 5 |
+
Acute Myeloid Leukemia disease tcga_code LAML derived:xena_detailed_category
|
| 6 |
+
Adipose - Subcutaneous gtex gtex_context Adipose - Subcutaneous derived:toil_matrix
|
| 7 |
+
Adipose - Visceral (Omentum) gtex gtex_context Adipose - Visceral (Omentum) derived:toil_matrix
|
| 8 |
+
Adrenal Gland gtex gtex_context Adrenal Gland derived:toil_matrix
|
| 9 |
+
Adrenal gland site gtex_context Adrenal Gland curated:TUMOR_TISSUE
|
| 10 |
+
Adrenal gland site tcga_code ACC derived:xena_primary_site
|
| 11 |
+
Adrenocortical Cancer disease gtex_context Adrenal Gland curated:TUMOR_TISSUE
|
| 12 |
+
Adrenocortical Cancer disease tcga_code ACC derived:xena_detailed_category
|
| 13 |
+
Artery - Aorta gtex gtex_context Artery - Aorta derived:toil_matrix
|
| 14 |
+
Artery - Coronary gtex gtex_context Artery - Coronary derived:toil_matrix
|
| 15 |
+
Artery - Tibial gtex gtex_context Artery - Tibial derived:toil_matrix
|
| 16 |
+
BLCA code gtex_context Bladder curated:TUMOR_TISSUE
|
| 17 |
+
BLCA code tcga_code BLCA derived:toil_matrix
|
| 18 |
+
BRCA code gtex_context Breast - Mammary Tissue curated:TUMOR_TISSUE
|
| 19 |
+
BRCA code tcga_code BRCA derived:toil_matrix
|
| 20 |
+
Bile duct site gtex_context Liver curated:TUMOR_TISSUE
|
| 21 |
+
Bile duct site tcga_code CHOL derived:xena_primary_site
|
| 22 |
+
Bladder gtex gtex_context Bladder derived:toil_matrix
|
| 23 |
+
Bladder site gtex_context Bladder curated:TUMOR_TISSUE
|
| 24 |
+
Bladder site tcga_code BLCA derived:xena_primary_site
|
| 25 |
+
Bladder Urothelial Carcinoma disease gtex_context Bladder curated:TUMOR_TISSUE
|
| 26 |
+
Bladder Urothelial Carcinoma disease tcga_code BLCA derived:xena_detailed_category
|
| 27 |
+
Brain site gtex_context Brain - Amygdala curated:TUMOR_TISSUE
|
| 28 |
+
Brain site gtex_context Brain - Anterior Cingulate Cortex (Ba24) curated:TUMOR_TISSUE
|
| 29 |
+
Brain site gtex_context Brain - Caudate (Basal Ganglia) curated:TUMOR_TISSUE
|
| 30 |
+
Brain site gtex_context Brain - Cerebellar Hemisphere curated:TUMOR_TISSUE
|
| 31 |
+
Brain site gtex_context Brain - Cerebellum curated:TUMOR_TISSUE
|
| 32 |
+
Brain site gtex_context Brain - Cortex curated:TUMOR_TISSUE
|
| 33 |
+
Brain site gtex_context Brain - Frontal Cortex (Ba9) curated:TUMOR_TISSUE
|
| 34 |
+
Brain site gtex_context Brain - Hippocampus curated:TUMOR_TISSUE
|
| 35 |
+
Brain site gtex_context Brain - Hypothalamus curated:TUMOR_TISSUE
|
| 36 |
+
Brain site gtex_context Brain - Nucleus Accumbens (Basal Ganglia) curated:TUMOR_TISSUE
|
| 37 |
+
Brain site gtex_context Brain - Putamen (Basal Ganglia) curated:TUMOR_TISSUE
|
| 38 |
+
Brain site gtex_context Brain - Spinal Cord (Cervical C-1) curated:TUMOR_TISSUE
|
| 39 |
+
Brain site gtex_context Brain - Substantia Nigra curated:TUMOR_TISSUE
|
| 40 |
+
Brain site tcga_code GBM derived:xena_primary_site
|
| 41 |
+
Brain site tcga_code LGG derived:xena_primary_site
|
| 42 |
+
Brain - Amygdala gtex gtex_context Brain - Amygdala derived:toil_matrix
|
| 43 |
+
Brain - Anterior Cingulate Cortex (Ba24) gtex gtex_context Brain - Anterior Cingulate Cortex (Ba24) derived:toil_matrix
|
| 44 |
+
Brain - Caudate (Basal Ganglia) gtex gtex_context Brain - Caudate (Basal Ganglia) derived:toil_matrix
|
| 45 |
+
Brain - Cerebellar Hemisphere gtex gtex_context Brain - Cerebellar Hemisphere derived:toil_matrix
|
| 46 |
+
Brain - Cerebellum gtex gtex_context Brain - Cerebellum derived:toil_matrix
|
| 47 |
+
Brain - Cortex gtex gtex_context Brain - Cortex derived:toil_matrix
|
| 48 |
+
Brain - Frontal Cortex (Ba9) gtex gtex_context Brain - Frontal Cortex (Ba9) derived:toil_matrix
|
| 49 |
+
Brain - Hippocampus gtex gtex_context Brain - Hippocampus derived:toil_matrix
|
| 50 |
+
Brain - Hypothalamus gtex gtex_context Brain - Hypothalamus derived:toil_matrix
|
| 51 |
+
Brain - Nucleus Accumbens (Basal Ganglia) gtex gtex_context Brain - Nucleus Accumbens (Basal Ganglia) derived:toil_matrix
|
| 52 |
+
Brain - Putamen (Basal Ganglia) gtex gtex_context Brain - Putamen (Basal Ganglia) derived:toil_matrix
|
| 53 |
+
Brain - Spinal Cord (Cervical C-1) gtex gtex_context Brain - Spinal Cord (Cervical C-1) derived:toil_matrix
|
| 54 |
+
Brain - Substantia Nigra gtex gtex_context Brain - Substantia Nigra derived:toil_matrix
|
| 55 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Amygdala curated:TUMOR_TISSUE
|
| 56 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Anterior Cingulate Cortex (Ba24) curated:TUMOR_TISSUE
|
| 57 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Caudate (Basal Ganglia) curated:TUMOR_TISSUE
|
| 58 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Cerebellar Hemisphere curated:TUMOR_TISSUE
|
| 59 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Cerebellum curated:TUMOR_TISSUE
|
| 60 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Cortex curated:TUMOR_TISSUE
|
| 61 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Frontal Cortex (Ba9) curated:TUMOR_TISSUE
|
| 62 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Hippocampus curated:TUMOR_TISSUE
|
| 63 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Hypothalamus curated:TUMOR_TISSUE
|
| 64 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Nucleus Accumbens (Basal Ganglia) curated:TUMOR_TISSUE
|
| 65 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Putamen (Basal Ganglia) curated:TUMOR_TISSUE
|
| 66 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Spinal Cord (Cervical C-1) curated:TUMOR_TISSUE
|
| 67 |
+
Brain Lower Grade Glioma disease gtex_context Brain - Substantia Nigra curated:TUMOR_TISSUE
|
| 68 |
+
Brain Lower Grade Glioma disease tcga_code LGG derived:xena_detailed_category
|
| 69 |
+
Breast site gtex_context Breast - Mammary Tissue curated:TUMOR_TISSUE
|
| 70 |
+
Breast site tcga_code BRCA derived:xena_primary_site
|
| 71 |
+
Breast - Mammary Tissue gtex gtex_context Breast - Mammary Tissue derived:toil_matrix
|
| 72 |
+
Breast Invasive Carcinoma disease gtex_context Breast - Mammary Tissue curated:TUMOR_TISSUE
|
| 73 |
+
Breast Invasive Carcinoma disease tcga_code BRCA derived:xena_detailed_category
|
| 74 |
+
CESC code gtex_context Cervix - Ectocervix curated:TUMOR_TISSUE
|
| 75 |
+
CESC code tcga_code CESC derived:toil_matrix
|
| 76 |
+
CHOL code gtex_context Liver curated:TUMOR_TISSUE
|
| 77 |
+
CHOL code tcga_code CHOL derived:toil_matrix
|
| 78 |
+
COAD code gtex_context Colon - Sigmoid curated:TUMOR_TISSUE
|
| 79 |
+
COAD code gtex_context Colon - Transverse curated:TUMOR_TISSUE
|
| 80 |
+
COAD code tcga_code COAD derived:toil_matrix
|
| 81 |
+
Cells - Ebv-Transformed Lymphocytes gtex gtex_context Cells - Ebv-Transformed Lymphocytes derived:toil_matrix
|
| 82 |
+
Cells - Leukemia Cell Line (Cml) gtex gtex_context Cells - Leukemia Cell Line (Cml) derived:toil_matrix
|
| 83 |
+
Cells - Transformed Fibroblasts gtex gtex_context Cells - Transformed Fibroblasts derived:toil_matrix
|
| 84 |
+
Cervical & Endocervical Cancer disease gtex_context Cervix - Ectocervix curated:TUMOR_TISSUE
|
| 85 |
+
Cervical & Endocervical Cancer disease tcga_code CESC derived:xena_detailed_category
|
| 86 |
+
Cervix site gtex_context Cervix - Ectocervix curated:TUMOR_TISSUE
|
| 87 |
+
Cervix site tcga_code CESC derived:xena_primary_site
|
| 88 |
+
Cervix - Ectocervix gtex gtex_context Cervix - Ectocervix derived:toil_matrix
|
| 89 |
+
Cholangiocarcinoma disease gtex_context Liver curated:TUMOR_TISSUE
|
| 90 |
+
Cholangiocarcinoma disease tcga_code CHOL derived:xena_detailed_category
|
| 91 |
+
Colon site gtex_context Colon - Sigmoid curated:TUMOR_TISSUE
|
| 92 |
+
Colon site gtex_context Colon - Transverse curated:TUMOR_TISSUE
|
| 93 |
+
Colon site tcga_code COAD derived:xena_primary_site
|
| 94 |
+
Colon - Sigmoid gtex gtex_context Colon - Sigmoid derived:toil_matrix
|
| 95 |
+
Colon - Transverse gtex gtex_context Colon - Transverse derived:toil_matrix
|
| 96 |
+
Colon Adenocarcinoma disease gtex_context Colon - Sigmoid curated:TUMOR_TISSUE
|
| 97 |
+
Colon Adenocarcinoma disease gtex_context Colon - Transverse curated:TUMOR_TISSUE
|
| 98 |
+
Colon Adenocarcinoma disease tcga_code COAD derived:xena_detailed_category
|
| 99 |
+
DLBC code gtex_context Spleen curated:TUMOR_TISSUE
|
| 100 |
+
DLBC code gtex_context Whole Blood curated:TUMOR_TISSUE
|
| 101 |
+
DLBC code tcga_code DLBC derived:toil_matrix
|
| 102 |
+
Diffuse Large B-Cell Lymphoma disease gtex_context Spleen curated:TUMOR_TISSUE
|
| 103 |
+
Diffuse Large B-Cell Lymphoma disease gtex_context Whole Blood curated:TUMOR_TISSUE
|
| 104 |
+
Diffuse Large B-Cell Lymphoma disease tcga_code DLBC derived:xena_detailed_category
|
| 105 |
+
ESCA code gtex_context Esophagus - Gastroesophageal Junction curated:TUMOR_TISSUE
|
| 106 |
+
ESCA code gtex_context Esophagus - Mucosa curated:TUMOR_TISSUE
|
| 107 |
+
ESCA code gtex_context Esophagus - Muscularis curated:TUMOR_TISSUE
|
| 108 |
+
ESCA code tcga_code ESCA derived:toil_matrix
|
| 109 |
+
Endometrium site gtex_context Uterus curated:TUMOR_TISSUE
|
| 110 |
+
Endometrium site tcga_code UCEC derived:xena_primary_site
|
| 111 |
+
Esophageal Carcinoma disease gtex_context Esophagus - Gastroesophageal Junction curated:TUMOR_TISSUE
|
| 112 |
+
Esophageal Carcinoma disease gtex_context Esophagus - Mucosa curated:TUMOR_TISSUE
|
| 113 |
+
Esophageal Carcinoma disease gtex_context Esophagus - Muscularis curated:TUMOR_TISSUE
|
| 114 |
+
Esophageal Carcinoma disease tcga_code ESCA derived:xena_detailed_category
|
| 115 |
+
Esophagus site gtex_context Esophagus - Gastroesophageal Junction curated:TUMOR_TISSUE
|
| 116 |
+
Esophagus site gtex_context Esophagus - Mucosa curated:TUMOR_TISSUE
|
| 117 |
+
Esophagus site gtex_context Esophagus - Muscularis curated:TUMOR_TISSUE
|
| 118 |
+
Esophagus site tcga_code ESCA derived:xena_primary_site
|
| 119 |
+
Esophagus - Gastroesophageal Junction gtex gtex_context Esophagus - Gastroesophageal Junction derived:toil_matrix
|
| 120 |
+
Esophagus - Mucosa gtex gtex_context Esophagus - Mucosa derived:toil_matrix
|
| 121 |
+
Esophagus - Muscularis gtex gtex_context Esophagus - Muscularis derived:toil_matrix
|
| 122 |
+
Eye site tcga_code UVM derived:xena_primary_site
|
| 123 |
+
Fallopian Tube gtex gtex_context Fallopian Tube derived:toil_matrix
|
| 124 |
+
GBM code gtex_context Brain - Cortex curated:TUMOR_TISSUE
|
| 125 |
+
GBM code gtex_context Brain - Frontal Cortex (Ba9) curated:TUMOR_TISSUE
|
| 126 |
+
GBM code tcga_code GBM derived:toil_matrix
|
| 127 |
+
Glioblastoma Multiforme disease gtex_context Brain - Cortex curated:TUMOR_TISSUE
|
| 128 |
+
Glioblastoma Multiforme disease gtex_context Brain - Frontal Cortex (Ba9) curated:TUMOR_TISSUE
|
| 129 |
+
Glioblastoma Multiforme disease tcga_code GBM derived:xena_detailed_category
|
| 130 |
+
HNSC code gtex_context Esophagus - Mucosa curated:TUMOR_TISSUE
|
| 131 |
+
HNSC code gtex_context Minor Salivary Gland curated:TUMOR_TISSUE
|
| 132 |
+
HNSC code tcga_code HNSC derived:toil_matrix
|
| 133 |
+
Head & Neck Squamous Cell Carcinoma disease gtex_context Esophagus - Mucosa curated:TUMOR_TISSUE
|
| 134 |
+
Head & Neck Squamous Cell Carcinoma disease gtex_context Minor Salivary Gland curated:TUMOR_TISSUE
|
| 135 |
+
Head & Neck Squamous Cell Carcinoma disease tcga_code HNSC derived:xena_detailed_category
|
| 136 |
+
Head and Neck region site gtex_context Esophagus - Mucosa curated:TUMOR_TISSUE
|
| 137 |
+
Head and Neck region site gtex_context Minor Salivary Gland curated:TUMOR_TISSUE
|
| 138 |
+
Head and Neck region site tcga_code HNSC derived:xena_primary_site
|
| 139 |
+
Heart - Atrial Appendage gtex gtex_context Heart - Atrial Appendage derived:toil_matrix
|
| 140 |
+
Heart - Left Ventricle gtex gtex_context Heart - Left Ventricle derived:toil_matrix
|
| 141 |
+
KICH code gtex_context Kidney - Cortex curated:TUMOR_TISSUE
|
| 142 |
+
KICH code tcga_code KICH derived:toil_matrix
|
| 143 |
+
KIRC code gtex_context Kidney - Cortex curated:TUMOR_TISSUE
|
| 144 |
+
KIRC code tcga_code KIRC derived:toil_matrix
|
| 145 |
+
KIRP code gtex_context Kidney - Cortex curated:TUMOR_TISSUE
|
| 146 |
+
KIRP code tcga_code KIRP derived:toil_matrix
|
| 147 |
+
Kidney site gtex_context Kidney - Cortex curated:TUMOR_TISSUE
|
| 148 |
+
Kidney site tcga_code KICH derived:xena_primary_site
|
| 149 |
+
Kidney site tcga_code KIRC derived:xena_primary_site
|
| 150 |
+
Kidney site tcga_code KIRP derived:xena_primary_site
|
| 151 |
+
Kidney - Cortex gtex gtex_context Kidney - Cortex derived:toil_matrix
|
| 152 |
+
Kidney Chromophobe disease gtex_context Kidney - Cortex curated:TUMOR_TISSUE
|
| 153 |
+
Kidney Chromophobe disease tcga_code KICH derived:xena_detailed_category
|
| 154 |
+
Kidney Clear Cell Carcinoma disease gtex_context Kidney - Cortex curated:TUMOR_TISSUE
|
| 155 |
+
Kidney Clear Cell Carcinoma disease tcga_code KIRC derived:xena_detailed_category
|
| 156 |
+
Kidney Papillary Cell Carcinoma disease gtex_context Kidney - Cortex curated:TUMOR_TISSUE
|
| 157 |
+
Kidney Papillary Cell Carcinoma disease tcga_code KIRP derived:xena_detailed_category
|
| 158 |
+
LAML code gtex_context Whole Blood curated:TUMOR_TISSUE
|
| 159 |
+
LAML code tcga_code LAML derived:toil_matrix
|
| 160 |
+
LGG code gtex_context Brain - Amygdala curated:TUMOR_TISSUE
|
| 161 |
+
LGG code gtex_context Brain - Anterior Cingulate Cortex (Ba24) curated:TUMOR_TISSUE
|
| 162 |
+
LGG code gtex_context Brain - Caudate (Basal Ganglia) curated:TUMOR_TISSUE
|
| 163 |
+
LGG code gtex_context Brain - Cerebellar Hemisphere curated:TUMOR_TISSUE
|
| 164 |
+
LGG code gtex_context Brain - Cerebellum curated:TUMOR_TISSUE
|
| 165 |
+
LGG code gtex_context Brain - Cortex curated:TUMOR_TISSUE
|
| 166 |
+
LGG code gtex_context Brain - Frontal Cortex (Ba9) curated:TUMOR_TISSUE
|
| 167 |
+
LGG code gtex_context Brain - Hippocampus curated:TUMOR_TISSUE
|
| 168 |
+
LGG code gtex_context Brain - Hypothalamus curated:TUMOR_TISSUE
|
| 169 |
+
LGG code gtex_context Brain - Nucleus Accumbens (Basal Ganglia) curated:TUMOR_TISSUE
|
| 170 |
+
LGG code gtex_context Brain - Putamen (Basal Ganglia) curated:TUMOR_TISSUE
|
| 171 |
+
LGG code gtex_context Brain - Spinal Cord (Cervical C-1) curated:TUMOR_TISSUE
|
| 172 |
+
LGG code gtex_context Brain - Substantia Nigra curated:TUMOR_TISSUE
|
| 173 |
+
LGG code tcga_code LGG derived:toil_matrix
|
| 174 |
+
LIHC code gtex_context Liver curated:TUMOR_TISSUE
|
| 175 |
+
LIHC code tcga_code LIHC derived:toil_matrix
|
| 176 |
+
LUAD code gtex_context Lung curated:TUMOR_TISSUE
|
| 177 |
+
LUAD code tcga_code LUAD derived:toil_matrix
|
| 178 |
+
LUSC code gtex_context Lung curated:TUMOR_TISSUE
|
| 179 |
+
LUSC code tcga_code LUSC derived:toil_matrix
|
| 180 |
+
Lining of body cavities site tcga_code MESO derived:xena_primary_site
|
| 181 |
+
Liver gtex gtex_context Liver derived:toil_matrix
|
| 182 |
+
Liver site gtex_context Liver curated:TUMOR_TISSUE
|
| 183 |
+
Liver site tcga_code LIHC derived:xena_primary_site
|
| 184 |
+
Liver Hepatocellular Carcinoma disease gtex_context Liver curated:TUMOR_TISSUE
|
| 185 |
+
Liver Hepatocellular Carcinoma disease tcga_code LIHC derived:xena_detailed_category
|
| 186 |
+
Lung gtex gtex_context Lung derived:toil_matrix
|
| 187 |
+
Lung site gtex_context Lung curated:TUMOR_TISSUE
|
| 188 |
+
Lung site tcga_code LUAD derived:xena_primary_site
|
| 189 |
+
Lung site tcga_code LUSC derived:xena_primary_site
|
| 190 |
+
Lung Adenocarcinoma disease gtex_context Lung curated:TUMOR_TISSUE
|
| 191 |
+
Lung Adenocarcinoma disease tcga_code LUAD derived:xena_detailed_category
|
| 192 |
+
Lung Squamous Cell Carcinoma disease gtex_context Lung curated:TUMOR_TISSUE
|
| 193 |
+
Lung Squamous Cell Carcinoma disease tcga_code LUSC derived:xena_detailed_category
|
| 194 |
+
Lymphatic tissue site gtex_context Spleen curated:TUMOR_TISSUE
|
| 195 |
+
Lymphatic tissue site gtex_context Whole Blood curated:TUMOR_TISSUE
|
| 196 |
+
Lymphatic tissue site tcga_code DLBC derived:xena_primary_site
|
| 197 |
+
MESO code tcga_code MESO derived:toil_matrix
|
| 198 |
+
Mesothelioma disease tcga_code MESO derived:xena_detailed_category
|
| 199 |
+
Minor Salivary Gland gtex gtex_context Minor Salivary Gland derived:toil_matrix
|
| 200 |
+
Muscle - Skeletal gtex gtex_context Muscle - Skeletal derived:toil_matrix
|
| 201 |
+
Nerve - Tibial gtex gtex_context Nerve - Tibial derived:toil_matrix
|
| 202 |
+
OV code gtex_context Fallopian Tube curated:TUMOR_TISSUE
|
| 203 |
+
OV code gtex_context Ovary curated:TUMOR_TISSUE
|
| 204 |
+
OV code tcga_code OV derived:toil_matrix
|
| 205 |
+
Ovarian Serous Cystadenocarcinoma disease gtex_context Fallopian Tube curated:TUMOR_TISSUE
|
| 206 |
+
Ovarian Serous Cystadenocarcinoma disease gtex_context Ovary curated:TUMOR_TISSUE
|
| 207 |
+
Ovarian Serous Cystadenocarcinoma disease tcga_code OV derived:xena_detailed_category
|
| 208 |
+
Ovary gtex gtex_context Ovary derived:toil_matrix
|
| 209 |
+
Ovary site gtex_context Fallopian Tube curated:TUMOR_TISSUE
|
| 210 |
+
Ovary site gtex_context Ovary curated:TUMOR_TISSUE
|
| 211 |
+
Ovary site tcga_code OV derived:xena_primary_site
|
| 212 |
+
PAAD code gtex_context Pancreas curated:TUMOR_TISSUE
|
| 213 |
+
PAAD code tcga_code PAAD derived:toil_matrix
|
| 214 |
+
PCPG code gtex_context Adrenal Gland curated:TUMOR_TISSUE
|
| 215 |
+
PCPG code tcga_code PCPG derived:toil_matrix
|
| 216 |
+
PRAD code gtex_context Prostate curated:TUMOR_TISSUE
|
| 217 |
+
PRAD code tcga_code PRAD derived:toil_matrix
|
| 218 |
+
Pancreas gtex gtex_context Pancreas derived:toil_matrix
|
| 219 |
+
Pancreas site gtex_context Pancreas curated:TUMOR_TISSUE
|
| 220 |
+
Pancreas site tcga_code PAAD derived:xena_primary_site
|
| 221 |
+
Pancreatic Adenocarcinoma disease gtex_context Pancreas curated:TUMOR_TISSUE
|
| 222 |
+
Pancreatic Adenocarcinoma disease tcga_code PAAD derived:xena_detailed_category
|
| 223 |
+
Paraganglia site gtex_context Adrenal Gland curated:TUMOR_TISSUE
|
| 224 |
+
Paraganglia site tcga_code PCPG derived:xena_primary_site
|
| 225 |
+
Pheochromocytoma & Paraganglioma disease gtex_context Adrenal Gland curated:TUMOR_TISSUE
|
| 226 |
+
Pheochromocytoma & Paraganglioma disease tcga_code PCPG derived:xena_detailed_category
|
| 227 |
+
Pituitary gtex gtex_context Pituitary derived:toil_matrix
|
| 228 |
+
Prostate gtex gtex_context Prostate derived:toil_matrix
|
| 229 |
+
Prostate site gtex_context Prostate curated:TUMOR_TISSUE
|
| 230 |
+
Prostate site tcga_code PRAD derived:xena_primary_site
|
| 231 |
+
Prostate Adenocarcinoma disease gtex_context Prostate curated:TUMOR_TISSUE
|
| 232 |
+
Prostate Adenocarcinoma disease tcga_code PRAD derived:xena_detailed_category
|
| 233 |
+
READ code gtex_context Colon - Sigmoid curated:TUMOR_TISSUE
|
| 234 |
+
READ code gtex_context Colon - Transverse curated:TUMOR_TISSUE
|
| 235 |
+
READ code tcga_code READ derived:toil_matrix
|
| 236 |
+
Rectum site gtex_context Colon - Sigmoid curated:TUMOR_TISSUE
|
| 237 |
+
Rectum site gtex_context Colon - Transverse curated:TUMOR_TISSUE
|
| 238 |
+
Rectum site tcga_code READ derived:xena_primary_site
|
| 239 |
+
Rectum Adenocarcinoma disease gtex_context Colon - Sigmoid curated:TUMOR_TISSUE
|
| 240 |
+
Rectum Adenocarcinoma disease gtex_context Colon - Transverse curated:TUMOR_TISSUE
|
| 241 |
+
Rectum Adenocarcinoma disease tcga_code READ derived:xena_detailed_category
|
| 242 |
+
SARC code tcga_code SARC derived:toil_matrix
|
| 243 |
+
SKCM code gtex_context Skin - Not Sun Exposed (Suprapubic) curated:TUMOR_TISSUE
|
| 244 |
+
SKCM code gtex_context Skin - Sun Exposed (Lower Leg) curated:TUMOR_TISSUE
|
| 245 |
+
SKCM code tcga_code SKCM derived:toil_matrix
|
| 246 |
+
STAD code gtex_context Stomach curated:TUMOR_TISSUE
|
| 247 |
+
STAD code tcga_code STAD derived:toil_matrix
|
| 248 |
+
Sarcoma disease tcga_code SARC derived:xena_detailed_category
|
| 249 |
+
Skin site gtex_context Skin - Not Sun Exposed (Suprapubic) curated:TUMOR_TISSUE
|
| 250 |
+
Skin site gtex_context Skin - Sun Exposed (Lower Leg) curated:TUMOR_TISSUE
|
| 251 |
+
Skin site tcga_code SKCM derived:xena_primary_site
|
| 252 |
+
Skin - Not Sun Exposed (Suprapubic) gtex gtex_context Skin - Not Sun Exposed (Suprapubic) derived:toil_matrix
|
| 253 |
+
Skin - Sun Exposed (Lower Leg) gtex gtex_context Skin - Sun Exposed (Lower Leg) derived:toil_matrix
|
| 254 |
+
Skin Cutaneous Melanoma disease gtex_context Skin - Not Sun Exposed (Suprapubic) curated:TUMOR_TISSUE
|
| 255 |
+
Skin Cutaneous Melanoma disease gtex_context Skin - Sun Exposed (Lower Leg) curated:TUMOR_TISSUE
|
| 256 |
+
Skin Cutaneous Melanoma disease tcga_code SKCM derived:xena_detailed_category
|
| 257 |
+
Small Intestine - Terminal Ileum gtex gtex_context Small Intestine - Terminal Ileum derived:toil_matrix
|
| 258 |
+
Soft tissue,Bone site tcga_code SARC derived:xena_primary_site
|
| 259 |
+
Spleen gtex gtex_context Spleen derived:toil_matrix
|
| 260 |
+
Stomach gtex gtex_context Stomach derived:toil_matrix
|
| 261 |
+
Stomach site gtex_context Stomach curated:TUMOR_TISSUE
|
| 262 |
+
Stomach site tcga_code STAD derived:xena_primary_site
|
| 263 |
+
Stomach Adenocarcinoma disease gtex_context Stomach curated:TUMOR_TISSUE
|
| 264 |
+
Stomach Adenocarcinoma disease tcga_code STAD derived:xena_detailed_category
|
| 265 |
+
TGCT code gtex_context Testis curated:TUMOR_TISSUE
|
| 266 |
+
TGCT code tcga_code TGCT derived:toil_matrix
|
| 267 |
+
THCA code gtex_context Thyroid curated:TUMOR_TISSUE
|
| 268 |
+
THCA code tcga_code THCA derived:toil_matrix
|
| 269 |
+
THYM code tcga_code THYM derived:toil_matrix
|
| 270 |
+
Testicular Germ Cell Tumor disease gtex_context Testis curated:TUMOR_TISSUE
|
| 271 |
+
Testicular Germ Cell Tumor disease tcga_code TGCT derived:xena_detailed_category
|
| 272 |
+
Testis gtex gtex_context Testis derived:toil_matrix
|
| 273 |
+
Testis site gtex_context Testis curated:TUMOR_TISSUE
|
| 274 |
+
Testis site tcga_code TGCT derived:xena_primary_site
|
| 275 |
+
Thymoma disease tcga_code THYM derived:xena_detailed_category
|
| 276 |
+
Thymus site tcga_code THYM derived:xena_primary_site
|
| 277 |
+
Thyroid gtex gtex_context Thyroid derived:toil_matrix
|
| 278 |
+
Thyroid Carcinoma disease gtex_context Thyroid curated:TUMOR_TISSUE
|
| 279 |
+
Thyroid Carcinoma disease tcga_code THCA derived:xena_detailed_category
|
| 280 |
+
Thyroid Gland site gtex_context Thyroid curated:TUMOR_TISSUE
|
| 281 |
+
Thyroid Gland site tcga_code THCA derived:xena_primary_site
|
| 282 |
+
UCEC code gtex_context Uterus curated:TUMOR_TISSUE
|
| 283 |
+
UCEC code tcga_code UCEC derived:toil_matrix
|
| 284 |
+
UCS code gtex_context Uterus curated:TUMOR_TISSUE
|
| 285 |
+
UCS code tcga_code UCS derived:toil_matrix
|
| 286 |
+
UVM code tcga_code UVM derived:toil_matrix
|
| 287 |
+
Uterine Carcinosarcoma disease gtex_context Uterus curated:TUMOR_TISSUE
|
| 288 |
+
Uterine Carcinosarcoma disease tcga_code UCS derived:xena_detailed_category
|
| 289 |
+
Uterine Corpus Endometrioid Carcinoma disease gtex_context Uterus curated:TUMOR_TISSUE
|
| 290 |
+
Uterine Corpus Endometrioid Carcinoma disease tcga_code UCEC derived:xena_detailed_category
|
| 291 |
+
Uterus gtex gtex_context Uterus derived:toil_matrix
|
| 292 |
+
Uterus site gtex_context Uterus curated:TUMOR_TISSUE
|
| 293 |
+
Uterus site tcga_code UCS derived:xena_primary_site
|
| 294 |
+
Uveal Melanoma disease tcga_code UVM derived:xena_detailed_category
|
| 295 |
+
Vagina gtex gtex_context Vagina derived:toil_matrix
|
| 296 |
+
White blood cell site gtex_context Whole Blood curated:TUMOR_TISSUE
|
| 297 |
+
White blood cell site tcga_code LAML derived:xena_primary_site
|
| 298 |
+
Whole Blood gtex gtex_context Whole Blood derived:toil_matrix
|
| 299 |
+
adrenal gtex gtex_context Adrenal Gland curated:spelling
|
| 300 |
+
adrenal site gtex_context Adrenal Gland curated:spelling
|
| 301 |
+
adrenal site tcga_code ACC curated:spelling
|
| 302 |
+
biliary site gtex_context Liver curated:spelling
|
| 303 |
+
biliary site tcga_code CHOL curated:spelling
|
| 304 |
+
blood site gtex_context Whole Blood curated:spelling
|
| 305 |
+
blood site tcga_code LAML curated:spelling
|
| 306 |
+
bone site tcga_code SARC curated:spelling
|
| 307 |
+
cerebral site gtex_context Brain - Amygdala curated:spelling
|
| 308 |
+
cerebral site gtex_context Brain - Anterior Cingulate Cortex (Ba24) curated:spelling
|
| 309 |
+
cerebral site gtex_context Brain - Caudate (Basal Ganglia) curated:spelling
|
| 310 |
+
cerebral site gtex_context Brain - Cerebellar Hemisphere curated:spelling
|
| 311 |
+
cerebral site gtex_context Brain - Cerebellum curated:spelling
|
| 312 |
+
cerebral site gtex_context Brain - Cortex curated:spelling
|
| 313 |
+
cerebral site gtex_context Brain - Frontal Cortex (Ba9) curated:spelling
|
| 314 |
+
cerebral site gtex_context Brain - Hippocampus curated:spelling
|
| 315 |
+
cerebral site gtex_context Brain - Hypothalamus curated:spelling
|
| 316 |
+
cerebral site gtex_context Brain - Nucleus Accumbens (Basal Ganglia) curated:spelling
|
| 317 |
+
cerebral site gtex_context Brain - Putamen (Basal Ganglia) curated:spelling
|
| 318 |
+
cerebral site gtex_context Brain - Spinal Cord (Cervical C-1) curated:spelling
|
| 319 |
+
cerebral site gtex_context Brain - Substantia Nigra curated:spelling
|
| 320 |
+
cerebral site tcga_code GBM curated:spelling
|
| 321 |
+
cerebral site tcga_code LGG curated:spelling
|
| 322 |
+
cervical site gtex_context Cervix - Ectocervix curated:spelling
|
| 323 |
+
cervical site tcga_code CESC curated:spelling
|
| 324 |
+
cholangiocarcinoma site gtex_context Liver curated:spelling
|
| 325 |
+
cholangiocarcinoma site tcga_code CHOL curated:spelling
|
| 326 |
+
colonic site gtex_context Colon - Sigmoid curated:spelling
|
| 327 |
+
colonic site gtex_context Colon - Transverse curated:spelling
|
| 328 |
+
colonic site tcga_code COAD curated:spelling
|
| 329 |
+
colorectal site gtex_context Colon - Sigmoid curated:spelling
|
| 330 |
+
colorectal site gtex_context Colon - Transverse curated:spelling
|
| 331 |
+
colorectal site tcga_code COAD curated:spelling
|
| 332 |
+
colorectum site gtex_context Colon - Sigmoid curated:spelling
|
| 333 |
+
colorectum site gtex_context Colon - Transverse curated:spelling
|
| 334 |
+
colorectum site tcga_code COAD curated:spelling
|
| 335 |
+
cutaneous site gtex_context Skin - Not Sun Exposed (Suprapubic) curated:spelling
|
| 336 |
+
cutaneous site gtex_context Skin - Sun Exposed (Lower Leg) curated:spelling
|
| 337 |
+
cutaneous site tcga_code SKCM curated:spelling
|
| 338 |
+
endometrial site gtex_context Uterus curated:spelling
|
| 339 |
+
endometrial site tcga_code UCEC curated:spelling
|
| 340 |
+
esophageal site gtex_context Esophagus - Gastroesophageal Junction curated:spelling
|
| 341 |
+
esophageal site gtex_context Esophagus - Mucosa curated:spelling
|
| 342 |
+
esophageal site gtex_context Esophagus - Muscularis curated:spelling
|
| 343 |
+
esophageal site tcga_code ESCA curated:spelling
|
| 344 |
+
gastric gtex gtex_context Stomach curated:spelling
|
| 345 |
+
gastric site gtex_context Stomach curated:spelling
|
| 346 |
+
gastric site tcga_code STAD curated:spelling
|
| 347 |
+
glioblastoma site gtex_context Brain - Amygdala curated:spelling
|
| 348 |
+
glioblastoma site gtex_context Brain - Anterior Cingulate Cortex (Ba24) curated:spelling
|
| 349 |
+
glioblastoma site gtex_context Brain - Caudate (Basal Ganglia) curated:spelling
|
| 350 |
+
glioblastoma site gtex_context Brain - Cerebellar Hemisphere curated:spelling
|
| 351 |
+
glioblastoma site gtex_context Brain - Cerebellum curated:spelling
|
| 352 |
+
glioblastoma site gtex_context Brain - Cortex curated:spelling
|
| 353 |
+
glioblastoma site gtex_context Brain - Frontal Cortex (Ba9) curated:spelling
|
| 354 |
+
glioblastoma site gtex_context Brain - Hippocampus curated:spelling
|
| 355 |
+
glioblastoma site gtex_context Brain - Hypothalamus curated:spelling
|
| 356 |
+
glioblastoma site gtex_context Brain - Nucleus Accumbens (Basal Ganglia) curated:spelling
|
| 357 |
+
glioblastoma site gtex_context Brain - Putamen (Basal Ganglia) curated:spelling
|
| 358 |
+
glioblastoma site gtex_context Brain - Spinal Cord (Cervical C-1) curated:spelling
|
| 359 |
+
glioblastoma site gtex_context Brain - Substantia Nigra curated:spelling
|
| 360 |
+
glioblastoma site tcga_code GBM curated:spelling
|
| 361 |
+
glioblastoma site tcga_code LGG curated:spelling
|
| 362 |
+
glioma site gtex_context Brain - Amygdala curated:spelling
|
| 363 |
+
glioma site gtex_context Brain - Anterior Cingulate Cortex (Ba24) curated:spelling
|
| 364 |
+
glioma site gtex_context Brain - Caudate (Basal Ganglia) curated:spelling
|
| 365 |
+
glioma site gtex_context Brain - Cerebellar Hemisphere curated:spelling
|
| 366 |
+
glioma site gtex_context Brain - Cerebellum curated:spelling
|
| 367 |
+
glioma site gtex_context Brain - Cortex curated:spelling
|
| 368 |
+
glioma site gtex_context Brain - Frontal Cortex (Ba9) curated:spelling
|
| 369 |
+
glioma site gtex_context Brain - Hippocampus curated:spelling
|
| 370 |
+
glioma site gtex_context Brain - Hypothalamus curated:spelling
|
| 371 |
+
glioma site gtex_context Brain - Nucleus Accumbens (Basal Ganglia) curated:spelling
|
| 372 |
+
glioma site gtex_context Brain - Putamen (Basal Ganglia) curated:spelling
|
| 373 |
+
glioma site gtex_context Brain - Spinal Cord (Cervical C-1) curated:spelling
|
| 374 |
+
glioma site gtex_context Brain - Substantia Nigra curated:spelling
|
| 375 |
+
glioma site tcga_code GBM curated:spelling
|
| 376 |
+
glioma site tcga_code LGG curated:spelling
|
| 377 |
+
head and neck site gtex_context Esophagus - Mucosa curated:spelling
|
| 378 |
+
head and neck site gtex_context Minor Salivary Gland curated:spelling
|
| 379 |
+
head and neck site tcga_code HNSC curated:spelling
|
| 380 |
+
hepatic gtex gtex_context Liver curated:spelling
|
| 381 |
+
hepatic site gtex_context Liver curated:spelling
|
| 382 |
+
hepatic site tcga_code LIHC curated:spelling
|
| 383 |
+
hepatocellular gtex gtex_context Liver curated:spelling
|
| 384 |
+
hepatocellular site gtex_context Liver curated:spelling
|
| 385 |
+
hepatocellular site tcga_code LIHC curated:spelling
|
| 386 |
+
leukaemia site gtex_context Whole Blood curated:spelling
|
| 387 |
+
leukaemia site tcga_code LAML curated:spelling
|
| 388 |
+
leukemia site gtex_context Whole Blood curated:spelling
|
| 389 |
+
leukemia site tcga_code LAML curated:spelling
|
| 390 |
+
lymphoma site gtex_context Spleen curated:spelling
|
| 391 |
+
lymphoma site gtex_context Whole Blood curated:spelling
|
| 392 |
+
lymphoma site tcga_code DLBC curated:spelling
|
| 393 |
+
mammary site gtex_context Breast - Mammary Tissue curated:spelling
|
| 394 |
+
mammary site tcga_code BRCA curated:spelling
|
| 395 |
+
melanoma site gtex_context Skin - Not Sun Exposed (Suprapubic) curated:spelling
|
| 396 |
+
melanoma site gtex_context Skin - Sun Exposed (Lower Leg) curated:spelling
|
| 397 |
+
melanoma site tcga_code SKCM curated:spelling
|
| 398 |
+
mesothelioma site tcga_code MESO curated:spelling
|
| 399 |
+
ocular site tcga_code UVM curated:spelling
|
| 400 |
+
oesophageal site gtex_context Esophagus - Gastroesophageal Junction curated:spelling
|
| 401 |
+
oesophageal site gtex_context Esophagus - Mucosa curated:spelling
|
| 402 |
+
oesophageal site gtex_context Esophagus - Muscularis curated:spelling
|
| 403 |
+
oesophageal site tcga_code ESCA curated:spelling
|
| 404 |
+
oesophagus site gtex_context Esophagus - Gastroesophageal Junction curated:spelling
|
| 405 |
+
oesophagus site gtex_context Esophagus - Mucosa curated:spelling
|
| 406 |
+
oesophagus site gtex_context Esophagus - Muscularis curated:spelling
|
| 407 |
+
oesophagus site tcga_code ESCA curated:spelling
|
| 408 |
+
ovarian gtex gtex_context Ovary curated:spelling
|
| 409 |
+
ovarian site gtex_context Fallopian Tube curated:spelling
|
| 410 |
+
ovarian site gtex_context Ovary curated:spelling
|
| 411 |
+
ovarian site tcga_code OV curated:spelling
|
| 412 |
+
pancreatic gtex gtex_context Pancreas curated:spelling
|
| 413 |
+
pancreatic site gtex_context Pancreas curated:spelling
|
| 414 |
+
pancreatic site tcga_code PAAD curated:spelling
|
| 415 |
+
paraganglioma site gtex_context Adrenal Gland curated:spelling
|
| 416 |
+
paraganglioma site tcga_code PCPG curated:spelling
|
| 417 |
+
pheochromocytoma site gtex_context Adrenal Gland curated:spelling
|
| 418 |
+
pheochromocytoma site tcga_code PCPG curated:spelling
|
| 419 |
+
prostatic gtex gtex_context Prostate curated:spelling
|
| 420 |
+
prostatic site gtex_context Prostate curated:spelling
|
| 421 |
+
prostatic site tcga_code PRAD curated:spelling
|
| 422 |
+
pulmonary gtex gtex_context Lung curated:spelling
|
| 423 |
+
pulmonary site gtex_context Lung curated:spelling
|
| 424 |
+
pulmonary site tcga_code LUAD curated:spelling
|
| 425 |
+
pulmonary site tcga_code LUSC curated:spelling
|
| 426 |
+
rectal site gtex_context Colon - Sigmoid curated:spelling
|
| 427 |
+
rectal site gtex_context Colon - Transverse curated:spelling
|
| 428 |
+
rectal site tcga_code READ curated:spelling
|
| 429 |
+
renal site gtex_context Kidney - Cortex curated:spelling
|
| 430 |
+
renal site tcga_code KICH curated:spelling
|
| 431 |
+
renal site tcga_code KIRC curated:spelling
|
| 432 |
+
renal site tcga_code KIRP curated:spelling
|
| 433 |
+
sarcoma site tcga_code SARC curated:spelling
|
| 434 |
+
soft tissue site tcga_code SARC curated:spelling
|
| 435 |
+
testicular gtex gtex_context Testis curated:spelling
|
| 436 |
+
testicular site gtex_context Testis curated:spelling
|
| 437 |
+
testicular site tcga_code TGCT curated:spelling
|
| 438 |
+
thymic site tcga_code THYM curated:spelling
|
| 439 |
+
thyroid site gtex_context Thyroid curated:spelling
|
| 440 |
+
thyroid site tcga_code THCA curated:spelling
|
| 441 |
+
urothelial gtex gtex_context Bladder curated:spelling
|
| 442 |
+
urothelial site gtex_context Bladder curated:spelling
|
| 443 |
+
urothelial site tcga_code BLCA curated:spelling
|
| 444 |
+
uterine gtex gtex_context Uterus curated:spelling
|
| 445 |
+
uterine site gtex_context Uterus curated:spelling
|
| 446 |
+
uterine site tcga_code UCS curated:spelling
|
| 447 |
+
uterine corpus site gtex_context Uterus curated:spelling
|
| 448 |
+
uterine corpus site tcga_code UCEC curated:spelling
|
| 449 |
+
uveal site tcga_code UVM curated:spelling
|
expression/protein_abundance_mmu.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9e8ca44c2f01f9bab28f0f41ae66bb70a189f9f61d81925a645af3c39c9907cd
|
| 3 |
+
size 1206843
|
expression/reference_expression.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b66301491e4fb0945eebfafa267dc287c8d4e9ffe2e81d0c8f351972f509605e
|
| 3 |
+
size 114886559
|
expression/reference_expression_mmu.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:02cf907bb4cb2bb2565401d72fc230b24c1f7e7b8847da2c714d2054f9c18bdd
|
| 3 |
+
size 3116340
|
expression/reference_expression_toil.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a461178a37bddb67283213fc6c05282eed7638f8278bff91baf94c4d85bb0aa8
|
| 3 |
+
size 38553668
|
expression/toil_floors.tsv
ADDED
|
@@ -0,0 +1,89 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
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|
|
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|
|
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|
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|
|
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|
|
|
|
|
|
|
|
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|
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|
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|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
source context q05 q10 q25 n_genes n_samples
|
| 2 |
+
toil_gtex Adipose - Subcutaneous 0.040000916 0.08000192 0.2900018 26040 318
|
| 3 |
+
toil_gtex Adipose - Visceral (Omentum) 0.040000916 0.070001826 0.2599961 26119 197
|
| 4 |
+
toil_gtex Adrenal Gland 0.02999974 0.06000199 0.23999915 26015 128
|
| 5 |
+
toil_gtex Artery - Aorta 0.040000916 0.08999935 0.31998995 25524 207
|
| 6 |
+
toil_gtex Artery - Coronary 0.040000916 0.08000192 0.31000295 25751 118
|
| 7 |
+
toil_gtex Artery - Tibial 0.040000916 0.08000192 0.3299993 24589 281
|
| 8 |
+
toil_gtex Bladder 0.049998555 0.099998094 0.36000732 26736 9
|
| 9 |
+
toil_gtex Brain - Amygdala 0.02999974 0.06000199 0.23999915 26046 69
|
| 10 |
+
toil_gtex Brain - Anterior Cingulate Cortex (Ba24) 0.02999974 0.06000199 0.2500071 26594 83
|
| 11 |
+
toil_gtex Brain - Caudate (Basal Ganglia) 0.02999974 0.06000199 0.23999915 27075 109
|
| 12 |
+
toil_gtex Brain - Cerebellar Hemisphere 0.049998555 0.08999935 0.34998956 27122 97
|
| 13 |
+
toil_gtex Brain - Cerebellum 0.049998555 0.099998094 0.3700023 27425 119
|
| 14 |
+
toil_gtex Brain - Cortex 0.040000916 0.08000192 0.2799918 27159 105
|
| 15 |
+
toil_gtex Brain - Frontal Cortex (Ba9) 0.040000916 0.070001826 0.2749992 26770 102
|
| 16 |
+
toil_gtex Brain - Hippocampus 0.02999974 0.06000199 0.23999915 26358 84
|
| 17 |
+
toil_gtex Brain - Hypothalamus 0.030000996 0.070001826 0.2599961 27235 82
|
| 18 |
+
toil_gtex Brain - Nucleus Accumbens (Basal Ganglia) 0.02999974 0.06000199 0.24500312 27068 104
|
| 19 |
+
toil_gtex Brain - Putamen (Basal Ganglia) 0.02999974 0.06000199 0.23000562 25961 81
|
| 20 |
+
toil_gtex Brain - Spinal Cord (Cervical C-1) 0.040000916 0.07500187 0.2849968 26134 60
|
| 21 |
+
toil_gtex Brain - Substantia Nigra 0.02999974 0.070001826 0.2599961 26187 57
|
| 22 |
+
toil_gtex Breast - Mammary Tissue 0.040000916 0.08000192 0.29999262 27044 179
|
| 23 |
+
toil_gtex Cells - Ebv-Transformed Lymphocytes 0.019999515 0.040000916 0.15999635 25259 107
|
| 24 |
+
toil_gtex Cells - Leukemia Cell Line (Cml) 0.019999515 0.044999734 0.17000377 24871 70
|
| 25 |
+
toil_gtex Cells - Transformed Fibroblasts 0.02999974 0.06000199 0.23000562 24328 256
|
| 26 |
+
toil_gtex Cervix - Ectocervix 0.030000215 0.06000088 0.23999858 28094 6
|
| 27 |
+
toil_gtex Colon - Sigmoid 0.040000916 0.08000192 0.29999262 25948 141
|
| 28 |
+
toil_gtex Colon - Transverse 0.040000916 0.08000192 0.27000663 26613 167
|
| 29 |
+
toil_gtex Esophagus - Gastroesophageal Junction 0.040000916 0.08000192 0.2900018 25786 137
|
| 30 |
+
toil_gtex Esophagus - Mucosa 0.040000916 0.070001826 0.27000663 25938 273
|
| 31 |
+
toil_gtex Esophagus - Muscularis 0.040000916 0.08000192 0.2900018 25631 245
|
| 32 |
+
toil_gtex Fallopian Tube 0.06000199 0.10999756 0.38000983 28082 5
|
| 33 |
+
toil_gtex Heart - Atrial Appendage 0.02999974 0.06000199 0.20000455 25363 175
|
| 34 |
+
toil_gtex Heart - Left Ventricle 0.019999515 0.040000916 0.15999635 23986 202
|
| 35 |
+
toil_gtex Kidney - Cortex 0.024999628 0.050001454 0.20999782 26516 28
|
| 36 |
+
toil_gtex Liver 0.02999974 0.049998555 0.18000482 24649 110
|
| 37 |
+
toil_gtex Lung 0.049998555 0.08999935 0.34998956 27206 288
|
| 38 |
+
toil_gtex Minor Salivary Gland 0.040000916 0.08000192 0.27000663 27258 55
|
| 39 |
+
toil_gtex Muscle - Skeletal 0.02999974 0.049998555 0.18000482 23401 396
|
| 40 |
+
toil_gtex Nerve - Tibial 0.049998555 0.099998094 0.3700023 27034 278
|
| 41 |
+
toil_gtex Ovary 0.040000916 0.07500187 0.29499722 26838 88
|
| 42 |
+
toil_gtex Pancreas 0.02999974 0.049998555 0.18000482 25047 167
|
| 43 |
+
toil_gtex Pituitary 0.049998555 0.099998094 0.36000732 28477 107
|
| 44 |
+
toil_gtex Prostate 0.044999734 0.08999935 0.3499985 27692 100
|
| 45 |
+
toil_gtex Skin - Not Sun Exposed (Suprapubic) 0.040000916 0.08000192 0.2799918 26707 233
|
| 46 |
+
toil_gtex Skin - Sun Exposed (Lower Leg) 0.040000916 0.08000192 0.3049978 26572 324
|
| 47 |
+
toil_gtex Small Intestine - Terminal Ileum 0.040000916 0.08000192 0.2749992 27637 92
|
| 48 |
+
toil_gtex Spleen 0.040000916 0.08000192 0.29999262 27157 100
|
| 49 |
+
toil_gtex Stomach 0.02999974 0.06000199 0.23000562 25735 175
|
| 50 |
+
toil_gtex Testis 0.070001826 0.12000176 0.4000072 36855 165
|
| 51 |
+
toil_gtex Thyroid 0.049998555 0.099998094 0.3700023 27300 279
|
| 52 |
+
toil_gtex Uterus 0.040000916 0.085000634 0.3299993 26839 78
|
| 53 |
+
toil_gtex Vagina 0.040000916 0.08999935 0.3299993 27096 85
|
| 54 |
+
toil_gtex Whole Blood 0.019999515 0.02999974 0.099998094 21838 337
|
| 55 |
+
toil_gtex __pooled__ 0.040000916 0.070001826 0.27000663 1402705 396
|
| 56 |
+
toil_tcga ACC 0.019999515 0.040000916 0.17000377 24378 76
|
| 57 |
+
toil_tcga BLCA 0.019999515 0.040000916 0.17000377 26115 403
|
| 58 |
+
toil_tcga BRCA 0.019999515 0.049998555 0.1899941 27126 1087
|
| 59 |
+
toil_tcga CESC 0.019999515 0.040000916 0.17000377 26331 301
|
| 60 |
+
toil_tcga CHOL 0.024999628 0.049999047 0.20999782 26374 36
|
| 61 |
+
toil_tcga COAD 0.019999515 0.049998555 0.1750043 26169 282
|
| 62 |
+
toil_tcga DLBC 0.019999515 0.040000916 0.14999785 24919 47
|
| 63 |
+
toil_tcga ESCA 0.019999515 0.040000916 0.14000322 30561 180
|
| 64 |
+
toil_tcga GBM 0.02999974 0.070001826 0.2500071 27300 153
|
| 65 |
+
toil_tcga HNSC 0.019999515 0.040000916 0.15999635 25899 514
|
| 66 |
+
toil_tcga KICH 0.019999515 0.02999974 0.12000176 25726 65
|
| 67 |
+
toil_tcga KIRC 0.019999515 0.040000916 0.15999635 27722 512
|
| 68 |
+
toil_tcga KIRP 0.019999515 0.049998555 0.1899941 26360 283
|
| 69 |
+
toil_tcga LAML 0.049998555 0.099998094 0.3700023 28056 173
|
| 70 |
+
toil_tcga LGG 0.02999974 0.06000199 0.21999718 27931 507
|
| 71 |
+
toil_tcga LIHC 0.019999515 0.040000916 0.14999785 24179 367
|
| 72 |
+
toil_tcga LUAD 0.02999974 0.049998555 0.20000455 27131 508
|
| 73 |
+
toil_tcga LUSC 0.019999515 0.049998555 0.18000482 27614 483
|
| 74 |
+
toil_tcga MESO 0.019999515 0.049998555 0.18000482 26673 87
|
| 75 |
+
toil_tcga OV 0.019999515 0.040000916 0.14000322 28858 418
|
| 76 |
+
toil_tcga PAAD 0.02999974 0.049998555 0.21999718 27483 157
|
| 77 |
+
toil_tcga PCPG 0.024999628 0.049998555 0.20000455 26205 178
|
| 78 |
+
toil_tcga PRAD 0.019999515 0.049998555 0.1899941 27144 492
|
| 79 |
+
toil_tcga READ 0.019999515 0.049998555 0.18000482 26295 89
|
| 80 |
+
toil_tcga SARC 0.02999974 0.049998555 0.1899941 25531 254
|
| 81 |
+
toil_tcga SKCM 0.019999515 0.040000916 0.15999635 25675 468
|
| 82 |
+
toil_tcga STAD 0.019999515 0.040000916 0.14000322 29404 410
|
| 83 |
+
toil_tcga TGCT 0.02999974 0.049998555 0.18000482 29059 147
|
| 84 |
+
toil_tcga THCA 0.019999515 0.040000916 0.1899941 26518 507
|
| 85 |
+
toil_tcga THYM 0.019999515 0.049998555 0.20000455 27330 119
|
| 86 |
+
toil_tcga UCEC 0.019999515 0.040000916 0.18000482 26404 177
|
| 87 |
+
toil_tcga UCS 0.02999974 0.049998555 0.20000455 26981 57
|
| 88 |
+
toil_tcga UVM 0.019999515 0.040000916 0.14999785 23508 79
|
| 89 |
+
toil_tcga __pooled__ 0.019999515 0.049998555 0.18000482 882959 1087
|
expression/toil_floors_mmu.tsv
ADDED
|
@@ -0,0 +1,72 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
source context q05 q10 q25 n_genes n_samples
|
| 2 |
+
fantom5_mouse __pooled__ 0.0990793183 0.166985497 0.717383385 429465 1
|
| 3 |
+
fantom5_mouse accessory axillary lymph node 0.166985497 0.166985497 1.38365448 12020 1
|
| 4 |
+
fantom5_mouse adrenal gland 0.0459894836 0.0459894836 0.405304521 13385 1
|
| 5 |
+
fantom5_mouse aorta 0.0459894836 0.0459894836 0.405304521 13162 1
|
| 6 |
+
fantom5_mouse bone (os femoris) 0.717383385 0.717383385 3.26015902 10340 1
|
| 7 |
+
fantom5_mouse cecum 0.717383385 3.26015902 3.26015902 9173 1
|
| 8 |
+
fantom5_mouse cerebellum 0.166985497 0.166985497 1.38365448 11958 1
|
| 9 |
+
fantom5_mouse colon 0.166985497 0.166985497 0.577963889 12659 1
|
| 10 |
+
fantom5_mouse corpora quadrigemina 0.0459894836 0.166985497 0.717383385 13084 1
|
| 11 |
+
fantom5_mouse corpus striatum 0.309359103 0.309359103 1.38365448 11807 1
|
| 12 |
+
fantom5_mouse cortex 0.0990793183 0.166985497 0.717383385 12867 1
|
| 13 |
+
fantom5_mouse diencephalon 0.0990793183 0.0990793183 1.38365448 12892 1
|
| 14 |
+
fantom5_mouse epididymis 0.166985497 0.166985497 0.577963889 13016 1
|
| 15 |
+
fantom5_mouse eyeball 0.0990793183 0.0990793183 0.405304521 13595 1
|
| 16 |
+
fantom5_mouse hippocampus 0.0990793183 0.0990793183 0.717383385 12710 1
|
| 17 |
+
fantom5_mouse intestinal mucosa 0.166985497 0.166985497 0.577963889 11107 1
|
| 18 |
+
fantom5_mouse intestine 0.166985497 0.166985497 0.577963889 11967 1
|
| 19 |
+
fantom5_mouse lung 0.0990793183 0.166985497 0.717383385 13370 1
|
| 20 |
+
fantom5_mouse medulla oblongata 0.0990793183 0.0990793183 0.577963889 13242 1
|
| 21 |
+
fantom5_mouse olfactory brain 0.166985497 0.166985497 1.38365448 12296 1
|
| 22 |
+
fantom5_mouse ovary 0.0459894836 0.0459894836 0.405304521 14064 1
|
| 23 |
+
fantom5_mouse pancreas 0.493148953 1.38365448 3.26015902 9097 1
|
| 24 |
+
fantom5_mouse pituitary gland 0.166985497 0.166985497 1.38365448 12222 1
|
| 25 |
+
fantom5_mouse prostate 0.0990793183 0.0990793183 0.493148953 12308 1
|
| 26 |
+
fantom5_mouse skin 0.0990793183 0.0990793183 0.577963889 13526 1
|
| 27 |
+
fantom5_mouse spinal cord 0.0990793183 0.0990793183 0.717383385 13303 1
|
| 28 |
+
fantom5_mouse spleen 0.0990793183 0.0990793183 0.493148953 12228 1
|
| 29 |
+
fantom5_mouse stomach 0.309359103 1.38365448 3.26015902 10780 1
|
| 30 |
+
fantom5_mouse submandibular gland 0.405304521 0.405304521 1.38365448 10990 1
|
| 31 |
+
fantom5_mouse testis 0.405304521 0.405304521 3.26015902 11113 1
|
| 32 |
+
fantom5_mouse thymus 0.166985497 0.166985497 0.717383385 11856 1
|
| 33 |
+
fantom5_mouse tongue 0.0990793183 0.0990793183 0.717383385 12850 1
|
| 34 |
+
fantom5_mouse urinary bladder 0.0990793183 0.0990793183 0.577963889 13257 1
|
| 35 |
+
fantom5_mouse uterus 0.0990793183 0.0990793183 0.493148953 13252 1
|
| 36 |
+
fantom5_mouse vagina 0.0990793183 0.0990793183 0.577963889 12979 1
|
| 37 |
+
fantom5_mouse vesicular gland 0.309359103 0.309359103 1.38365448 10990 1
|
| 38 |
+
gse245293 B16F10 0.0863542557 0.147608891 0.709316909 19074 3
|
| 39 |
+
gse245293 CT26 0.0808949843 0.133418322 0.602678776 19182 3
|
| 40 |
+
gse245293 E0771 0.0942154527 0.164973333 0.778372049 19101 3
|
| 41 |
+
gse245293 LLC 0.0898912326 0.147942439 0.675284326 19700 3
|
| 42 |
+
gse245293 MC38 0.0903859511 0.157022417 0.767261088 19890 3
|
| 43 |
+
gse245293 Panc02 0.094235152 0.165870145 0.870251417 19764 3
|
| 44 |
+
gse245293 __pooled__ 0.0882425308 0.152038917 0.714352965 116711 3
|
| 45 |
+
prjna1271699+ 4T1 0.0907283276 0.201277331 0.991839886 16827 0
|
| 46 |
+
prjna1271699+ 4T1orth 0.129452452 0.210650012 0.996328354 16699 0
|
| 47 |
+
prjna1271699+ A20 0.0949191302 0.157220826 0.662428319 16502 0
|
| 48 |
+
prjna1271699+ B16BL6 0.129452452 0.205734506 0.889154255 15882 0
|
| 49 |
+
prjna1271699+ B16F0 0.076880008 0.129452452 0.528137326 17105 0
|
| 50 |
+
prjna1271699+ B16F1 0.076880008 0.129452452 0.539604843 16719 0
|
| 51 |
+
prjna1271699+ C1498 0.0816102177 0.138391808 0.556235015 15929 0
|
| 52 |
+
prjna1271699+ CloneM3 0.129452452 0.205734506 0.910723627 15760 0
|
| 53 |
+
prjna1271699+ Colon26 0.0862378627 0.157220826 0.844071329 16671 0
|
| 54 |
+
prjna1271699+ EL4 0.0949191302 0.166985497 0.701014161 17045 0
|
| 55 |
+
prjna1271699+ EMT6 0.0949191302 0.166985497 0.894779742 16177 0
|
| 56 |
+
prjna1271699+ GL261 0.129452452 0.205734506 0.98558861 17720 0
|
| 57 |
+
prjna1271699+ H22 0.0907283276 0.157220826 0.695897818 16640 0
|
| 58 |
+
prjna1271699+ Hepa1-6 0.0907283276 0.157220826 0.814445496 17151 0
|
| 59 |
+
prjna1271699+ J558 0.0862378627 0.147964045 0.652128041 16481 0
|
| 60 |
+
prjna1271699+ JC 0.0816102177 0.143396497 0.711802483 17128 0
|
| 61 |
+
prjna1271699+ KLN205 0.0862378627 0.152635559 0.844071329 18160 0
|
| 62 |
+
prjna1271699+ L1210 0.0862378627 0.138391808 0.528137326 16918 0
|
| 63 |
+
prjna1271699+ L5178R 0.0816102177 0.138391808 0.556235015 16175 0
|
| 64 |
+
prjna1271699+ MB49 0.0816102177 0.129452452 0.539604843 16623 0
|
| 65 |
+
prjna1271699+ MBT2 0.0862378627 0.143396497 0.657023311 17120 0
|
| 66 |
+
prjna1271699+ MPC11 0.201277331 0.269641459 0.867251277 15036 0
|
| 67 |
+
prjna1271699+ Neuro2a 0.0816102177 0.133858606 0.647043347 17522 0
|
| 68 |
+
prjna1271699+ P815 0.0907283276 0.152635559 0.766629577 15932 0
|
| 69 |
+
prjna1271699+ RM1 0.0862378627 0.143396497 0.68285197 16868 0
|
| 70 |
+
prjna1271699+ Renca 0.0907283276 0.157220826 0.820747018 16407 0
|
| 71 |
+
prjna1271699+ WEHI164 0.0862378627 0.143396497 0.67239815 17406 0
|
| 72 |
+
prjna1271699+ __pooled__ 0.0898167714 0.152635559 0.717383385 450603 0
|
expression/toil_matrix.npz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:005df372d40ce4ea1f8d56e1f8e842550d4edebcc81a67ad26a7c20e1612a875
|
| 3 |
+
size 6632665
|
expression/toil_matrix_mmu.npz
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8ad1e180053facb0e823e83865a60cd77acb5cf3cdaf30ad4887dee2d6c5100f
|
| 3 |
+
size 2664374
|
expression/tumor_expression_mmu.tsv.gz
ADDED
|
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| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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oid sha256:6c2abec625d3e440878e722fe561266cbd0ae5914640d5f5ecfe37d357f343b9
|
| 3 |
+
size 1999293
|
hla_loh/SOURCES.md
ADDED
|
@@ -0,0 +1,134 @@
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|
|
| 1 |
+
# `hla_loh/` — sources
|
| 2 |
+
|
| 3 |
+
Per-allele HLA-I **loss of heterozygosity** and genetic immune escape, over 6,319 tumours
|
| 4 |
+
(4,439 Hartwig metastatic + 1,880 PCAWG primary), plus the join that carries the PCAWG arm onto
|
| 5 |
+
TCGA barcodes so it meets `tcga/tcga_neoantigens.tsv.gz`.
|
| 6 |
+
|
| 7 |
+
The question this directory exists to answer: **when a tumour loses one HLA allele, is the lost one
|
| 8 |
+
the allele that better presented its neoantigens?** That is the escape mechanism a polyepitope
|
| 9 |
+
cassette has to be designed against, and `bench/cassette/tcga_loh_perallele.py` in the benchmark
|
| 10 |
+
repo is the consumer.
|
| 11 |
+
|
| 12 |
+
## Provenance in one line
|
| 13 |
+
|
| 14 |
+
Everything here is **derived** — LILAC allele-specific copy-number calls on whole-genome sequencing,
|
| 15 |
+
published as supplementary data — except `do_to_tcga_barcode.tsv`, which is a lookup we performed
|
| 16 |
+
against the GDC API. Nothing in this directory is a measurement we made.
|
| 17 |
+
|
| 18 |
+
**Primary source.** Martínez-Jiménez F, Priestley P, Shale C, Baber J, Rozemuller E, Cuppen E.
|
| 19 |
+
*Genetic immune escape landscape in primary and metastatic cancer.* **Nature Genetics 55(5):820–831
|
| 20 |
+
(2023)**, [doi:10.1038/s41588-023-01367-1](https://doi.org/10.1038/s41588-023-01367-1).
|
| 21 |
+
|
| 22 |
+
## What ships, and what deliberately does not
|
| 23 |
+
|
| 24 |
+
**The publisher's workbooks are not redistributable; the facts in them are.** The three
|
| 25 |
+
`41588_2023_1367_MOESM*.xlsx` are copyrighted Springer Nature supplementary material. They are kept
|
| 26 |
+
in `raw/hla_loh/` (gitignored) and `harmonize.py` rebuilds neutral long-format parquet from them.
|
| 27 |
+
This is the same call already taken for `icb_cohorts/`: harmonised output published, publisher
|
| 28 |
+
workbook held back. An `.xlsx` is a layout and a copyrightable compilation; a table of allele copy
|
| 29 |
+
numbers is neither.
|
| 30 |
+
|
| 31 |
+
### Published (this directory)
|
| 32 |
+
|
| 33 |
+
| file | rows × cols | what it is | provenance |
|
| 34 |
+
|---|---|---|---|
|
| 35 |
+
| `gie_allele.parquet` | 37,914 × 9 | one row per **(sample, allele slot)** — 6,319 samples × the six slots `A1 A2 B1 B2 C1 C2`. Carries `allele`, `copy_number`, `n_mutations`, plus `cohort`/`cancer_type`. This is Supplementary Data 2 unpivoted | derived (LILAC on WGS) |
|
| 36 |
+
| `gie_sample.parquet` | 6,319 × 32 | one row per **sample**: the escape read-outs — `loh_lilac`, `copy_number_neutral_loh_hla`, `loh_focal`/`loh_nonfocal`/`loh_hfocal`, `mut_hla_lilac`, the five pathway flags with their detail strings, `genetic_immune_escape`, per-locus `ploidy_minor`/`ploidy_major`/`del_HLA-*` | derived |
|
| 37 |
+
| `hla_typing_callers.parquet` | 226,008 × 8 | one row per **(sample, caller, tissue, slot)** — 6,278 samples × 3 callers (`LILAC`, `xHLA`, `polysolver`) × normal/tumour × 6 slots. Supplementary Data 1, unpivoted. The caller-concordance reference; **not** used in the join | derived |
|
| 38 |
+
| `sample_metadata.parquet` | 7,619 × 40 | per-sample cohort, cancer type, biopsy site, treatment history, MSI/HR status and mutation load (`sbs`/`dbs`/`indel`/`sv`, clonal and subclonal). Supplementary Table 2 | derived |
|
| 39 |
+
| `escape_genes.parquet` | 21 × 6 | the immune-escape gene panel the paper's calls are made over, with pathway assignment and admissible mutation types. Supplementary Table 1 | curated gene list |
|
| 40 |
+
| `perallele_tcga_loh.parquet` | 2,747 × 3 | **the deliverable the benchmark consumes.** `donor` (TCGA barcode), `hla_allele`, `allele_lost`. 481 TCGA donors overlapping `bench/cassette/scored.parquet`; 238 loss calls | derived (join, 2026-09-11) |
|
| 41 |
+
| `do_to_tcga_barcode.tsv` | 711 × 4 | `icgc_donor_id` → `case_uuid` → `tcga_barcode` → `gdc_project`, for the PCAWG donors that are TCGA cases | derived (GDC `/cases` lookup) |
|
| 42 |
+
| `harmonize.py` | — | rebuilds the five parquets above from `raw/hla_loh/` | — |
|
| 43 |
+
|
| 44 |
+
### Held back in `raw/hla_loh/` (gitignored)
|
| 45 |
+
|
| 46 |
+
| file | why it is not published |
|
| 47 |
+
|---|---|
|
| 48 |
+
| `41588_2023_1367_MOESM4_ESM.xlsx` | copyrighted Springer Nature supplement (Supplementary Tables 1–2) |
|
| 49 |
+
| `41588_2023_1367_MOESM5_ESM.xlsx` | copyrighted (Supplementary Data 1) |
|
| 50 |
+
| `41588_2023_1367_MOESM6_ESM.xlsx` | copyrighted (Supplementary Data 2) |
|
| 51 |
+
| `pcawg_sample_sheet.2016-10-18.tsv` | third-party PCAWG release file, re-fetchable (command below); an input, not our output |
|
| 52 |
+
|
| 53 |
+
**The Hartwig arm of the source study needs its own access application** for the underlying
|
| 54 |
+
sequencing. Nothing here is that sequencing — these are the published per-sample call tables.
|
| 55 |
+
|
| 56 |
+
## Identifiers, and why they are public
|
| 57 |
+
|
| 58 |
+
- **PCAWG samples are keyed `DO#####`** (`icgc_donor_id`), Hartwig samples `HMF#####A`. Both are
|
| 59 |
+
pseudonymous research identifiers assigned by the consortium, as published.
|
| 60 |
+
- **TCGA barcodes** (`TCGA-XX-XXXX`) are likewise public — `tcga/` in this compendium already ships
|
| 61 |
+
per-barcode HLA and neoantigen calls on the same basis (open-access somatic + expression, as in
|
| 62 |
+
TCIA / Thorsson 2018).
|
| 63 |
+
- PCAWG's `donor_wgs_exclusion_white_gray` is the consortium's **published QC whitelist/greylist**
|
| 64 |
+
flag, not a consent tier. An earlier `.gitignore` rule in this repo described this directory as a
|
| 65 |
+
re-identification risk; that reading was wrong and has been withdrawn.
|
| 66 |
+
|
| 67 |
+
## Three things a consumer has to know
|
| 68 |
+
|
| 69 |
+
**1. `n_mutations` is a count, not a flag.** `*_MUT` takes `0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0`
|
| 70 |
+
across the six slots (37,707 zero, 207 non-zero, 42 of them non-integral). Reading it as a boolean
|
| 71 |
+
loses every fractional and multi-hit call.
|
| 72 |
+
|
| 73 |
+
**2. Four columns are comma-separated locus lists, not booleans.**
|
| 74 |
+
`copy_number_neutral_loh_hla`, `loh_focal`, `loh_nonfocal`, `loh_hfocal` carry values like
|
| 75 |
+
`HLA-A,HLA-C,HLA-B`. They sit beside ten genuine booleans in the same sheet and look identical at a
|
| 76 |
+
glance; `copy_number_neutral_loh_hla` has 784 non-null values that a bool cast would have silently
|
| 77 |
+
nulled. They are kept verbatim as the source wrote them.
|
| 78 |
+
|
| 79 |
+
**3. The allele vocabulary is this repo's, not the workbook's.** The workbooks write `A*02:01`;
|
| 80 |
+
`gie_allele.parquet` and `hla_typing_callers.parquet` write **`HLA-A*02:01`**, matching
|
| 81 |
+
`perallele_tcga_loh.parquet`, `tcga/`, and `mhcmatch`. Two spellings for one molecule is the
|
| 82 |
+
recurring failure mode in this compendium, and a lookup that silently matches neither returns the
|
| 83 |
+
same shape as "nothing was presented".
|
| 84 |
+
|
| 85 |
+
## The join
|
| 86 |
+
|
| 87 |
+
```
|
| 88 |
+
gie_sample / gie_allele (cohort = PCAWG, sample_id = DO#####)
|
| 89 |
+
│ icgc_donor_id
|
| 90 |
+
▼
|
| 91 |
+
do_to_tcga_barcode.tsv 711 PCAWG donors that are TCGA cases
|
| 92 |
+
│ tcga_barcode
|
| 93 |
+
▼
|
| 94 |
+
perallele_tcga_loh.parquet ──► bench/cassette/scored.parquet (481 donors overlap)
|
| 95 |
+
```
|
| 96 |
+
|
| 97 |
+
All 711 bridge rows join to a PCAWG sample in `gie_sample.parquet`, and all 481 donors in
|
| 98 |
+
`perallele_tcga_loh.parquet` are present in the bridge. The Hartwig arm has no TCGA counterpart and
|
| 99 |
+
does not enter the join.
|
| 100 |
+
|
| 101 |
+
## Regenerate
|
| 102 |
+
|
| 103 |
+
```zsh
|
| 104 |
+
# the five harmonised parquets, from the workbooks in raw/hla_loh/
|
| 105 |
+
python hla_loh/harmonize.py
|
| 106 |
+
|
| 107 |
+
# the PCAWG sample sheet (the ICGC DCC portal is retired; this is the operations repo mirror)
|
| 108 |
+
gh api repos/ICGC-TCGA-PanCancer/pcawg-operations/contents/lists/sample_sheet/pcawg_sample_sheet.2016-10-18.tsv \
|
| 109 |
+
-H "Accept: application/vnd.github.raw+json" > raw/hla_loh/pcawg_sample_sheet.2016-10-18.tsv
|
| 110 |
+
|
| 111 |
+
# the workbooks
|
| 112 |
+
curl -O https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-023-01367-1/MediaObjects/41588_2023_1367_MOESM4_ESM.xlsx
|
| 113 |
+
curl -O https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-023-01367-1/MediaObjects/41588_2023_1367_MOESM5_ESM.xlsx
|
| 114 |
+
curl -O https://static-content.springer.com/esm/art%3A10.1038%2Fs41588-023-01367-1/MediaObjects/41588_2023_1367_MOESM6_ESM.xlsx
|
| 115 |
+
```
|
| 116 |
+
|
| 117 |
+
`do_to_tcga_barcode.tsv` and `perallele_tcga_loh.parquet` were built on 2026-09-11 and are **not
|
| 118 |
+
scripted**. Per-allele lost = `loh_lilac` true at sample level **and**, at each heterozygous locus,
|
| 119 |
+
the allele whose tumour copy number is the minor one (`min(CN) < 0.5 <= max(CN)`); a homozygous
|
| 120 |
+
locus is never called lost, because the allele name is retained. The GDC step POSTs
|
| 121 |
+
`{"filters":{"op":"in","content":{"field":"case_id","value":[...]}},"fields":"case_id,submitter_id,project.project_id"}`
|
| 122 |
+
to `https://api.gdc.cancer.gov/cases` in batches, resolving the sample sheet's `submitter_donor_id`
|
| 123 |
+
(a TCGA **case UUID** for `*-US` projects, not a barcode) to the barcode.
|
| 124 |
+
|
| 125 |
+
## Not harmonised, and why
|
| 126 |
+
|
| 127 |
+
Four sheets of Supplementary Data 1 (`Platinum-Yoruban trio`, `TRACERx hla-I typing`,
|
| 128 |
+
`TRACERx LOH of HLA`, and the three `GenDX validation` sheets) are the paper's **caller-validation**
|
| 129 |
+
tables — 54 to 600 rows each, against orthogonal typing. Nine sheets of Supplementary Data 2
|
| 130 |
+
(`metastatic GIE`, `primary GIE`, the per-gene and mutual-exclusivity breakdowns, `GIE primary vs
|
| 131 |
+
metastatic`) are the paper's **aggregate result** tables, every one of them a group-by over
|
| 132 |
+
`gie_sample.parquet`. Neither set is an input to anything here, and both are re-derivable or
|
| 133 |
+
re-fetchable from the source. `Supp. Table 2 - cancer type data` (58 rows) is a value-count over
|
| 134 |
+
`sample_metadata.parquet` and is dropped for the same reason.
|
hla_loh/do_to_tcga_barcode.tsv
ADDED
|
@@ -0,0 +1,712 @@
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| 1 |
+
icgc_donor_id case_uuid tcga_barcode gdc_project
|
| 2 |
+
DO10172 58facedb-fcb8-4ecf-8338-2bfa4947acef TCGA-A6-6141 TCGA-COAD
|
| 3 |
+
DO10486 ddf69ed9-4c53-48b8-9f01-495bd60c3425 TCGA-AA-3514 TCGA-COAD
|
| 4 |
+
DO10631 f87c6dcc-1a9e-4df5-962c-1f946e2611f8 TCGA-AA-A01T TCGA-COAD
|
| 5 |
+
DO10908 737b35e1-d668-4fce-9b6e-76946c7952b6 TCGA-27-2523 TCGA-GBM
|
| 6 |
+
DO10960 9853c6bb-a42c-4698-abe5-f3c897ebc8f6 TCGA-06-0214 TCGA-GBM
|
| 7 |
+
DO10975 74139255-a635-4c87-814d-3dd04ed630a8 TCGA-06-0190 TCGA-GBM
|
| 8 |
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DO11046 e17a6048-7a72-42c0-ad3f-97cbff02bc9f TCGA-19-2629 TCGA-GBM
|
| 9 |
+
DO11148 0078b0c4-68a9-483b-9aab-61156d263213 TCGA-14-1034 TCGA-GBM
|
| 10 |
+
DO11202 b1c4579c-72fa-4974-830c-b6a81ace45f4 TCGA-14-2554 TCGA-GBM
|
| 11 |
+
DO11382 01a92062-967a-4900-8dc7-a5ecd3b3f8e2 TCGA-06-0171 TCGA-GBM
|
| 12 |
+
DO11501 c184c3ca-7ad3-4202-b108-cb9fd5f5d947 TCGA-41-5651 TCGA-GBM
|
| 13 |
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DO11782 be3a7ef3-34ed-40e1-9d9c-187940596b26 TCGA-27-2528 TCGA-GBM
|
| 14 |
+
DO11794 5c984433-33cf-42fc-b3ba-511efcdcab19 TCGA-06-0211 TCGA-GBM
|
| 15 |
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DO11854 bf339349-062f-4ea9-a0b2-d87d3a21099e TCGA-14-1402 TCGA-GBM
|
| 16 |
+
DO11860 5613f5f6-086d-470b-8f77-6dcb7f8625b7 TCGA-06-0744 TCGA-GBM
|
| 17 |
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|
| 18 |
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|
| 19 |
+
DO12352 a2ac9937-f351-4d78-9261-264bf6c21e0c TCGA-02-2483 TCGA-GBM
|
| 20 |
+
DO12370 93ed7a2b-b0cb-4a84-871f-5c34a0b6a640 TCGA-19-5960 TCGA-GBM
|
| 21 |
+
DO12382 309005a2-93a8-4566-b8d3-6b9310144266 TCGA-19-1389 TCGA-GBM
|
| 22 |
+
DO12454 713190ed-c6c1-4695-814b-85ca9b95e6a0 TCGA-06-5415 TCGA-GBM
|
| 23 |
+
DO12484 3f960d3b-a58c-43d0-a8a4-f3555b399c9d TCGA-06-5411 TCGA-GBM
|
| 24 |
+
DO1249 0dca98b0-f43e-45b6-9a02-00092c78678c TCGA-D8-A27H TCGA-BRCA
|
| 25 |
+
DO1257 0bc5744c-5fa3-45bb-87d0-70a02068b392 TCGA-BH-A0DG TCGA-BRCA
|
| 26 |
+
DO1274 1549dc64-3dab-43fc-96e9-b07d520957e1 TCGA-D8-A27F TCGA-BRCA
|
| 27 |
+
DO12742 521ea765-1bd1-423d-a75d-091243df37a9 TCGA-02-2485 TCGA-GBM
|
| 28 |
+
DO1281 1b703058-e596-45bc-80fe-8b98d545c2e2 TCGA-AR-A2LK TCGA-BRCA
|
| 29 |
+
DO12814 ea3b5da2-6a12-400c-bf0f-e442f5ec1132 TCGA-06-0157 TCGA-GBM
|
| 30 |
+
DO1287 b094e8b2-8ece-4b36-8025-18073a8b873c TCGA-AO-A124 TCGA-BRCA
|
| 31 |
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DO1290 b2aac45b-2073-4c7a-adb9-769a4fdcc111 TCGA-E9-A1NH TCGA-BRCA
|
| 32 |
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DO1291 9fefbe7c-f66a-4940-843e-285cb7b392c1 TCGA-AR-A24Z TCGA-BRCA
|
| 33 |
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|
| 34 |
+
DO1301 18eb4dfc-556f-4bf3-a411-4780209ed1e0 TCGA-BH-A0B3 TCGA-BRCA
|
| 35 |
+
DO13132 f45f4a30-6b4c-4f15-9140-959d6a25a45f TCGA-27-1831 TCGA-GBM
|
| 36 |
+
DO13144 6c5154d2-af36-492f-b520-d925528824e4 TCGA-32-1970 TCGA-GBM
|
| 37 |
+
DO13192 3258cb3b-f63b-463c-b2e4-d638149157c5 TCGA-06-0145 TCGA-GBM
|
| 38 |
+
DO1328 6fa2a667-9c36-4526-8a58-1975e863a806 TCGA-AC-A2BK TCGA-BRCA
|
| 39 |
+
DO13318 6c28f086-6a25-40b6-93eb-bba0014acda6 TCGA-06-2557 TCGA-GBM
|
| 40 |
+
DO13348 c931f3bd-74c5-4ebd-bc0f-c7c6becd25ab TCGA-06-0686 TCGA-GBM
|
| 41 |
+
DO13474 2c5fa2d4-8e35-42e4-8bca-9fb3371a19c8 TCGA-26-5132 TCGA-GBM
|
| 42 |
+
DO13510 8da3103e-3e6c-4176-a583-d5fe5e60601e TCGA-06-0125 TCGA-GBM
|
| 43 |
+
DO13575 b37d944c-c65c-46d6-bb8b-3eb37cb85b68 TCGA-06-2570 TCGA-GBM
|
| 44 |
+
DO1384 20e8106b-1290-4735-abe4-7621e08e3dc8 TCGA-BH-A0E0 TCGA-BRCA
|
| 45 |
+
DO13869 de7b7cac-f094-4d59-8651-e991e34ea093 TCGA-06-0210 TCGA-GBM
|
| 46 |
+
DO1392 8785012f-f73e-4d68-87cf-1d804af32782 TCGA-A7-A13D TCGA-BRCA
|
| 47 |
+
DO13971 054f472f-98cb-4559-b2e2-b5f800fc8eef TCGA-06-0221 TCGA-GBM
|
| 48 |
+
DO14049 2f4a127f-101a-4192-b3e8-f9be2c8648bc TCGA-19-2624 TCGA-GBM
|
| 49 |
+
DO14171 f030e410-2585-461e-ad23-9fdf026ac06c TCGA-CR-7382 TCGA-HNSC
|
| 50 |
+
DO14192 5fce954c-11b9-41f4-9471-4b5bdc0efa7f TCGA-CR-6491 TCGA-HNSC
|
| 51 |
+
DO14204 d5eedfb5-4fa3-4a1e-ad0d-ea550b22a5ba TCGA-CV-6433 TCGA-HNSC
|
| 52 |
+
DO14232 70741b73-9683-42bd-87fc-6a021a70103b TCGA-BA-5556 TCGA-HNSC
|
| 53 |
+
DO14236 3e5f451a-5882-4914-ae1a-95c898c2bcca TCGA-BA-6872 TCGA-HNSC
|
| 54 |
+
DO14238 05fbd98c-28bd-41bc-ae60-16ac0c16c723 TCGA-CV-7090 TCGA-HNSC
|
| 55 |
+
DO14242 bf6b931e-cb2e-4cc0-b603-e9fc71fdc509 TCGA-BA-6873 TCGA-HNSC
|
| 56 |
+
DO14250 fafd6f5b-1d76-4537-bd1c-e0bd7b4e2166 TCGA-BA-6869 TCGA-HNSC
|
| 57 |
+
DO14264 c1f50a22-38df-41cc-a1f4-f7985504a7ac TCGA-BB-4225 TCGA-HNSC
|
| 58 |
+
DO14282 9f89510c-ed07-471f-b35e-7c87c237b9fe TCGA-CR-7385 TCGA-HNSC
|
| 59 |
+
DO14296 ba9d4568-71a8-41fc-a2be-0bf784e36d6a TCGA-CR-6480 TCGA-HNSC
|
| 60 |
+
DO14343 d89b1fd6-bef4-4803-8ed3-3b442be600b6 TCGA-CV-5432 TCGA-HNSC
|
| 61 |
+
DO14420 d189a6c6-210f-4957-b500-457d5ce3f867 TCGA-CR-7404 TCGA-HNSC
|
| 62 |
+
DO14566 4075717b-2240-4842-a00a-0c4dbcb910c4 TCGA-CV-6961 TCGA-HNSC
|
| 63 |
+
DO14582 c7df3466-b9a7-4818-883b-d0cd08483570 TCGA-CR-5249 TCGA-HNSC
|
| 64 |
+
DO14614 e8ac39e6-377a-412c-b18d-f6c4fc654ec6 TCGA-HD-7753 TCGA-HNSC
|
| 65 |
+
DO14678 3c975b68-4a6e-4c97-a8d1-b9a427d4957c TCGA-CV-5442 TCGA-HNSC
|
| 66 |
+
DO14686 cd032ddb-55f2-4c77-8dcf-e4e630f7de6f TCGA-CR-6470 TCGA-HNSC
|
| 67 |
+
DO14718 534f85f2-b9eb-45be-87d5-c3dffd13d541 TCGA-CV-7255 TCGA-HNSC
|
| 68 |
+
DO14742 d2ca9a4d-c14c-40c0-871c-88794a02e4aa TCGA-CV-7180 TCGA-HNSC
|
| 69 |
+
DO14750 e303b8c4-6428-4134-bb9d-a95dc1d8e35e TCGA-CQ-6228 TCGA-HNSC
|
| 70 |
+
DO14814 f33aa93f-b32d-4c4f-9a71-1e5ab69c6899 TCGA-CN-6989 TCGA-HNSC
|
| 71 |
+
DO14862 cb99fb14-641c-4fb3-bf1d-17efb8cd982a TCGA-BA-5149 TCGA-HNSC
|
| 72 |
+
DO15014 8fc1f1be-d2d5-4b3a-9973-f4d964018beb TCGA-CV-5973 TCGA-HNSC
|
| 73 |
+
DO15022 4cffea0b-90a7-4c86-a73f-bb8feca3ada7 TCGA-CN-5365 TCGA-HNSC
|
| 74 |
+
DO15046 ad6c9d09-2c03-4786-a72d-dd2aa5f603d4 TCGA-CX-7086 TCGA-HNSC
|
| 75 |
+
DO15110 1f744ada-6de1-446d-b62c-76a7fc8b40af TCGA-CN-4737 TCGA-HNSC
|
| 76 |
+
DO1537 a482fc4c-ff1d-46a0-a968-7df06422cc4b TCGA-B6-A0WX TCGA-BRCA
|
| 77 |
+
DO15398 6909302d-358a-46cd-8052-eedb09c83c83 TCGA-CN-6994 TCGA-HNSC
|
| 78 |
+
DO15414 53d9a130-4a3a-4491-85cd-9a44415a1632 TCGA-DQ-5625 TCGA-HNSC
|
| 79 |
+
DO15430 d7641ca1-a062-4dd2-9414-447af7311a84 TCGA-CN-5374 TCGA-HNSC
|
| 80 |
+
DO15503 90a8d2cb-1f31-45f9-8575-eed3cb9a7798 TCGA-CV-7100 TCGA-HNSC
|
| 81 |
+
DO15591 64bb5550-2735-4401-a0db-58ec1020a32d TCGA-CV-5431 TCGA-HNSC
|
| 82 |
+
DO15791 e7d49212-e9cb-4cd7-923a-bdfc82a4a150 TCGA-CN-6011 TCGA-HNSC
|
| 83 |
+
DO15815 8c238d30-df8e-4e6b-98fc-21696269a294 TCGA-CV-6956 TCGA-HNSC
|
| 84 |
+
DO15870 0956bc67-dd55-4c54-a56a-f22f2856662a TCGA-CR-6472 TCGA-HNSC
|
| 85 |
+
DO15911 180036a2-3b56-405e-a1fe-d5932517b6c7 TCGA-CV-5443 TCGA-HNSC
|
| 86 |
+
DO15943 468649e7-1525-4b8a-8ab5-ddb27db5f022 TCGA-CR-6482 TCGA-HNSC
|
| 87 |
+
DO16015 9e178d80-fe90-4998-9dae-726c4beceef7 TCGA-CR-5250 TCGA-HNSC
|
| 88 |
+
DO16136 9da334ed-314c-4445-ba29-ffd9bb42403c TCGA-AK-3428 TCGA-KIRC
|
| 89 |
+
DO16233 900b5f21-e8fb-4a3f-b8e3-ef098716e508 TCGA-CJ-4899 TCGA-KIRC
|
| 90 |
+
DO16248 3f93d1cf-a307-4e29-9e6f-239e9ffaa179 TCGA-CW-6093 TCGA-KIRC
|
| 91 |
+
DO16269 1e417b93-837d-4110-9090-239eb82c3e20 TCGA-BP-4968 TCGA-KIRC
|
| 92 |
+
DO1663 c364e81c-eb1e-4870-ab37-9c661f5f2e3d TCGA-AR-A0TX TCGA-BRCA
|
| 93 |
+
DO16769 8aaa4e25-5c12-4ace-96dc-91aaa0c4457c TCGA-B0-5094 TCGA-KIRC
|
| 94 |
+
DO16999 9b84d013-713b-4571-888c-12863fdd4a52 TCGA-BP-4327 TCGA-KIRC
|
| 95 |
+
DO17054 fb9bafa5-7133-4955-8156-4eb6763dc8e1 TCGA-B0-5695 TCGA-KIRC
|
| 96 |
+
DO17074 33cf0893-0411-4758-aa90-602bfedf0850 TCGA-BP-4807 TCGA-KIRC
|
| 97 |
+
DO17085 f2801b21-5444-4cc3-a642-c60c6d82cd3d TCGA-CZ-5454 TCGA-KIRC
|
| 98 |
+
DO17373 2939c03a-6f3f-4f7b-b246-34361baadeb9 TCGA-CJ-5681 TCGA-KIRC
|
| 99 |
+
DO17751 9f5c0a43-d222-484a-9249-d288387dd7a1 TCGA-BP-5010 TCGA-KIRC
|
| 100 |
+
DO17848 cc353686-e2b6-46a1-8095-48e2bc44b11f TCGA-BP-4977 TCGA-KIRC
|
| 101 |
+
DO18016 577847b9-f9b6-4954-868f-3d5ab2b4f694 TCGA-CZ-5453 TCGA-KIRC
|
| 102 |
+
DO18056 4c325ee1-9b8e-45d4-a0a7-e47ec1eac402 TCGA-CJ-5682 TCGA-KIRC
|
| 103 |
+
DO18164 9ecdde9c-2179-4830-bd10-c4fa40f26066 TCGA-BP-4756 TCGA-KIRC
|
| 104 |
+
DO18572 2ed3296a-5b10-40d8-8af4-90dc031657cd TCGA-AK-3455 TCGA-KIRC
|
| 105 |
+
DO18651 4b2ec8aa-8460-440e-b75b-6b92ae7a3ffc TCGA-DV-5566 TCGA-KIRC
|
| 106 |
+
DO18671 781acafa-a58b-4180-bbca-c5b675d51b17 TCGA-BP-5168 TCGA-KIRC
|
| 107 |
+
DO18701 f5759059-c0e3-4f1a-af96-5c7197d3c33c TCGA-CZ-5987 TCGA-KIRC
|
| 108 |
+
DO18839 bbbce1ba-c739-43ba-b9cf-a4f746491ae3 TCGA-B2-4102 TCGA-KIRC
|
| 109 |
+
DO18988 1adb05d4-e1ca-4ede-81c7-ff24a243abae TCGA-B2-4099 TCGA-KIRC
|
| 110 |
+
DO19028 659294b9-ded9-498a-bc00-c1d4d456aa4a TCGA-A3-3363 TCGA-KIRC
|
| 111 |
+
DO19380 78048432-2de1-498c-9cf1-9c5bd43daa39 TCGA-CJ-4870 TCGA-KIRC
|
| 112 |
+
DO19733 0487fc41-386c-4d76-9084-daf959bf5e98 TCGA-CW-5585 TCGA-KIRC
|
| 113 |
+
DO20082 bbdaa931-e922-49be-bfbf-fa0c2ae27d7a TCGA-CW-6087 TCGA-KIRC
|
| 114 |
+
DO20416 7664c241-b369-4d76-9cd1-7f2bdc1d267f TCGA-BP-4326 TCGA-KIRC
|
| 115 |
+
DO2055 441919eb-8c9f-478c-bda3-8de4f8295e8a TCGA-C8-A12L TCGA-BRCA
|
| 116 |
+
DO20604 7ca06dbe-f073-4aee-86ff-a545fad5ea93 TCGA-CJ-4878 TCGA-KIRC
|
| 117 |
+
DO20768 a121406a-30b2-4717-ae96-edcb17d911e2 TCGA-B9-4115 TCGA-KIRP
|
| 118 |
+
DO20770 71b35cd8-ecee-4599-bb24-b1ef78118423 TCGA-B9-4117 TCGA-KIRP
|
| 119 |
+
DO2078 f6e1ec78-5ad9-4879-9b7e-262d17b166ad TCGA-BH-A18R TCGA-BRCA
|
| 120 |
+
DO20780 5c9a6c40-982e-45c7-98d3-aeb6cad16986 TCGA-B9-4113 TCGA-KIRP
|
| 121 |
+
DO20839 2272c592-b9b6-4483-9f7f-6e185699b900 TCGA-AL-3468 TCGA-KIRP
|
| 122 |
+
DO2084 aeb6a3a6-b57a-4c53-90e6-e833777dd2a1 TCGA-BH-A0H6 TCGA-BRCA
|
| 123 |
+
DO20849 4edd92e1-5da5-49a3-80e4-fa456aa425b8 TCGA-B9-4116 TCGA-KIRP
|
| 124 |
+
DO20863 a6e960af-3ca5-42fb-b546-451c2583282d TCGA-B9-4617 TCGA-KIRP
|
| 125 |
+
DO20867 75a53bff-bf0f-4ad8-9b8e-14a27914d4f2 TCGA-B3-3925 TCGA-KIRP
|
| 126 |
+
DO20905 a7bf785f-0539-4899-ac21-47d1fc347ee8 TCGA-AL-3473 TCGA-KIRP
|
| 127 |
+
DO20915 df135722-b98d-4dec-906d-77da13df39b9 TCGA-B9-4114 TCGA-KIRP
|
| 128 |
+
DO20923 42d044ae-1416-477d-ac80-779c49bc8d35 TCGA-AL-3472 TCGA-KIRP
|
| 129 |
+
DO20928 9fc40b03-0b56-4b1f-93ed-dbc455ae3fd9 TCGA-AL-3466 TCGA-KIRP
|
| 130 |
+
DO2096 4e6edfe6-adcb-4c12-8ff4-38a79f5887e8 TCGA-B6-A0X5 TCGA-BRCA
|
| 131 |
+
DO2114 96457ebf-ae88-41f4-97e5-cdc3163418aa TCGA-BH-A0H0 TCGA-BRCA
|
| 132 |
+
DO219106 9f45a227-1fd5-478c-a194-4a5c61b68cc3 TCGA-QG-A5YW TCGA-COAD
|
| 133 |
+
DO219580 25e893ad-a0fd-45ae-9aa9-9bcd7c2ec8e5 TCGA-MR-A520 TCGA-LIHC
|
| 134 |
+
DO219585 c7a2f394-3e3f-4c90-9f1e-f2be3e5b0d6b TCGA-DJ-A13L TCGA-THCA
|
| 135 |
+
DO219944 ea0e5ae7-1f15-4504-aea3-efd1f6733304 TCGA-DX-A1KU TCGA-SARC
|
| 136 |
+
DO219945 cd73a55f-03c1-4da0-9fa7-1ae4a263664e TCGA-DX-A1KW TCGA-SARC
|
| 137 |
+
DO219949 854e42bc-95b2-4b77-a4b2-ffbd6c28b281 TCGA-DX-A1L0 TCGA-SARC
|
| 138 |
+
DO219951 73d2e8ef-4e10-416a-845b-f48374e48ecb TCGA-DX-A1L2 TCGA-SARC
|
| 139 |
+
DO219952 9517413b-b5b8-4130-8ba1-d86d44abe2ec TCGA-DX-A1L3 TCGA-SARC
|
| 140 |
+
DO219954 b5f27002-4277-418d-90b0-cc5a2742692e TCGA-DX-A23R TCGA-SARC
|
| 141 |
+
DO219960 3608bfbb-2a92-4ec2-a330-bf4790a8b65c TCGA-DX-A240 TCGA-SARC
|
| 142 |
+
DO219961 6b189eff-b919-49d6-8775-dbf32c9ccbba TCGA-DX-A2IZ TCGA-SARC
|
| 143 |
+
DO219964 f75d2589-fd7b-49a1-a1c8-0b4726b81f3c TCGA-DX-A2J4 TCGA-SARC
|
| 144 |
+
DO219965 39f68467-3d37-4b19-aa43-ed7b120c619d TCGA-DX-A3LS TCGA-SARC
|
| 145 |
+
DO219966 e7a72f7e-77b1-4d95-8f37-39a6cffc35dc TCGA-DX-A3LT TCGA-SARC
|
| 146 |
+
DO219967 e786de34-4c21-460f-89ab-008de4347049 TCGA-DX-A3LU TCGA-SARC
|
| 147 |
+
DO219968 2cacd9d0-8414-4f17-8259-c9d838597f39 TCGA-DX-A3LW TCGA-SARC
|
| 148 |
+
DO219969 3505f91d-bc66-4732-84b6-18c5b32ca6b8 TCGA-DX-A3LY TCGA-SARC
|
| 149 |
+
DO219970 b85e2156-d5ac-486c-93e1-86525ea41a64 TCGA-DX-A3M1 TCGA-SARC
|
| 150 |
+
DO219972 8f3f6f7c-4b50-467e-a6e8-d836735913f6 TCGA-DX-A3U5 TCGA-SARC
|
| 151 |
+
DO219974 ea3a524b-6ee3-4904-8924-29fcaa20985c TCGA-DX-A3U7 TCGA-SARC
|
| 152 |
+
DO220118 7bab6742-4e3d-4fcf-9b90-d3e0218b4d79 TCGA-FX-A3NJ TCGA-SARC
|
| 153 |
+
DO220122 7f9031da-124a-4a38-83e6-878a50e58c24 TCGA-FX-A3RE TCGA-SARC
|
| 154 |
+
DO220126 12bfd446-2ee5-4664-a492-c023d134f60f TCGA-FX-A48G TCGA-SARC
|
| 155 |
+
DO220138 9c9b53e5-cdbe-4510-a839-4a3b5039681b TCGA-HB-A43Z TCGA-SARC
|
| 156 |
+
DO220140 c5b0e6be-48b5-40e5-9d90-f9112050aa88 TCGA-HB-A5W3 TCGA-SARC
|
| 157 |
+
DO220153 c5e23947-253b-40d2-916a-277b2a580be2 TCGA-IE-A4EI TCGA-SARC
|
| 158 |
+
DO220157 590fdb76-20ef-4f95-9783-7d80e7a25e5a TCGA-IE-A4EK TCGA-SARC
|
| 159 |
+
DO220163 4e8c570c-3cbe-4878-8b2c-b966868dd60d TCGA-IF-A4AJ TCGA-SARC
|
| 160 |
+
DO220168 5a64f7e9-3833-4a3c-962b-13eb3e305c69 TCGA-IS-A3K7 TCGA-SARC
|
| 161 |
+
DO220172 257dc623-41e1-4fa3-859c-c787db10bd60 TCGA-IS-A3KA TCGA-SARC
|
| 162 |
+
DO220174 0da5ee6c-d012-4a57-9992-9d74937aaa5f TCGA-IW-A3M4 TCGA-SARC
|
| 163 |
+
DO220176 c20682a1-f340-430d-99f9-077fe093ed19 TCGA-IW-A3M5 TCGA-SARC
|
| 164 |
+
DO220221 8654c2b5-27d7-43c5-9a39-7fdefd7a0324 TCGA-MO-A47R TCGA-SARC
|
| 165 |
+
DO220331 83b3060c-2449-4581-88ef-817d126e4525 TCGA-KL-8323 TCGA-KICH
|
| 166 |
+
DO220333 6101ffe6-2ef6-4256-9d6b-a7c545836995 TCGA-KL-8325 TCGA-KICH
|
| 167 |
+
DO220334 03f3dd32-e1d1-485b-a968-3f55798f4d46 TCGA-KL-8326 TCGA-KICH
|
| 168 |
+
DO220335 02979422-5149-4750-ad5f-483e0bec6ac5 TCGA-KL-8327 TCGA-KICH
|
| 169 |
+
DO220336 452135f2-6de6-4593-a091-ddf6344ee431 TCGA-KL-8328 TCGA-KICH
|
| 170 |
+
DO220338 0f3c4f8f-0766-46b0-81b4-18ca4f6ea20a TCGA-KL-8330 TCGA-KICH
|
| 171 |
+
DO220339 8ac7f218-bd7b-4dda-8879-2cec49748e42 TCGA-KL-8331 TCGA-KICH
|
| 172 |
+
DO220340 3ec3bcb3-492c-4942-be02-76a67f57e38f TCGA-KL-8332 TCGA-KICH
|
| 173 |
+
DO220341 02ec7a3a-2812-4a65-93cd-07bf655cc91b TCGA-KL-8333 TCGA-KICH
|
| 174 |
+
DO220342 9aabcbd7-a26a-49b1-abf1-02c0dc3adcf2 TCGA-KL-8334 TCGA-KICH
|
| 175 |
+
DO220348 3129e869-bb2f-423a-bd20-d0daae33b11a TCGA-KL-8340 TCGA-KICH
|
| 176 |
+
DO220349 70e306ee-9584-49d6-81a1-a49d837ab6aa TCGA-KL-8341 TCGA-KICH
|
| 177 |
+
DO220350 c689ae1d-4a6b-45db-b4d1-6b34c5c61522 TCGA-KL-8342 TCGA-KICH
|
| 178 |
+
DO220351 b4a0bf74-1381-4c17-b97d-206810d9e59a TCGA-KL-8343 TCGA-KICH
|
| 179 |
+
DO220352 a5af1391-89d3-4f41-930e-7e8272afec98 TCGA-KL-8344 TCGA-KICH
|
| 180 |
+
DO220354 a122d361-835e-4cf2-850f-af7864aa112c TCGA-KL-8346 TCGA-KICH
|
| 181 |
+
DO220355 23656226-2d99-4548-959f-5c2eed803e33 TCGA-KM-8438 TCGA-KICH
|
| 182 |
+
DO220356 739deed4-d1dc-458f-bd0f-1e24d2bfd305 TCGA-KM-8439 TCGA-KICH
|
| 183 |
+
DO220357 872092b3-d31e-44d7-bd03-e29f52f8ab5a TCGA-KM-8440 TCGA-KICH
|
| 184 |
+
DO220358 f31ba8ec-a3dd-4dd8-8d15-f6c5975ebe44 TCGA-KM-8441 TCGA-KICH
|
| 185 |
+
DO220359 40f79cfe-a51f-497b-b06e-7a4f9004f092 TCGA-KM-8442 TCGA-KICH
|
| 186 |
+
DO220360 2f1c4db6-88a7-4a5d-a5fe-94d85f569440 TCGA-KM-8443 TCGA-KICH
|
| 187 |
+
DO220361 4e4faf51-2cbf-4910-a093-186660e4a19b TCGA-KM-8476 TCGA-KICH
|
| 188 |
+
DO220362 9680b5ff-1882-434f-9981-e8ac33764b93 TCGA-KM-8477 TCGA-KICH
|
| 189 |
+
DO220363 2ea84e23-6a0a-4c5d-9c70-d5355a85df35 TCGA-KM-8639 TCGA-KICH
|
| 190 |
+
DO220423 f3ba1cf8-ac67-4da8-96f3-8680dc55f247 TCGA-KN-8418 TCGA-KICH
|
| 191 |
+
DO220433 dc04b82c-e1e8-4917-ba19-c2fea4a1b69a TCGA-KN-8419 TCGA-KICH
|
| 192 |
+
DO220439 2fc175d1-9d95-44fd-a53e-820f6f404551 TCGA-KN-8421 TCGA-KICH
|
| 193 |
+
DO220449 4a88bc85-4c6e-4fac-8dbb-d3fc5f36b35e TCGA-KN-8422 TCGA-KICH
|
| 194 |
+
DO220468 8c9e1f60-1838-4aa4-a667-c1cef7dddbdf TCGA-KN-8424 TCGA-KICH
|
| 195 |
+
DO220474 cc316faf-dcb1-4dea-b8b0-a17d000ff40f TCGA-KN-8425 TCGA-KICH
|
| 196 |
+
DO220478 b08dfba8-6afb-4217-9259-72be6f1f3363 TCGA-KN-8426 TCGA-KICH
|
| 197 |
+
DO220479 955d428c-8fe8-456b-b86d-65ea6c2a1b18 TCGA-KN-8427 TCGA-KICH
|
| 198 |
+
DO220509 04e9a63a-d742-488c-8564-bf9d79aa2091 TCGA-KN-8431 TCGA-KICH
|
| 199 |
+
DO220510 af1111af-9384-4e37-9b16-227d38007ebd TCGA-KN-8432 TCGA-KICH
|
| 200 |
+
DO220519 e1e15226-9f8e-4035-8f08-eb1fed57080e TCGA-KN-8434 TCGA-KICH
|
| 201 |
+
DO220525 f2b58595-dec0-4194-ab33-7c7dc6c058a6 TCGA-KN-8435 TCGA-KICH
|
| 202 |
+
DO220535 f1c1502e-1bd6-425a-b651-454363809668 TCGA-KN-8437 TCGA-KICH
|
| 203 |
+
DO220549 d52c3cb6-921e-4f26-b49c-e6e4f11f9c06 TCGA-KO-8405 TCGA-KICH
|
| 204 |
+
DO220554 9aa02d46-294b-4e99-b9ee-56edcbfb5275 TCGA-KO-8406 TCGA-KICH
|
| 205 |
+
DO220556 151f07f5-1529-4fcd-912c-e55b2ff8abef TCGA-KO-8407 TCGA-KICH
|
| 206 |
+
DO220577 ee8c1919-17a9-4df1-8aa5-79546621b23c TCGA-KO-8411 TCGA-KICH
|
| 207 |
+
DO22106 799e9106-e62e-4681-8861-137f474ee8b8 TCGA-HT-7602 TCGA-LGG
|
| 208 |
+
DO22117 01c4ca12-ac08-41dd-9e4f-bfb0971688a2 TCGA-DU-7009 TCGA-LGG
|
| 209 |
+
DO22145 fc3b7596-f515-446f-81db-fed0154ca2c5 TCGA-EZ-7264 TCGA-LGG
|
| 210 |
+
DO22195 400b2fa3-baf7-4baa-99b4-88b9f25b5049 TCGA-E1-5318 TCGA-LGG
|
| 211 |
+
DO2222 49717f75-0f2d-4e1c-9a12-f1cd7877b80a TCGA-AN-A04D TCGA-BRCA
|
| 212 |
+
DO222303 67325322-483f-443f-9ffa-2a20d108a2fb TCGA-FF-8046 TCGA-DLBC
|
| 213 |
+
DO222304 f978cb0f-d319-4c01-b4c5-23ae1403a106 TCGA-FF-8047 TCGA-DLBC
|
| 214 |
+
DO222305 1f971af1-6772-4fe6-8d35-bbe527a037fe TCGA-FF-8041 TCGA-DLBC
|
| 215 |
+
DO222306 f0a326d2-1f3e-4a5d-bca8-32aaccc52338 TCGA-FF-8042 TCGA-DLBC
|
| 216 |
+
DO222307 c8cde9ea-89e9-4ee8-8a46-417a48f6d3ab TCGA-FF-8061 TCGA-DLBC
|
| 217 |
+
DO222308 a8e2df1e-4042-42af-9231-3a00e83489f0 TCGA-FF-8043 TCGA-DLBC
|
| 218 |
+
DO222309 0e9fcccc-0630-408d-a121-2c6413824cb7 TCGA-FF-8062 TCGA-DLBC
|
| 219 |
+
DO22285 6a0bcf0c-fa4c-4119-99d2-f722b781d20f TCGA-FG-8182 TCGA-LGG
|
| 220 |
+
DO22293 648252da-dd86-48d1-ae75-4257e3142b0b TCGA-FG-5964 TCGA-LGG
|
| 221 |
+
DO22295 70939245-41e0-4845-a473-5fef719b9828 TCGA-DU-6407 TCGA-LGG
|
| 222 |
+
DO22324 42251e02-b687-4bf1-a1b6-e3dd978542e4 TCGA-HW-7487 TCGA-LGG
|
| 223 |
+
DO22356 f86fa219-34e9-4812-8edf-2b66d7219779 TCGA-CS-5395 TCGA-LGG
|
| 224 |
+
DO22382 84ae73e4-3b39-4444-9bfb-3fc6057eab32 TCGA-DU-7301 TCGA-LGG
|
| 225 |
+
DO22417 5017c115-39b8-4c5c-8be6-2cb298d7c641 TCGA-HT-7695 TCGA-LGG
|
| 226 |
+
DO22490 97cb8701-7d63-4142-b404-006fbb3ecb0b TCGA-DU-6401 TCGA-LGG
|
| 227 |
+
DO22496 010aac75-3bfe-4bf2-b866-af0f2d92f125 TCGA-DB-5278 TCGA-LGG
|
| 228 |
+
DO22524 1cc53ade-73c0-492b-a7dc-328269fd0e66 TCGA-DU-5874 TCGA-LGG
|
| 229 |
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DO22536 84569c1a-6baa-4380-ae3c-707df1be4618 TCGA-E1-5319 TCGA-LGG
|
| 230 |
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DO22548 a5d3f3fb-6541-419d-b47e-720d438f1bff TCGA-CS-6668 TCGA-LGG
|
| 231 |
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DO22720 a5f851c5-6130-4ea1-9f86-be59331a92f7 TCGA-IK-7675 TCGA-LGG
|
| 232 |
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DO22736 55a67639-ed05-4143-af96-a377bc5489ee TCGA-HW-7486 TCGA-LGG
|
| 233 |
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DO22836 68015cc7-d3a1-4c90-b8fb-5d14143d9960 TCGA-DD-A1EB TCGA-LIHC
|
| 234 |
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DO22838 728aedb3-551a-4c16-9be3-7733a9539e9e TCGA-ES-A2HS TCGA-LIHC
|
| 235 |
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DO22839 39eb1db0-2722-4af4-8254-d0e10256c64d TCGA-G3-A3CK TCGA-LIHC
|
| 236 |
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DO22854 b7d6621c-8bce-4a01-8588-9d7761d9372b TCGA-EP-A26S TCGA-LIHC
|
| 237 |
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DO22860 ca8afb2c-be3d-41e8-8dbc-19fe7ab6154b TCGA-CC-5260 TCGA-LIHC
|
| 238 |
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DO22866 f435ec00-0db8-46b4-96bf-890a9d931df3 TCGA-G3-A25V TCGA-LIHC
|
| 239 |
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DO22875 1ad7f17e-b3b8-40e9-aab8-5a17e0aec408 TCGA-CC-5261 TCGA-LIHC
|
| 240 |
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DO22884 f6e5979d-d6a9-4408-bd1b-1b37d1064e83 TCGA-G3-A25T TCGA-LIHC
|
| 241 |
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DO22887 dfa15fbe-bb14-40ce-afc6-70694112e3e2 TCGA-BC-A10Q TCGA-LIHC
|
| 242 |
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DO22896 6dcfc418-ab28-4365-95ea-c1ae254f2341 TCGA-BC-A217 TCGA-LIHC
|
| 243 |
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DO22914 bfd20d71-18a5-4f01-8d56-84378ad92359 TCGA-FV-A3I1 TCGA-LIHC
|
| 244 |
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DO22929 4bbdbe77-f3cb-4b64-9826-72debb734784 TCGA-BC-A216 TCGA-LIHC
|
| 245 |
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DO22932 18c08546-bed3-4ed9-b223-479afe633c8e TCGA-EP-A2KB TCGA-LIHC
|
| 246 |
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DO22935 f15279dd-5d12-460b-9bd1-b4f95681ca65 TCGA-EP-A3RK TCGA-LIHC
|
| 247 |
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DO22953 1b428ada-eb3f-40c2-b4bf-1bbf6b61729d TCGA-CC-5262 TCGA-LIHC
|
| 248 |
+
DO22956 7dd3bda9-3a0a-46fa-b052-a6d24b847804 TCGA-ES-A2HT TCGA-LIHC
|
| 249 |
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DO22959 3acf7438-fd65-442c-8a30-68b6714537f3 TCGA-DD-A1EI TCGA-LIHC
|
| 250 |
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DO22970 06ac44e9-1226-4202-828b-6f224766d7ac TCGA-G3-A25W TCGA-LIHC
|
| 251 |
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DO22978 3dd7c93d-8194-44ee-8014-10dd81ef6441 TCGA-FV-A3R3 TCGA-LIHC
|
| 252 |
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DO22986 cab5b264-abab-4bdb-816e-9d2c0ac30629 TCGA-DD-A1EH TCGA-LIHC
|
| 253 |
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DO22994 52292ffc-0902-4d97-b461-20723987a177 TCGA-DD-A1EJ TCGA-LIHC
|
| 254 |
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DO22998 0ff6364a-e6f4-47ca-a7e7-14705bf0a8df TCGA-DD-A1ED TCGA-LIHC
|
| 255 |
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DO23018 b865dec4-f051-4fbe-9405-f832ff2010d7 TCGA-G3-A25S TCGA-LIHC
|
| 256 |
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DO23023 440375d7-cdcd-418a-8992-0a8ca42d290d TCGA-EP-A2KA TCGA-LIHC
|
| 257 |
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DO23028 670c1cbc-4494-45f1-bb8b-18db82d4f7e0 TCGA-CC-A1HT TCGA-LIHC
|
| 258 |
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DO23048 2854ffe8-1f77-497a-bf49-11e0189fee35 TCGA-DD-A1EL TCGA-LIHC
|
| 259 |
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DO23073 239b4298-b090-480a-a19f-9758a827aa30 TCGA-G3-A25Y TCGA-LIHC
|
| 260 |
+
DO2323 96312510-c126-485d-8109-ed81844a1dc3 TCGA-AN-A0G0 TCGA-BRCA
|
| 261 |
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DO2341 d9dc3b59-613d-469e-8b4f-6c5a557eb26a TCGA-EW-A3U0 TCGA-BRCA
|
| 262 |
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DO23584 97831d28-ab41-4c18-bfc2-c4c6bc757d13 TCGA-91-6847 TCGA-LUAD
|
| 263 |
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DO23608 d5326429-9805-47f9-97b0-fbda658e3f01 TCGA-91-6840 TCGA-LUAD
|
| 264 |
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DO23610 ebcba7f2-ce13-4bae-97cd-91a6b1dcd465 TCGA-50-5932 TCGA-LUAD
|
| 265 |
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DO23648 df576520-a6b6-4c9b-8d06-3f59cc5342fd TCGA-75-7030 TCGA-LUAD
|
| 266 |
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DO23651 028e99e9-5b9a-4954-bb6e-6d4709a3cea8 TCGA-55-6986 TCGA-LUAD
|
| 267 |
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DO23687 f063ddbb-1668-40df-9ec2-b0a23ca2c389 TCGA-55-6984 TCGA-LUAD
|
| 268 |
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DO23702 9b132e4f-7e35-4cc5-8711-43ad62b906d0 TCGA-50-6591 TCGA-LUAD
|
| 269 |
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DO23735 a1e65587-24c1-4b41-92a7-4e1f15fffd78 TCGA-49-4512 TCGA-LUAD
|
| 270 |
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DO23744 bf15f7ad-9d92-473b-91d1-f24aa373ab97 TCGA-44-2659 TCGA-LUAD
|
| 271 |
+
DO23786 61c655ec-52b5-453f-a6cc-b2aba445b027 TCGA-49-4486 TCGA-LUAD
|
| 272 |
+
DO23838 9f81c602-8afa-4588-b0b6-6e5a1a128d5a TCGA-05-4396 TCGA-LUAD
|
| 273 |
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DO23942 63bd2175-4b7c-44c9-aef3-9efc8f79837b TCGA-05-5429 TCGA-LUAD
|
| 274 |
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DO24130 397d3f69-1453-4057-b177-8723eec923d1 TCGA-97-8171 TCGA-LUAD
|
| 275 |
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DO24307 a3de401d-91fe-49a2-bb07-81c1a06506e6 TCGA-05-4389 TCGA-LUAD
|
| 276 |
+
DO24467 cd0dc947-b708-464e-beb7-954c9d4583e7 TCGA-73-4659 TCGA-LUAD
|
| 277 |
+
DO24497 d2824e6d-3784-45c2-9b0f-52b17356b5da TCGA-75-5147 TCGA-LUAD
|
| 278 |
+
DO24646 1ab7ad70-0f80-41d8-8efa-3baebc8223cc TCGA-64-1680 TCGA-LUAD
|
| 279 |
+
DO24675 3d2aa654-1b5f-4eb4-a1c2-af31f5760069 TCGA-05-4420 TCGA-LUAD
|
| 280 |
+
DO24729 501c987e-d1eb-48a9-89eb-72a5062c90b4 TCGA-78-7158 TCGA-LUAD
|
| 281 |
+
DO24860 46592b7b-6968-42a6-83af-0917c9f4a9a5 TCGA-78-7535 TCGA-LUAD
|
| 282 |
+
DO25010 c4d1e105-28b7-48df-abef-fbe09782fdb2 TCGA-55-6972 TCGA-LUAD
|
| 283 |
+
DO2503 74f31744-0aed-4633-beec-4e203315f0b7 TCGA-BH-A0DT TCGA-BRCA
|
| 284 |
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DO25068 dbd5b0de-94c9-45dd-afb3-6820a7ecaca2 TCGA-67-6215 TCGA-LUAD
|
| 285 |
+
DO2509 5fd9552a-c742-4388-940d-295d1107ae00 TCGA-AR-A1AY TCGA-BRCA
|
| 286 |
+
DO25104 f77cf4d3-9829-4fd3-95a1-b29aed9e94b8 TCGA-64-1678 TCGA-LUAD
|
| 287 |
+
DO25189 368e23f0-e573-4547-bf5a-14080baf737b TCGA-50-5930 TCGA-LUAD
|
| 288 |
+
DO25320 6dfd47d2-831a-4386-9051-f78199a16bb5 TCGA-05-4397 TCGA-LUAD
|
| 289 |
+
DO25444 762dea8a-5b41-4058-979a-b7876ed13d7e TCGA-05-4395 TCGA-LUAD
|
| 290 |
+
DO2551 4da999a0-ef41-4a0b-b1d1-446b39cc855a TCGA-EW-A1PH TCGA-BRCA
|
| 291 |
+
DO25622 21fb46f9-4bbb-441c-af19-a687e9138344 TCGA-49-6742 TCGA-LUAD
|
| 292 |
+
DO25737 8994c8c4-9ea0-46cb-b4a5-0055d7da5bfa TCGA-44-6148 TCGA-LUAD
|
| 293 |
+
DO25797 0df573ee-28f0-4244-b434-09e6ca59fbf0 TCGA-67-3771 TCGA-LUAD
|
| 294 |
+
DO25847 31c96e35-5e2f-429c-b12a-7bc5a497a300 TCGA-05-4398 TCGA-LUAD
|
| 295 |
+
DO25887 a8d6694c-a213-4544-ac0b-63bce16d8f4e TCGA-75-6203 TCGA-LUAD
|
| 296 |
+
DO2593 993103b1-e5a1-4c33-8629-be53ebc41d64 TCGA-A8-A094 TCGA-BRCA
|
| 297 |
+
DO25985 659668e8-f0d9-4ff2-bbc8-9246f2ef49ab TCGA-55-7281 TCGA-LUAD
|
| 298 |
+
DO26062 0d66bf6c-eed0-4726-bd5b-3bf6d610b4e0 TCGA-50-6597 TCGA-LUAD
|
| 299 |
+
DO26230 4c31127e-d095-4978-9dac-35153c27f6ed TCGA-73-4666 TCGA-LUAD
|
| 300 |
+
DO26280 35cb7841-9b09-465a-90c5-e3b8a9faad49 TCGA-55-6982 TCGA-LUAD
|
| 301 |
+
DO2629 0a2a3529-f645-4967-9a58-89ee20b8bb62 TCGA-E2-A15E TCGA-BRCA
|
| 302 |
+
DO26354 cc4bd56a-25c5-4c48-b583-ac3aeb778ca6 TCGA-38-4628 TCGA-LUAD
|
| 303 |
+
DO26427 9a874b64-d0d6-416e-97bc-e9071ed0b16b TCGA-18-3415 TCGA-LUSC
|
| 304 |
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DO26434 7d905f8f-3967-4ea8-96c8-17b1b03fbec3 TCGA-43-5670 TCGA-LUSC
|
| 305 |
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DO26445 9293e197-e38a-4e19-a7d0-1b45d1ad48bd TCGA-43-3920 TCGA-LUSC
|
| 306 |
+
DO26459 395babb3-3f5d-4e71-a675-af4443f23028 TCGA-85-8052 TCGA-LUSC
|
| 307 |
+
DO26461 3edf933a-be1a-46e8-bfb0-acdff30e64a0 TCGA-56-1622 TCGA-LUSC
|
| 308 |
+
DO26463 0e2ee54a-51c9-4868-842d-a2a1c1cfb016 TCGA-21-1082 TCGA-LUSC
|
| 309 |
+
DO26469 fd9ee494-65fe-4de4-adff-7952a059b17f TCGA-60-2698 TCGA-LUSC
|
| 310 |
+
DO2647 27b05b15-a44b-45ed-a6e3-e7d1ca488ea9 TCGA-A8-A09X TCGA-BRCA
|
| 311 |
+
DO26475 aecf85cc-058c-46f3-9cdc-3573ac3b8438 TCGA-94-7943 TCGA-LUSC
|
| 312 |
+
DO26476 e58e8850-154f-4695-bee0-005c76410327 TCGA-60-2713 TCGA-LUSC
|
| 313 |
+
DO26492 4ca13c92-84b4-4edf-842a-b20b7e713415 TCGA-96-7545 TCGA-LUSC
|
| 314 |
+
DO26512 3666bc65-8e40-409e-9a1f-41583dd6d978 TCGA-68-7755 TCGA-LUSC
|
| 315 |
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DO26540 a3e1ac67-a1f2-44fb-8343-a7e8239fc24a TCGA-60-2724 TCGA-LUSC
|
| 316 |
+
DO26553 305f72a8-069b-410b-bbe4-4ecb761c748d TCGA-22-5485 TCGA-LUSC
|
| 317 |
+
DO26563 e7d1b042-5b1c-4c29-b501-a5ef4810ecd2 TCGA-34-5240 TCGA-LUSC
|
| 318 |
+
DO26572 d4bc755a-2585-4529-ae36-7e1d88bdecfe TCGA-18-3408 TCGA-LUSC
|
| 319 |
+
DO26596 ce8612ab-3149-4a6a-b424-29c0c21c9b8b TCGA-43-3394 TCGA-LUSC
|
| 320 |
+
DO26599 220be009-8f1e-4a77-ab04-3220b5f87ea6 TCGA-21-1076 TCGA-LUSC
|
| 321 |
+
DO26653 0ab8d063-62b4-4d47-82aa-e3351a60029d TCGA-21-1078 TCGA-LUSC
|
| 322 |
+
DO26692 9dff11cd-0784-4a60-9c77-43f8957d3dfa TCGA-90-7767 TCGA-LUSC
|
| 323 |
+
DO26710 da93a143-5799-4856-a5f4-3ff3b9284311 TCGA-66-2795 TCGA-LUSC
|
| 324 |
+
DO26719 8e6020a1-68df-4d57-ad4e-33acc0861fb3 TCGA-85-8277 TCGA-LUSC
|
| 325 |
+
DO26769 0398eae1-7216-4595-80a5-6b117d96e070 TCGA-21-1083 TCGA-LUSC
|
| 326 |
+
DO26798 6fd72426-f6c8-47ca-a500-d5d3600b9b15 TCGA-60-2722 TCGA-LUSC
|
| 327 |
+
DO26806 1f6b2aca-7357-40d1-ba7a-99227d9900a2 TCGA-37-4135 TCGA-LUSC
|
| 328 |
+
DO26894 bd15f523-45c4-4d7c-a3cc-4fb56abb0e54 TCGA-22-5477 TCGA-LUSC
|
| 329 |
+
DO26934 adc853b1-b8bf-488c-a1e2-e95603459b55 TCGA-98-8022 TCGA-LUSC
|
| 330 |
+
DO2694 2a84997d-ccee-4f46-bea2-752534f26416 TCGA-E2-A1LL TCGA-BRCA
|
| 331 |
+
DO26957 dc6ceece-efaa-4b9d-9e12-d35863d7a902 TCGA-92-8064 TCGA-LUSC
|
| 332 |
+
DO26971 037c57d1-b4a5-45dc-bda4-0550461d321b TCGA-22-1016 TCGA-LUSC
|
| 333 |
+
DO26976 b5e2cbda-bbfa-4ef8-a9c4-cb978bef9b23 TCGA-56-7582 TCGA-LUSC
|
| 334 |
+
DO26987 1f11de87-a5fe-49fd-80c1-e279b2bc69de TCGA-60-2711 TCGA-LUSC
|
| 335 |
+
DO26993 7b982d5e-3a7d-40ac-bd25-6044c62879b6 TCGA-68-8250 TCGA-LUSC
|
| 336 |
+
DO27025 1ee543d5-b8c0-4f79-8373-6bb6319f2ee2 TCGA-66-2744 TCGA-LUSC
|
| 337 |
+
DO2706 eadf8482-e60e-4307-adb7-d5c3b9fa6cae TCGA-A2-A25B TCGA-BRCA
|
| 338 |
+
DO27067 81b7cbc1-c037-426f-95b0-d729a30697da TCGA-52-7812 TCGA-LUSC
|
| 339 |
+
DO27081 da61857a-b00e-4a19-ab02-8fbb2ee56bea TCGA-34-2600 TCGA-LUSC
|
| 340 |
+
DO27104 19f0cb8c-2e57-4310-967f-a9890f1605db TCGA-18-4721 TCGA-LUSC
|
| 341 |
+
DO2712 566792ae-f853-4a47-856d-f02cdcfcb18a TCGA-A8-A07I TCGA-BRCA
|
| 342 |
+
DO27120 e21964b8-47c7-4a5f-bfa7-7206222883d2 TCGA-21-5782 TCGA-LUSC
|
| 343 |
+
DO27128 43abe847-4ba7-466e-8283-5d7b80b999a7 TCGA-66-2756 TCGA-LUSC
|
| 344 |
+
DO2719 1d27253f-b036-44e7-a04d-8da5bbf57419 TCGA-E2-A1LK TCGA-BRCA
|
| 345 |
+
DO27208 422a46b2-a67c-4a7e-923f-9b651ced96f8 TCGA-22-5492 TCGA-LUSC
|
| 346 |
+
DO27279 87ac0ad4-8c9d-409e-9a86-1d201d01769d TCGA-66-2789 TCGA-LUSC
|
| 347 |
+
DO27318 376dfd27-68e8-4a1a-9c4f-5064279b2a9e TCGA-33-4586 TCGA-LUSC
|
| 348 |
+
DO27326 e6b72c24-1607-43b9-8b8a-7bf83eea5895 TCGA-66-2759 TCGA-LUSC
|
| 349 |
+
DO27334 9af6ed4e-8cdc-4f49-84e9-ba1053b5b3ca TCGA-66-2793 TCGA-LUSC
|
| 350 |
+
DO27453 585e6487-b0a3-4828-8a06-46bee01dff74 TCGA-60-2719 TCGA-LUSC
|
| 351 |
+
DO27484 3d1f4059-2220-45b4-a4d2-b14f76cec96a TCGA-60-2726 TCGA-LUSC
|
| 352 |
+
DO27588 c2598334-f866-4cc3-93ec-2d2e8b85d319 TCGA-77-6843 TCGA-LUSC
|
| 353 |
+
DO27636 ee850103-6314-41a3-9734-8b520de95b40 TCGA-77-7139 TCGA-LUSC
|
| 354 |
+
DO27747 b913d254-8307-4b8a-8313-d978e32bb38f TCGA-66-2766 TCGA-LUSC
|
| 355 |
+
DO2783 05506f4c-e701-4a9d-ae06-97f066aade43 TCGA-AN-A0AT TCGA-BRCA
|
| 356 |
+
DO27978 13f5814c-1f99-4ffa-84a4-3bbd8979faae TCGA-13-1477 TCGA-OV
|
| 357 |
+
DO28004 c8febeef-8e7e-459e-88cb-8086692dc559 TCGA-13-1491 TCGA-OV
|
| 358 |
+
DO28024 15556b28-c6bd-455a-87b6-4b7b3e33d0e4 TCGA-25-1634 TCGA-OV
|
| 359 |
+
DO28053 5c127332-5ca0-45f1-a5ac-4876ad94e491 TCGA-25-2400 TCGA-OV
|
| 360 |
+
DO28089 f34aa3b6-e966-49c6-bc55-130545772c53 TCGA-24-1103 TCGA-OV
|
| 361 |
+
DO28093 f0c353fd-947c-41e2-b643-3ecc0d69796c TCGA-04-1347 TCGA-OV
|
| 362 |
+
DO28119 6d10d4ee-6331-4bba-93bc-a7b64cc0b22a TCGA-04-1331 TCGA-OV
|
| 363 |
+
DO28233 a5030259-cf9c-4a58-8710-b9da8ee59320 TCGA-10-0937 TCGA-OV
|
| 364 |
+
DO28273 ee0a4a13-613e-4c5d-96c3-8083a013702d TCGA-13-0890 TCGA-OV
|
| 365 |
+
DO28289 58d34254-4f5b-40a4-9e9f-7160062fb2a4 TCGA-13-0912 TCGA-OV
|
| 366 |
+
DO28412 4c18d9cf-4af4-4a86-8b1c-f78795fbbd7e TCGA-24-1562 TCGA-OV
|
| 367 |
+
DO2842 359f12f9-5c41-48a4-85bc-fd7e307bf7d8 TCGA-EW-A1P8 TCGA-BRCA
|
| 368 |
+
DO28521 c435627c-159d-4a6d-a819-30abac24bf4d TCGA-25-1632 TCGA-OV
|
| 369 |
+
DO28763 e9483296-cb91-497a-b955-39a3c3289dac TCGA-04-1514 TCGA-OV
|
| 370 |
+
DO28823 90f4b65c-cfd4-4066-a5b9-842885c172e2 TCGA-13-0727 TCGA-OV
|
| 371 |
+
DO28952 21f6bf91-dfc6-4a59-8b76-88a0f95c7b47 TCGA-24-1544 TCGA-OV
|
| 372 |
+
DO28962 d3164236-c14a-4230-b527-ecd1a3992f02 TCGA-24-1614 TCGA-OV
|
| 373 |
+
DO29146 7ca97692-0bf5-4bbd-81ce-10a051d04bd5 TCGA-10-0938 TCGA-OV
|
| 374 |
+
DO29308 4b930a10-4b12-4428-84f9-3255b4a3bc4f TCGA-04-1349 TCGA-OV
|
| 375 |
+
DO29398 9fb1ba57-2007-4477-b000-2d36f163efd2 TCGA-23-1110 TCGA-OV
|
| 376 |
+
DO29760 c9e28934-8379-4511-817c-d787f2c4ca3a TCGA-13-1487 TCGA-OV
|
| 377 |
+
DO29850 098acb76-0bf5-44e5-bcae-f919cf5fa5e5 TCGA-24-1466 TCGA-OV
|
| 378 |
+
DO29940 29949b65-2913-4c86-bbd7-b85c5df6f15e TCGA-61-2000 TCGA-OV
|
| 379 |
+
DO29980 ff530f28-0ec0-4494-bb54-44bb055bae1c TCGA-13-0723 TCGA-OV
|
| 380 |
+
DO30060 ba68f2cf-9271-41fd-9655-1fac7681f588 TCGA-24-1558 TCGA-OV
|
| 381 |
+
DO30090 2b14123b-8fcd-402c-9399-4e7c47f20252 TCGA-24-2290 TCGA-OV
|
| 382 |
+
DO30220 700e91bb-d675-41b2-bbbd-935767c7b447 TCGA-23-1118 TCGA-OV
|
| 383 |
+
DO30340 eeb9d147-608d-4692-8adf-2f601d23a8ff TCGA-36-1574 TCGA-OV
|
| 384 |
+
DO3037 784de7ac-8424-42eb-83d4-a1bebaa42b97 TCGA-A1-A0SM TCGA-BRCA
|
| 385 |
+
DO30550 5e18b17d-4626-4b6d-8ac6-e560cee0376c TCGA-24-1419 TCGA-OV
|
| 386 |
+
DO30750 7dcc809b-e33a-4453-b92a-c00786f48cb0 TCGA-24-1557 TCGA-OV
|
| 387 |
+
DO30970 30b8f4cd-9245-4496-a8a8-c3e59093bc0a TCGA-04-1367 TCGA-OV
|
| 388 |
+
DO3110 8bd68696-5eeb-46d7-9dc6-c39c849029a4 TCGA-A8-A08L TCGA-BRCA
|
| 389 |
+
DO31150 e43d3769-e25f-4fb7-9080-ea26defaf094 TCGA-13-0906 TCGA-OV
|
| 390 |
+
DO3140 2779fa01-ac93-4e80-a997-3385f72172c3 TCGA-A8-A08S TCGA-BRCA
|
| 391 |
+
DO31420 66c92b9e-de3c-4d1a-bb69-46ffbc6caf33 TCGA-24-1548 TCGA-OV
|
| 392 |
+
DO31551 62379be5-13f0-474b-94d3-6f944ec4ee96 TCGA-24-1552 TCGA-OV
|
| 393 |
+
DO3158 b5b99291-507e-4b68-a039-9a0f571f55df TCGA-AO-A12H TCGA-BRCA
|
| 394 |
+
DO31729 0c1a2e7d-e7e4-481e-a012-ef214c444497 TCGA-04-1542 TCGA-OV
|
| 395 |
+
DO31769 f5033d52-ec36-4e67-88d8-b6d898b81b2f TCGA-25-2391 TCGA-OV
|
| 396 |
+
DO3182 a824b3bd-34d5-4cc1-a92f-f9d6ac0f1814 TCGA-C8-A12Q TCGA-BRCA
|
| 397 |
+
DO31869 3c037acf-f453-4513-a6dd-129163ddde2a TCGA-09-2050 TCGA-OV
|
| 398 |
+
DO3188 1557183c-aedd-4df6-b98a-3654667f7b69 TCGA-A8-A09I TCGA-BRCA
|
| 399 |
+
DO32237 0a48873a-092a-4b25-822b-b0b3c18c08a8 TCGA-24-2024 TCGA-OV
|
| 400 |
+
DO32391 4eac2c98-86d2-4ee6-a1d3-157d013c78dc TCGA-09-1666 TCGA-OV
|
| 401 |
+
DO32420 3768d34f-1527-40db-b116-cd80cee5ec3a TCGA-09-2045 TCGA-OV
|
| 402 |
+
DO32596 8a6d2ce3-cc57-451b-9b07-8263782aa23f TCGA-23-1124 TCGA-OV
|
| 403 |
+
DO32646 206ce0ed-36a4-47ab-ac8e-f6fca6ac5c18 TCGA-24-0982 TCGA-OV
|
| 404 |
+
DO3412 f51af6db-2655-47fb-9ffc-83a503a728ea TCGA-AN-A0XR TCGA-BRCA
|
| 405 |
+
DO3458 1174f6e4-ffbe-4e59-a000-8d861c968369 TCGA-AO-A03L TCGA-BRCA
|
| 406 |
+
DO3482 08da7c4c-3067-4bcf-9d7a-78566df72e69 TCGA-B6-A0RE TCGA-BRCA
|
| 407 |
+
DO3500 2d29a4ac-98e7-4663-9dd6-5681bc32ac2e TCGA-A2-A3KC TCGA-BRCA
|
| 408 |
+
DO3614 d67cd793-2931-429a-9084-2f3c4c8be7ad TCGA-A2-A0EY TCGA-BRCA
|
| 409 |
+
DO36226 5dd69c3e-9a3d-450c-9f10-10b4d6daeeba TCGA-HC-7075 TCGA-PRAD
|
| 410 |
+
DO36248 dc40cf49-b208-4f59-b5f0-cfbdd02970b2 TCGA-CH-5750 TCGA-PRAD
|
| 411 |
+
DO36253 c039014f-821c-43d6-9d3c-914f9c8d98fa TCGA-CH-5788 TCGA-PRAD
|
| 412 |
+
DO36254 3acd8328-76f7-4f43-9660-394b95748c3d TCGA-CH-5771 TCGA-PRAD
|
| 413 |
+
DO36262 91e249a6-b0b0-487e-9fad-0e9f5798dcae TCGA-HC-7740 TCGA-PRAD
|
| 414 |
+
DO36273 87959988-bb25-47ee-afc1-9af92592bbe7 TCGA-G9-7522 TCGA-PRAD
|
| 415 |
+
DO36283 f6381367-142c-45d0-92b3-c1727d1813ce TCGA-HC-7744 TCGA-PRAD
|
| 416 |
+
DO36285 73f875cb-bcfc-43ba-ab97-4e94caf836d4 TCGA-HC-7737 TCGA-PRAD
|
| 417 |
+
DO36345 6845076e-e940-4437-8618-81db4a447544 TCGA-G9-6365 TCGA-PRAD
|
| 418 |
+
DO36360 4fe004e5-49f9-4336-a690-6ebad512c813 TCGA-EJ-7791 TCGA-PRAD
|
| 419 |
+
DO36372 59207bcd-f127-481f-b576-8716da7d7d97 TCGA-G9-6336 TCGA-PRAD
|
| 420 |
+
DO36386 72802851-9340-4c5f-a90b-23b7afd7577a TCGA-AG-4008 TCGA-READ
|
| 421 |
+
DO36431 e2d395f8-bf13-4fe4-8031-683b0eedd875 TCGA-AG-3890 TCGA-READ
|
| 422 |
+
DO36459 1388bba1-02ba-40df-93e5-c3d58b7bbae0 TCGA-AG-4015 TCGA-READ
|
| 423 |
+
DO36556 8feb0e8f-d09b-437d-8651-0cdecfe776bf TCGA-F5-6814 TCGA-READ
|
| 424 |
+
DO36572 1586cb0a-081a-4364-9470-46f9d0f0167d TCGA-AG-3901 TCGA-READ
|
| 425 |
+
DO3662 5d1d00c6-fcae-479e-ae1e-de76efd41d98 TCGA-BH-A0BW TCGA-BRCA
|
| 426 |
+
DO36801 10290e91-9784-4f94-8daa-b5d9a8b0efb9 TCGA-AG-3593 TCGA-READ
|
| 427 |
+
DO36822 335d0351-3740-4847-9ff4-be2f078c70f7 TCGA-AF-2689 TCGA-READ
|
| 428 |
+
DO36945 0bb786ee-07cf-4853-9b49-ec95cd14e282 TCGA-AG-A032 TCGA-READ
|
| 429 |
+
DO36969 830fd359-abd4-4c99-89e4-2d6bb9ce5637 TCGA-AF-2691 TCGA-READ
|
| 430 |
+
DO37074 8e00e7e7-ffaf-44f0-91a7-172671f18e08 TCGA-AG-4007 TCGA-READ
|
| 431 |
+
DO37259 9af35239-ffb9-4d4f-889e-f743da271eef TCGA-AG-3896 TCGA-READ
|
| 432 |
+
DO37409 40e17bf8-6c19-4e5e-b60d-ad54f504a970 TCGA-DA-A1HY TCGA-SKCM
|
| 433 |
+
DO37418 b6691e3c-ee3f-49a2-8bd4-2086413e5d8d TCGA-DA-A3F8 TCGA-SKCM
|
| 434 |
+
DO37424 7b92fcd4-f9be-4b97-94a2-b2e8348aeaba TCGA-DA-A1I8 TCGA-SKCM
|
| 435 |
+
DO37425 b5e37b9b-6264-4cc8-9b44-1d8ac0692d6c TCGA-EE-A2M5 TCGA-SKCM
|
| 436 |
+
DO37426 ce28f65d-4ae8-4e63-815f-5b8d4389e092 TCGA-ER-A19J TCGA-SKCM
|
| 437 |
+
DO37427 85400bf1-23e0-4e0f-8df7-f0d108641aee TCGA-D3-A3MO TCGA-SKCM
|
| 438 |
+
DO37432 3429967f-4f77-4894-bf27-c4a22698ca92 TCGA-GN-A266 TCGA-SKCM
|
| 439 |
+
DO37435 d8eac750-9daf-4465-b672-9e31fd369057 TCGA-DA-A3F5 TCGA-SKCM
|
| 440 |
+
DO37439 81cedb9e-ee43-476b-9ed9-8d0fdb346065 TCGA-EE-A2A0 TCGA-SKCM
|
| 441 |
+
DO37440 d7ee2693-39a8-4e3a-b6f6-e4729800e34e TCGA-EB-A24D TCGA-SKCM
|
| 442 |
+
DO37445 8c54fcfd-999f-43c5-b31f-26d006f5fff3 TCGA-D3-A1Q1 TCGA-SKCM
|
| 443 |
+
DO37474 1404791b-86bf-4dfd-85b4-c8ff356f109c TCGA-FS-A1ZK TCGA-SKCM
|
| 444 |
+
DO37515 e36c9411-773b-4a8e-9b43-895b3cb044c7 TCGA-GN-A262 TCGA-SKCM
|
| 445 |
+
DO37527 76cc1d42-acec-46b3-9663-5e4de2550353 TCGA-GN-A264 TCGA-SKCM
|
| 446 |
+
DO37531 c061ae6c-7a02-47c3-aecd-a56487c01603 TCGA-EE-A2MI TCGA-SKCM
|
| 447 |
+
DO37587 5f6df69c-4ebf-4811-8b28-ad393dfbe1a3 TCGA-ER-A19L TCGA-SKCM
|
| 448 |
+
DO37599 a01a350d-64be-4af3-91c3-7d681c3db11d TCGA-EE-A185 TCGA-SKCM
|
| 449 |
+
DO37607 3582fb74-f43d-4fcd-8f22-bb1506fcff1c TCGA-DA-A3F3 TCGA-SKCM
|
| 450 |
+
DO37627 c2948fe9-5798-43bc-a743-511e94e417f8 TCGA-FS-A1ZD TCGA-SKCM
|
| 451 |
+
DO37659 210726bd-db19-48ae-9a3d-e6ac44480e85 TCGA-DA-A1HV TCGA-SKCM
|
| 452 |
+
DO37703 4cdb18f5-47bf-475f-b34c-fa4912515268 TCGA-FS-A1ZP TCGA-SKCM
|
| 453 |
+
DO37719 68f75ab5-9d77-40c5-a8ce-f56016bbe9e4 TCGA-DA-A1I0 TCGA-SKCM
|
| 454 |
+
DO37723 7ce09a09-074e-4f38-bed7-98ab34347fbe TCGA-FS-A1ZU TCGA-SKCM
|
| 455 |
+
DO37750 3409f118-1798-48d6-9556-12a5c77ebefb TCGA-ER-A19D TCGA-SKCM
|
| 456 |
+
DO37758 01ad975d-c2ed-4e4d-bd3b-c9512fc9073c TCGA-DA-A1I2 TCGA-SKCM
|
| 457 |
+
DO37782 fa099993-ef30-4eff-a8fb-aa41b5ea671a TCGA-GN-A26C TCGA-SKCM
|
| 458 |
+
DO37827 238111f9-a08b-49b4-9035-cc43026330e7 TCGA-D3-A1Q5 TCGA-SKCM
|
| 459 |
+
DO37861 5bc515c8-7727-4d69-98e9-31bbcb748550 TCGA-GN-A26A TCGA-SKCM
|
| 460 |
+
DO37896 be73fde2-d843-426b-9306-a02d3f2a754b TCGA-EE-A2GN TCGA-SKCM
|
| 461 |
+
DO38129 800d3a17-269f-4d97-94ff-8585f20038d8 TCGA-DA-A1HW TCGA-SKCM
|
| 462 |
+
DO38226 647dc54f-a51b-40ea-9d97-c93598c2af71 TCGA-HF-7136 TCGA-STAD
|
| 463 |
+
DO38229 af1086cc-b330-4ad6-ae1c-3b5c7259949a TCGA-BR-6564 TCGA-STAD
|
| 464 |
+
DO38238 e37cae9e-0656-4b89-b326-739a5cc859e0 TCGA-D7-6519 TCGA-STAD
|
| 465 |
+
DO38380 8fccff71-3710-4cb9-b8af-61707aebe5d8 TCGA-CG-4442 TCGA-STAD
|
| 466 |
+
DO38388 8d3e3d09-bc61-4d5e-b8d6-5aee6cb488ae TCGA-BR-7722 TCGA-STAD
|
| 467 |
+
DO3840 d6f7afc0-1558-43ad-acb1-2b5311ed2264 TCGA-GM-A2DF TCGA-BRCA
|
| 468 |
+
DO38404 27988b3e-cde1-4f4b-82a1-6d8ad08db8e9 TCGA-BR-6452 TCGA-STAD
|
| 469 |
+
DO38423 e2aaabd9-f4ba-4763-a28a-c14d46a4bc5d TCGA-D7-6822 TCGA-STAD
|
| 470 |
+
DO38535 39dd61a8-cd17-4df3-ae17-61e239eb00bd TCGA-CG-4474 TCGA-STAD
|
| 471 |
+
DO38547 35496e4e-598f-4ffc-b11e-8edaeb676079 TCGA-D7-6528 TCGA-STAD
|
| 472 |
+
DO38571 8b5746f9-dbee-40bd-9141-1081960cf286 TCGA-D7-5579 TCGA-STAD
|
| 473 |
+
DO38603 53086e20-a727-40d6-b42d-8030109b5130 TCGA-D7-6815 TCGA-STAD
|
| 474 |
+
DO38635 cb622cd6-776d-4681-8a67-4addd61c66b8 TCGA-D7-6527 TCGA-STAD
|
| 475 |
+
DO38655 1ca3c5e0-32b0-4467-8ec0-ca212e35d2b3 TCGA-BR-6456 TCGA-STAD
|
| 476 |
+
DO38663 e0916597-e489-45ef-a8f7-653a3c6591ef TCGA-BR-4280 TCGA-STAD
|
| 477 |
+
DO38671 8a24f1a3-4bab-4591-9676-7bee01d096c1 TCGA-BR-A4J4 TCGA-STAD
|
| 478 |
+
DO38731 283776cd-32e8-4a79-bed1-1411c9d3a9e0 TCGA-F1-6875 TCGA-STAD
|
| 479 |
+
DO3874 60df7543-6da5-4c75-943b-5800c1e08234 TCGA-E2-A14X TCGA-BRCA
|
| 480 |
+
DO38775 c991697f-77f9-412a-9bd1-0e47f7387cb2 TCGA-CD-5799 TCGA-STAD
|
| 481 |
+
DO38779 8cb0144b-be6b-40a1-86a2-708f96d9b615 TCGA-CG-4443 TCGA-STAD
|
| 482 |
+
DO38791 956017ba-5e4b-42c7-b93d-42316874ba93 TCGA-BR-4255 TCGA-STAD
|
| 483 |
+
DO38807 2d291fa3-f0fd-4bc1-91a6-783863714190 TCGA-EQ-5647 TCGA-STAD
|
| 484 |
+
DO38831 87c217d4-66f4-46ca-8244-7856ce658fd3 TCGA-IN-7806 TCGA-STAD
|
| 485 |
+
DO38847 0b4e8d73-459d-4930-9d1a-abb8d53e1844 TCGA-CG-5724 TCGA-STAD
|
| 486 |
+
DO38871 0aecac64-5982-4d76-8f31-958f6a00951d TCGA-CD-5802 TCGA-STAD
|
| 487 |
+
DO38899 4fc8e011-4433-4537-b03f-457a3a70240f TCGA-EL-A4K6 TCGA-THCA
|
| 488 |
+
DO38901 a09d0be0-fd41-4b30-b488-9a9f2abef8e7 TCGA-EL-A3TB TCGA-THCA
|
| 489 |
+
DO38903 fb83f7d7-2182-4fb7-8e3e-8ad7f1feac72 TCGA-EL-A3CV TCGA-THCA
|
| 490 |
+
DO38911 6abc861a-376d-446b-acee-fb0f03a82c09 TCGA-EM-A22O TCGA-THCA
|
| 491 |
+
DO38946 c31b3adf-3fc6-4a72-83b0-bbc0f295b5e7 TCGA-DE-A0Y3 TCGA-THCA
|
| 492 |
+
DO38966 e35b2928-8784-45d0-a71a-a2df161542fa TCGA-DJ-A2Q1 TCGA-THCA
|
| 493 |
+
DO38978 77bb90f0-e923-4e28-bb88-4d6b420e0b5c TCGA-DE-A2OL TCGA-THCA
|
| 494 |
+
DO39016 8d54a2ed-03f1-4a23-bdd2-f3395f5d3716 TCGA-EL-A3H1 TCGA-THCA
|
| 495 |
+
DO39020 b0cb6ed7-3d9f-4d5e-8df5-edcc921c43b4 TCGA-FY-A2QD TCGA-THCA
|
| 496 |
+
DO39022 f9cf605f-f287-4eba-8e8f-8ba47e14ecc4 TCGA-DJ-A2Q8 TCGA-THCA
|
| 497 |
+
DO39046 2f59f12d-ca0c-4ac6-9718-98a6d455af46 TCGA-EM-A2CP TCGA-THCA
|
| 498 |
+
DO39070 bed1fb3c-4459-460e-8190-29211857fa73 TCGA-BJ-A28V TCGA-THCA
|
| 499 |
+
DO39072 7629f864-a009-4aa9-bf7e-2d99e00d294e TCGA-ET-A4KN TCGA-THCA
|
| 500 |
+
DO39084 e70feafd-1390-4f4c-a635-da65c1a7daeb TCGA-FE-A233 TCGA-THCA
|
| 501 |
+
DO39127 785803bb-42a8-4afb-b7b4-58c114894408 TCGA-FK-A3S3 TCGA-THCA
|
| 502 |
+
DO39210 63b01b43-a68a-4046-89b0-736ba4d08504 TCGA-FK-A3SD TCGA-THCA
|
| 503 |
+
DO39216 6ee95dc8-2bac-4691-afa2-8222e07506ad TCGA-DJ-A3US TCGA-THCA
|
| 504 |
+
DO39306 f9ceffc0-d544-418d-b4a9-bd3c84e37026 TCGA-BJ-A0Z2 TCGA-THCA
|
| 505 |
+
DO39363 dc4d8468-6f34-40cb-a826-8d582cd86fde TCGA-BJ-A0ZB TCGA-THCA
|
| 506 |
+
DO39481 ebbd54ad-7b97-4081-ad11-7f7f3ce74eef TCGA-E8-A418 TCGA-THCA
|
| 507 |
+
DO39534 fef9c64f-5959-4da0-aaa2-66b56fc7b4c3 TCGA-EM-A3FQ TCGA-THCA
|
| 508 |
+
DO3958 084c6aec-94f7-4090-8f3f-59fa9e89721a TCGA-A2-A0D1 TCGA-BRCA
|
| 509 |
+
DO39610 7746be87-7ad0-48a7-84fb-08a81b220bdb TCGA-EL-A3MY TCGA-THCA
|
| 510 |
+
DO39635 8a233b01-2959-467f-8950-02be16309156 TCGA-EL-A3T9 TCGA-THCA
|
| 511 |
+
DO39684 55e03ffc-f006-4ec8-8e5d-a738ff091f8a TCGA-EM-A2OV TCGA-THCA
|
| 512 |
+
DO39798 afb00cd4-e5da-4251-86fb-532c3f65324c TCGA-FK-A3SE TCGA-THCA
|
| 513 |
+
DO39841 3e787a0e-b5d2-401e-b1e5-077376090b3a TCGA-DJ-A13R TCGA-THCA
|
| 514 |
+
DO39847 78d14f86-896a-4f98-9274-b2e8e387add9 TCGA-FE-A22Z TCGA-THCA
|
| 515 |
+
DO39886 4aed5116-d27b-4911-b310-05c2248b861f TCGA-EM-A2CN TCGA-THCA
|
| 516 |
+
DO39910 085ffcdc-8600-42e2-852a-506465f716a2 TCGA-DE-A3KN TCGA-THCA
|
| 517 |
+
DO40069 acb80b76-2394-4250-8687-60e40e16e83b TCGA-DJ-A4UT TCGA-THCA
|
| 518 |
+
DO40147 e1ff7694-64e1-47ed-b0d8-34acfb4ffef1 TCGA-L6-A4ET TCGA-THCA
|
| 519 |
+
DO40167 2501ee46-8d38-448b-8765-e9c9706cbbe8 TCGA-BJ-A191 TCGA-THCA
|
| 520 |
+
DO4018 ef4cbd38-bc79-4d60-a715-647edd2ebe9e TCGA-E2-A1LG TCGA-BRCA
|
| 521 |
+
DO40193 39d90499-8549-4da5-b391-c421f18facf1 TCGA-FE-A3PD TCGA-THCA
|
| 522 |
+
DO40274 e4b08e66-0772-4956-8670-e5c6553eecbe TCGA-DJ-A1QL TCGA-THCA
|
| 523 |
+
DO40405 a00da907-e527-45f9-9afb-2fad2c7b0940 TCGA-DJ-A2Q2 TCGA-THCA
|
| 524 |
+
DO40484 e8f56d0f-eee4-4def-a43a-dec91f4382a1 TCGA-EM-A3AQ TCGA-THCA
|
| 525 |
+
DO40496 b46d88e9-8477-4900-8da9-5e7dbded0653 TCGA-ET-A3DV TCGA-THCA
|
| 526 |
+
DO40735 934a58af-0ffc-487e-9e95-fa7643b7d5cb TCGA-EL-A3CX TCGA-THCA
|
| 527 |
+
DO40831 24118b8f-59fe-482a-aed3-7b1bde1571b7 TCGA-B5-A0K8 TCGA-UCEC
|
| 528 |
+
DO40930 95e6d327-9ab7-45de-9527-f5634b3995c3 TCGA-AX-A06B TCGA-UCEC
|
| 529 |
+
DO41056 a8614c49-c7f1-4de9-bd2f-4764b094d604 TCGA-A5-A0GG TCGA-UCEC
|
| 530 |
+
DO41157 c88e3901-f3ee-436a-ba80-143c8eab7d69 TCGA-AX-A2H5 TCGA-UCEC
|
| 531 |
+
DO41217 d39df936-4bc6-404d-9e1d-f6416cc5a34c TCGA-AJ-A23M TCGA-UCEC
|
| 532 |
+
DO41343 de4e13c8-5ad0-46d2-ae54-b9ca22b939d8 TCGA-EO-A1Y8 TCGA-UCEC
|
| 533 |
+
DO4138 5dd423e8-feaa-4568-a750-500948c41d6c TCGA-B6-A0I1 TCGA-BRCA
|
| 534 |
+
DO41398 9205c164-7975-421a-90c3-edfe8def595c TCGA-E6-A1LZ TCGA-UCEC
|
| 535 |
+
DO41446 d17668c4-5d66-4e16-8b70-b54616581aae TCGA-AP-A0LL TCGA-UCEC
|
| 536 |
+
DO4155 d3d545b3-457f-4389-821f-704cb24aff7f TCGA-AR-A256 TCGA-BRCA
|
| 537 |
+
DO41716 8d9e4917-334b-4c76-aee1-1e22be772db0 TCGA-BK-A139 TCGA-UCEC
|
| 538 |
+
DO41911 944a102f-3475-45aa-9e30-4b92d1449f00 TCGA-DI-A1NN TCGA-UCEC
|
| 539 |
+
DO41951 f7e06a10-74ca-4f52-a944-b3f22c016998 TCGA-AP-A05D TCGA-UCEC
|
| 540 |
+
DO42169 9aa8c2b4-db32-4899-bf0c-d578cb175e90 TCGA-AP-A0L8 TCGA-UCEC
|
| 541 |
+
DO42209 13f31bcb-befd-4ed2-adc0-dc9e9ce52b75 TCGA-BS-A0TG TCGA-UCEC
|
| 542 |
+
DO42304 45d58088-d3f4-4eb0-b75c-bcb98c0981dc TCGA-A5-A0G9 TCGA-UCEC
|
| 543 |
+
DO42400 7b7d2d2b-abe6-4f32-add0-3715096995d1 TCGA-BS-A0TC TCGA-UCEC
|
| 544 |
+
DO42432 ffaa98a0-2b69-46dc-aee5-c5c3f2abbc38 TCGA-BS-A0TD TCGA-UCEC
|
| 545 |
+
DO42464 66e305ca-8a16-44f0-b837-85e529b7ad55 TCGA-BG-A18C TCGA-UCEC
|
| 546 |
+
DO42536 f8ac5fd2-11f2-4b3a-a3a6-ff1a6d8e1cae TCGA-EY-A1GW TCGA-UCEC
|
| 547 |
+
DO42937 5c22c8d6-5805-455c-8e1c-e6b8006738e4 TCGA-D1-A17K TCGA-UCEC
|
| 548 |
+
DO43074 16e69011-c295-479f-b521-86e66fba498d TCGA-D1-A16G TCGA-UCEC
|
| 549 |
+
DO43162 c10b2885-70ea-4d32-bc48-8b85530df702 TCGA-EY-A1GS TCGA-UCEC
|
| 550 |
+
DO43210 94817e78-78f5-4556-9394-610df65605e2 TCGA-A5-A0GJ TCGA-UCEC
|
| 551 |
+
DO43314 79ecc142-8f6e-4260-a7ce-c8e2a6212c35 TCGA-B5-A0K3 TCGA-UCEC
|
| 552 |
+
DO43338 bad34c92-1e64-4187-a0b4-061105fd13ba TCGA-AX-A1C8 TCGA-UCEC
|
| 553 |
+
DO43514 d2aa7d70-a25f-4a9e-9d52-37e372461824 TCGA-B5-A11G TCGA-UCEC
|
| 554 |
+
DO4359 5ed024e8-d05e-4c65-9441-eda9930ccc82 TCGA-A7-A26G TCGA-BRCA
|
| 555 |
+
DO43739 5ffa2ec6-2d94-4b09-8fb9-3591cf38fa4f TCGA-BS-A0U9 TCGA-UCEC
|
| 556 |
+
DO43811 e56491af-718f-4ca1-b3cd-0e5b3154697b TCGA-D1-A1NU TCGA-UCEC
|
| 557 |
+
DO43859 5d2f1a0f-a01b-48c2-b749-1ca710aa2bb4 TCGA-B5-A1MY TCGA-UCEC
|
| 558 |
+
DO44086 490eabca-4575-4bd6-9604-6b6e23d069ce TCGA-NH-A50T TCGA-COAD
|
| 559 |
+
DO44087 857a5889-19e7-4208-8362-f1377c408037 TCGA-A6-A567 TCGA-COAD
|
| 560 |
+
DO44088 c83d38fc-e011-4f75-a100-96513611f3e9 TCGA-QG-A5YV TCGA-COAD
|
| 561 |
+
DO44089 e8ec26bd-c88d-42b9-ac8a-ab121b8140f8 TCGA-AD-A5EK TCGA-COAD
|
| 562 |
+
DO44091 d8e9ea52-ff28-4626-baa3-71350fd53ddf TCGA-AY-A54L TCGA-COAD
|
| 563 |
+
DO44092 790266bc-c252-43f4-85d1-1b06ee529cf8 TCGA-A6-A566 TCGA-COAD
|
| 564 |
+
DO44093 300fff5a-0f61-402d-b845-3938ba7b1294 TCGA-QG-A5YX TCGA-COAD
|
| 565 |
+
DO44094 d3b7ada6-5395-4212-a921-8e97c92b605d TCGA-NH-A50V TCGA-COAD
|
| 566 |
+
DO44095 74a0264d-1d31-430d-9a88-e7334c8aa96c TCGA-A6-A56B TCGA-COAD
|
| 567 |
+
DO44097 cb664eef-be6f-44ef-aa90-6f6da0e6440a TCGA-BT-A3PJ TCGA-BLCA
|
| 568 |
+
DO44103 53886143-c1c6-40e9-88e6-e4e5e0271fc8 TCGA-A2-A3XX TCGA-BRCA
|
| 569 |
+
DO44111 65cac997-4d39-4501-85ec-4fcb328a8eb5 TCGA-GM-A3XL TCGA-BRCA
|
| 570 |
+
DO44273 174850b4-5ec2-462b-a890-89bd1716b3c2 TCGA-A2-A3Y0 TCGA-BRCA
|
| 571 |
+
DO44285 14c65927-b9dd-4ef6-9917-db7bc431ff2f TCGA-C5-A0TN TCGA-CESC
|
| 572 |
+
DO44462 1db41963-a520-47f0-828c-ed5c626507b1 TCGA-IA-A40Y TCGA-KIRP
|
| 573 |
+
DO44465 8b3c068d-5090-4030-8e7e-39da005dee79 TCGA-A4-A4ZT TCGA-KIRP
|
| 574 |
+
DO44469 925ae8ff-3200-4564-be76-94db0984b027 TCGA-A4-A48D TCGA-KIRP
|
| 575 |
+
DO44478 0c151271-2cf9-43f4-9e9b-fe927de43131 TCGA-IA-A40X TCGA-KIRP
|
| 576 |
+
DO44496 b809c2f2-185c-496a-a1d4-589294443a1d TCGA-B9-A44B TCGA-KIRP
|
| 577 |
+
DO44515 36b46540-472f-4a4d-a801-b8b6ae660dc9 TCGA-HE-A5NF TCGA-KIRP
|
| 578 |
+
DO44517 d80f601b-1064-4ff0-9c51-7f6afb22d1f5 TCGA-A4-A57E TCGA-KIRP
|
| 579 |
+
DO44520 2c084454-18e9-4837-9bba-7e91a73a48e0 TCGA-GL-A4EM TCGA-KIRP
|
| 580 |
+
DO44525 78412f82-cc55-43d5-96ca-1d02cf957725 TCGA-GL-A59R TCGA-KIRP
|
| 581 |
+
DO4473 5580b21a-2cdb-4777-ad79-6e06654144f5 TCGA-E2-A15H TCGA-BRCA
|
| 582 |
+
DO44872 c95ed407-bcc2-44aa-bc59-bf7549f2d665 TCGA-FV-A23B TCGA-LIHC
|
| 583 |
+
DO44875 c4c4820b-6f4c-4cdd-b233-4d22f5933fd9 TCGA-DD-A3A6 TCGA-LIHC
|
| 584 |
+
DO44877 65e430e1-0c9a-4045-83a9-653b7cff811d TCGA-FV-A4ZQ TCGA-LIHC
|
| 585 |
+
DO44883 69d6881b-9e9a-407b-8b02-bad5fc2455bf TCGA-DD-A3A8 TCGA-LIHC
|
| 586 |
+
DO44885 89791f8f-dc06-4a04-b166-afbfd6cf53de TCGA-DD-A4NG TCGA-LIHC
|
| 587 |
+
DO44888 457cca7c-a8b4-414e-a3c0-7fccf0ee2034 TCGA-DD-A3A9 TCGA-LIHC
|
| 588 |
+
DO44889 bce25281-502e-4599-9679-32dc8462ffb1 TCGA-DD-A4NE TCGA-LIHC
|
| 589 |
+
DO44890 bd48fa5e-0152-46f0-a4fa-8cf723ff2dcd TCGA-FV-A495 TCGA-LIHC
|
| 590 |
+
DO44919 d680df09-368e-42b5-b540-45c41ed31042 TCGA-FV-A2QQ TCGA-LIHC
|
| 591 |
+
DO44930 bee9d6c8-948f-4b76-97ca-b985064249ef TCGA-DD-A4NA TCGA-LIHC
|
| 592 |
+
DO44966 8afa3140-dcbd-44c0-ab13-4ada6a0444d4 TCGA-FV-A496 TCGA-LIHC
|
| 593 |
+
DO44974 56403463-1727-4c23-a709-de5f168c4073 TCGA-ED-A459 TCGA-LIHC
|
| 594 |
+
DO44978 9a56d8a8-dadd-442e-bc09-732cd619aef7 TCGA-G3-A5SL TCGA-LIHC
|
| 595 |
+
DO44982 614c0ecf-6a84-4c86-8706-79be5bb00991 TCGA-DD-A3A7 TCGA-LIHC
|
| 596 |
+
DO44986 c0bf9278-9cbb-4361-b4d8-ab172b67e276 TCGA-DD-A4ND TCGA-LIHC
|
| 597 |
+
DO44994 17833039-0d7e-47f0-90e6-8cabae94c124 TCGA-FV-A3R2 TCGA-LIHC
|
| 598 |
+
DO45009 a8c2aa08-9156-4845-ad77-5e060858c757 TCGA-FV-A3I0 TCGA-LIHC
|
| 599 |
+
DO4635 790ff5db-b7f3-4946-aaed-305f66b1dd6a TCGA-BH-A0AV TCGA-BRCA
|
| 600 |
+
DO46813 e2b33861-2247-413f-b2e5-9be3b604666c TCGA-HC-7233 TCGA-PRAD
|
| 601 |
+
DO4695 88db1340-e4bf-451a-87c0-6e9168296f5e TCGA-EW-A1PB TCGA-BRCA
|
| 602 |
+
DO4719 3fc3755d-a3f8-4e2c-813f-ff124f2a75c1 TCGA-A2-A259 TCGA-BRCA
|
| 603 |
+
DO472 9c274536-3ca1-4f0e-93a8-1688074d862f TCGA-DK-A1A6 TCGA-BLCA
|
| 604 |
+
DO47255 905ea29f-588a-497b-9f06-2eef3bdc9442 TCGA-EE-A29B TCGA-SKCM
|
| 605 |
+
DO47268 296f44f9-3ecb-4b40-ae51-c3a06e3fbe00 TCGA-EE-A3JI TCGA-SKCM
|
| 606 |
+
DO47331 d6283ab0-9019-4ad2-93aa-2e6b39cd9641 TCGA-EE-A3J5 TCGA-SKCM
|
| 607 |
+
DO47451 d32a4219-4cca-470f-b6b3-11447d9f5e0b TCGA-ER-A2NF TCGA-SKCM
|
| 608 |
+
DO47542 3fea3f02-2db3-44e0-984e-c61130d92c9b TCGA-ER-A19E TCGA-SKCM
|
| 609 |
+
DO4766 f0787165-6f58-4d67-b510-928eea2c4882 TCGA-E2-A109 TCGA-BRCA
|
| 610 |
+
DO477 904a8757-e0c5-41ef-b583-c8f170caaac1 TCGA-DK-A1A5 TCGA-BLCA
|
| 611 |
+
DO47737 d5efcde2-21e6-4071-9aa4-3c5688a45b3f TCGA-BR-8682 TCGA-STAD
|
| 612 |
+
DO47747 30a82e9f-2a2d-4a66-bdb1-26b881c21d01 TCGA-HU-A4G6 TCGA-STAD
|
| 613 |
+
DO47753 5b7e557a-6aee-4077-b3cd-c96179e643ae TCGA-CD-8529 TCGA-STAD
|
| 614 |
+
DO47785 fe45eaee-ff0c-4d52-b18c-04dc2d64d3b4 TCGA-HU-A4H0 TCGA-STAD
|
| 615 |
+
DO47817 aef8d98f-7a1c-47f3-b5a7-a3cb299c70de TCGA-BR-8381 TCGA-STAD
|
| 616 |
+
DO47841 c6316fb5-99a7-4adf-832f-dce44ddcd3aa TCGA-HU-8608 TCGA-STAD
|
| 617 |
+
DO47859 60879669-96c2-4710-82e0-0ae9d0e5b242 TCGA-BR-8373 TCGA-STAD
|
| 618 |
+
DO47863 b8a1c8c7-3945-4d84-8f58-3a1a1eb10730 TCGA-BR-8486 TCGA-STAD
|
| 619 |
+
DO479 1e308b12-0590-4dae-94d0-a539fcf25df7 TCGA-CF-A3MF TCGA-BLCA
|
| 620 |
+
DO47911 337819d2-5281-4b41-9583-a476924bc837 TCGA-BR-8690 TCGA-STAD
|
| 621 |
+
DO47929 4e9d13da-438d-4b5e-8e86-67432b5e9471 TCGA-HU-8245 TCGA-STAD
|
| 622 |
+
DO4796 38a8b734-9acc-42f9-b5b7-e51b0dfc6504 TCGA-A8-A092 TCGA-BRCA
|
| 623 |
+
DO47997 2b4f014f-74e9-4dda-a1ad-abcbd124fc44 TCGA-D7-A4YX TCGA-STAD
|
| 624 |
+
DO48040 1e1cdf72-820f-48a4-8fb6-89575d07cde9 TCGA-D7-8570 TCGA-STAD
|
| 625 |
+
DO48065 cfdd2155-57a9-499c-92e1-fb82b577b050 TCGA-EM-A3FL TCGA-THCA
|
| 626 |
+
DO48084 8cfb88f6-7b37-4f1e-882b-3a368b604167 TCGA-BJ-A45K TCGA-THCA
|
| 627 |
+
DO48103 2b84cfbc-7cf6-4cb1-a1e3-ba85dafb89f4 TCGA-EM-A3FR TCGA-THCA
|
| 628 |
+
DO48159 0c50a2c2-1d4c-45ca-9127-f96d4db18daf TCGA-EM-A3AL TCGA-THCA
|
| 629 |
+
DO483 3ed614e7-f356-4d87-985b-d3bbbae3bb40 TCGA-DK-A1A7 TCGA-BLCA
|
| 630 |
+
DO48474 a40beabe-496e-4324-a1d1-5a7d8b002aab TCGA-MH-A560 TCGA-KIRP
|
| 631 |
+
DO48482 f0f7e57f-1e9d-4f24-a685-a0811aacbbed TCGA-MH-A55Z TCGA-KIRP
|
| 632 |
+
DO48495 45bdcfd6-1e3f-4be8-b843-ae949e8e43eb TCGA-MH-A562 TCGA-KIRP
|
| 633 |
+
DO48505 c52abac9-07da-4b3d-bf8a-2161cacf9682 TCGA-ED-A4XI TCGA-LIHC
|
| 634 |
+
DO48509 e6e6a773-b155-45a9-989f-75c12ce1702a TCGA-MH-A561 TCGA-KIRP
|
| 635 |
+
DO48516 2030e10b-a4c2-460f-9e29-90c8a76ea05e TCGA-MH-A55W TCGA-KIRP
|
| 636 |
+
DO48518 6c79f64b-63fe-46f5-bf7b-77208231b0f2 TCGA-DD-A1EG TCGA-LIHC
|
| 637 |
+
DO48552 1983fade-44f1-41e0-8983-f2ce8d649655 TCGA-DD-A4NB TCGA-LIHC
|
| 638 |
+
DO48977 0809ba8b-4ab6-4f43-934c-c1ccbc014a7e TCGA-EK-A2PK TCGA-CESC
|
| 639 |
+
DO48987 00bca18c-b3d4-45a3-8f19-034cc40449a4 TCGA-C5-A2LV TCGA-CESC
|
| 640 |
+
DO48991 262311a0-7a10-4934-8fba-11ee581ad738 TCGA-C5-A1BF TCGA-CESC
|
| 641 |
+
DO48993 110cd100-5730-4873-9fab-2ae6c8732062 TCGA-C5-A1M8 TCGA-CESC
|
| 642 |
+
DO49009 7941d1c7-5319-4e8d-a15f-60b61cb68a9b TCGA-C5-A1BJ TCGA-CESC
|
| 643 |
+
DO49014 a3be445a-a592-4073-815b-47ae41f9c4a0 TCGA-C5-A1M9 TCGA-CESC
|
| 644 |
+
DO496 5d54c742-5a8e-4c40-8d62-95e75e210ab8 TCGA-GD-A2C5 TCGA-BLCA
|
| 645 |
+
DO49659 c7f1b389-aec7-4993-8afb-b7a56064f8fc TCGA-BW-A5NQ TCGA-LIHC
|
| 646 |
+
DO49695 2d00f09f-0074-46c8-b357-b5f33567762a TCGA-HP-A5MZ TCGA-LIHC
|
| 647 |
+
DO49728 b06cdf61-61c9-4035-a55d-1ef21dc21069 TCGA-AL-A5DJ TCGA-KIRP
|
| 648 |
+
DO49736 072507cf-1249-4b28-86d4-3ab97822a74c TCGA-B1-A47M TCGA-KIRP
|
| 649 |
+
DO49738 f1ec5a7b-0973-4c38-9908-862196df9541 TCGA-B1-A47N TCGA-KIRP
|
| 650 |
+
DO49768 8d28f586-3de5-4033-a9fd-4f67be406fb4 TCGA-HE-A5NJ TCGA-KIRP
|
| 651 |
+
DO498 493a4ff2-37a5-4b79-928d-83dbfe534556 TCGA-DK-A1AE TCGA-BLCA
|
| 652 |
+
DO49815 da5b0169-e4f4-4674-b348-220af03ff4b5 TCGA-HC-8258 TCGA-PRAD
|
| 653 |
+
DO49880 fb66b7f5-22ca-4d4d-97c2-30e6edfff781 TCGA-C5-A2LT TCGA-CESC
|
| 654 |
+
DO49954 61eb08d0-ea01-45e7-884b-ac3d6070172a TCGA-EK-A2RL TCGA-CESC
|
| 655 |
+
DO49956 c13215d9-26a8-4c8b-b4fe-5da6aa3222af TCGA-EK-A2RM TCGA-CESC
|
| 656 |
+
DO5046 f05d314c-5ec5-4e2a-b785-9a702716f111 TCGA-E2-A156 TCGA-BRCA
|
| 657 |
+
DO51251 b85b14d7-3b5a-4800-af12-622ec03b9fe5 TCGA-ER-A19T TCGA-SKCM
|
| 658 |
+
DO51551 6a6c1569-1045-4ed3-acdb-5c45e47614ca TCGA-B1-A47O TCGA-KIRP
|
| 659 |
+
DO51592 374f614a-bcd1-4283-ac6f-fc27ddc79987 TCGA-C5-A1ML TCGA-CESC
|
| 660 |
+
DO51594 7c93642d-7e05-40f8-b1ef-a014edcfba42 TCGA-DG-A2KJ TCGA-CESC
|
| 661 |
+
DO5200 233b02f3-c4f0-4a67-9db5-e68d5cdaccb6 TCGA-BH-A18U TCGA-BRCA
|
| 662 |
+
DO52029 bd75d8ee-916b-4abc-bf33-bce6a4217076 TCGA-BA-A4IH TCGA-HNSC
|
| 663 |
+
DO52171 ee63c44b-91f4-4004-8dea-192eab527036 TCGA-BW-A5NO TCGA-LIHC
|
| 664 |
+
DO52172 56a82a56-0241-4d3d-9de2-696b0c36df91 TCGA-BW-A5NP TCGA-LIHC
|
| 665 |
+
DO522 cede5274-279d-4021-8aca-5ecc4bf94d66 TCGA-H4-A2HQ TCGA-BLCA
|
| 666 |
+
DO52307 1987b453-f97d-45c7-9c89-b9a33313d645 TCGA-PD-A5DF TCGA-LIHC
|
| 667 |
+
DO5249 733a2a0f-b37a-4b81-b49e-3c0f30d1eb37 TCGA-A2-A04X TCGA-BRCA
|
| 668 |
+
DO5347 75113445-d2d6-44a0-866c-c9175e6d214b TCGA-A2-A0D4 TCGA-BRCA
|
| 669 |
+
DO536 556fcbc8-172a-4af1-8822-ae036e8d68e8 TCGA-BL-A13J TCGA-BLCA
|
| 670 |
+
DO548 56d8a1b8-1268-4362-a7f1-77b76fbf1b59 TCGA-DK-A1AA TCGA-BLCA
|
| 671 |
+
DO555 178b28cd-99c3-48dc-8d09-1ef71b4cee80 TCGA-DK-A3IL TCGA-BLCA
|
| 672 |
+
DO561 797a71ee-372d-41e5-aeee-5ab3c4661110 TCGA-BT-A2LA TCGA-BLCA
|
| 673 |
+
DO5626 44bec761-b603-49c0-8634-f6bfe0319bb1 TCGA-AO-A0JM TCGA-BRCA
|
| 674 |
+
DO5661 b97bf89a-7a85-4eef-ae7e-f787aead1f0a TCGA-BH-A1FC TCGA-BRCA
|
| 675 |
+
DO5745 9f6be944-83de-42ab-8738-f0022f475e61 TCGA-GI-A2C9 TCGA-BRCA
|
| 676 |
+
DO575 8c1dd7f7-b74a-4fa2-b6a7-86f0348d2567 TCGA-FD-A3N6 TCGA-BLCA
|
| 677 |
+
DO5808 9435447e-d65f-408b-863b-6576b1d652dd TCGA-AO-A03N TCGA-BRCA
|
| 678 |
+
DO6144 5d8ed961-a012-4b6b-bffb-9468d3953646 TCGA-EW-A1J5 TCGA-BRCA
|
| 679 |
+
DO6231 f45210d3-9e66-4f5e-bef1-5ee5547cc893 TCGA-EW-A1PC TCGA-BRCA
|
| 680 |
+
DO6249 941f75a1-fea4-4539-ba69-60bb11608f6d TCGA-C5-A1MI TCGA-CESC
|
| 681 |
+
DO6252 8a4da4f0-30f2-497e-8a7c-988fd6b813cb TCGA-C5-A1BN TCGA-CESC
|
| 682 |
+
DO6255 6b74c07a-9932-4a80-8296-9db08d0c6964 TCGA-EX-A1H5 TCGA-CESC
|
| 683 |
+
DO6314 e733f289-a0ec-478c-aad9-1378ad26ddfa TCGA-C5-A2LY TCGA-CESC
|
| 684 |
+
DO6332 dfd30f94-19e3-4838-95df-14a3763419af TCGA-DS-A0VL TCGA-CESC
|
| 685 |
+
DO639 a658becf-3934-49f2-9e74-291dca61d4bb TCGA-FD-A3N5 TCGA-BLCA
|
| 686 |
+
DO669 ca554128-da9d-4f37-9560-ca083509e01d TCGA-C4-A0F7 TCGA-BLCA
|
| 687 |
+
DO675 f48937ed-e294-41f0-8872-7010391167a9 TCGA-BT-A20P TCGA-BLCA
|
| 688 |
+
DO689 24f21425-b001-4986-aedf-5b4dd851c6ad TCGA-BT-A20V TCGA-BLCA
|
| 689 |
+
DO695 419deaac-ea45-4bdd-9fa0-b5cd8429b44f TCGA-BT-A20Q TCGA-BLCA
|
| 690 |
+
DO720 cce62116-3dcd-400d-ada0-9839ca02466e TCGA-FT-A3EE TCGA-BLCA
|
| 691 |
+
DO7801 df5ab6cc-6f68-4b6b-95e2-954c6b57ba9c TCGA-CA-6718 TCGA-COAD
|
| 692 |
+
DO7831 a06c09a8-0951-49f4-a765-5147bbcae109 TCGA-AA-3664 TCGA-COAD
|
| 693 |
+
DO7990 fafd4576-0709-49e1-926f-ad1ee09907ec TCGA-A6-2683 TCGA-COAD
|
| 694 |
+
DO804 096b4f32-10c1-4737-a0dd-cae04c54ee33 TCGA-BT-A20T TCGA-BLCA
|
| 695 |
+
DO8047 dca004bf-9c14-45a1-b186-ab3759f6c7fe TCGA-AA-3685 TCGA-COAD
|
| 696 |
+
DO822 f8c0a69c-df53-4a0a-a30e-4424c0ce3dd5 TCGA-DK-A1AG TCGA-BLCA
|
| 697 |
+
DO8264 268c01b3-d2ce-44c0-a2fe-ea846a1253cc TCGA-CA-6717 TCGA-COAD
|
| 698 |
+
DO828 7fee9d6f-c0ea-43c2-89c2-66d965a077a8 TCGA-CF-A27C TCGA-BLCA
|
| 699 |
+
DO8360 5e7074a6-5011-4dc5-950c-41365dbc6d09 TCGA-AD-6964 TCGA-COAD
|
| 700 |
+
DO856 932bcdd4-5145-49e8-9a95-8a546396f572 TCGA-BT-A3PH TCGA-BLCA
|
| 701 |
+
DO8658 750770db-53cf-4021-a8c2-5e0ddb97fef5 TCGA-AA-3994 TCGA-COAD
|
| 702 |
+
DO8730 33e989b9-fac4-47d8-b429-269044ac772a TCGA-AA-3977 TCGA-COAD
|
| 703 |
+
DO8858 ca22edd4-eb7c-4677-84aa-6f0354459a19 TCGA-AA-A00R TCGA-COAD
|
| 704 |
+
DO8898 36eaf209-c332-4321-b6aa-51a3368ecd79 TCGA-AA-3555 TCGA-COAD
|
| 705 |
+
DO9034 5116e3b4-2bac-40f5-8046-b9c1783faaa5 TCGA-A6-3807 TCGA-COAD
|
| 706 |
+
DO9042 a512701f-88bb-4b05-8c94-fd285b9dd13e TCGA-AA-3666 TCGA-COAD
|
| 707 |
+
DO9074 ff1407c6-9174-4bae-a19b-d34ca71b898c TCGA-A6-2680 TCGA-COAD
|
| 708 |
+
DO9106 84e34cf2-b3bb-4a1a-8fb7-6c2c604c4008 TCGA-AA-A01S TCGA-COAD
|
| 709 |
+
DO9732 bfb07784-693b-4c25-874e-4ad6e04a5d46 TCGA-AA-3529 TCGA-COAD
|
| 710 |
+
DO9788 7d8eab0a-e6c8-4449-9ebf-50c41db94a06 TCGA-A6-2681 TCGA-COAD
|
| 711 |
+
DO9876 e457344d-76fb-46bf-b362-61a6e811d131 TCGA-AA-A00N TCGA-COAD
|
| 712 |
+
DO9940 df228c56-89ac-4122-93b3-6c0740c4ec09 TCGA-AA-3534 TCGA-COAD
|
hla_loh/escape_genes.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:116948dcec63d66c344a72a63183a09919cc62a3b60fe33e2c49e98ea948fc89
|
| 3 |
+
size 3626
|
hla_loh/gie_allele.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9b43ee1c870e053bec6bf29d223e5bb38df653729501edbb7fbbd5860b2e8de1
|
| 3 |
+
size 145724
|
hla_loh/gie_sample.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:10579459c2a274671a4f61d539492ce284737d4fc9ce71ceefcd77c506da4077
|
| 3 |
+
size 71045
|
hla_loh/harmonize.py
ADDED
|
@@ -0,0 +1,177 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
# 2026-09-16 -- harmonise the Martinez-Jimenez 2023 HLA-I immune-escape supplements into parquet.
|
| 3 |
+
#
|
| 4 |
+
# The three publisher workbooks are copyrighted Springer Nature supplementary material and are not
|
| 5 |
+
# ours to redistribute, so they live in the gitignored `raw/hla_loh/`. The *facts* they carry are
|
| 6 |
+
# not copyrightable, so this script writes neutral, long-format tables that do ship. Same call, and
|
| 7 |
+
# the same shape, as `icb_cohorts/`: harmonised output published, publisher workbook kept back.
|
| 8 |
+
#
|
| 9 |
+
# Re-run after refreshing raw/hla_loh/*.xlsx: python hla_loh/harmonize.py
|
| 10 |
+
#
|
| 11 |
+
# Source: Martinez-Jimenez F, Priestley P, Shale C, Baber J, Rozemuller E, Cuppen E. "Genetic immune
|
| 12 |
+
# escape landscape in primary and metastatic cancer." Nat Genet 55(5):820-831, 2023.
|
| 13 |
+
# doi:10.1038/s41588-023-01367-1 (verified against crossref, 2026-09-16).
|
| 14 |
+
# MOESM4 = Supplementary Tables 1-2, MOESM5 = Supplementary Data 1, MOESM6 = Supplementary Data 2.
|
| 15 |
+
|
| 16 |
+
from __future__ import annotations
|
| 17 |
+
|
| 18 |
+
import os
|
| 19 |
+
|
| 20 |
+
import openpyxl
|
| 21 |
+
import polars as pl
|
| 22 |
+
|
| 23 |
+
HERE = os.path.dirname(os.path.abspath(__file__))
|
| 24 |
+
RAW = os.path.join(os.path.dirname(HERE), "raw", "hla_loh")
|
| 25 |
+
SLOTS = ("A1", "A2", "B1", "B2", "C1", "C2")
|
| 26 |
+
CALLERS = ("LILAC", "xHLA", "polysolver")
|
| 27 |
+
TISSUE = {"n": "normal", "t": "tumour"}
|
| 28 |
+
|
| 29 |
+
|
| 30 |
+
def sheet(fn: str, name: str) -> tuple[list[str], list[tuple]]:
|
| 31 |
+
"""Header and non-empty data rows of one worksheet, values only."""
|
| 32 |
+
wb = openpyxl.load_workbook(os.path.join(RAW, fn), read_only=True, data_only=True)
|
| 33 |
+
ws = wb[name]
|
| 34 |
+
it = ws.iter_rows(values_only=True)
|
| 35 |
+
hdr = [str(h) for h in next(it) if h is not None]
|
| 36 |
+
rows = [r for r in it if r and r[0] is not None]
|
| 37 |
+
wb.close()
|
| 38 |
+
return hdr, rows
|
| 39 |
+
|
| 40 |
+
|
| 41 |
+
def _b(x):
|
| 42 |
+
"""openpyxl hands these back as the strings 'True'/'False'; 'None' and None are both missing."""
|
| 43 |
+
if x is None or x in ("None", ""):
|
| 44 |
+
return None
|
| 45 |
+
return str(x) == "True"
|
| 46 |
+
|
| 47 |
+
|
| 48 |
+
def _f(x):
|
| 49 |
+
if x is None or x in ("None", ""):
|
| 50 |
+
return None
|
| 51 |
+
try:
|
| 52 |
+
return float(x)
|
| 53 |
+
except (TypeError, ValueError):
|
| 54 |
+
return None
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
def _s(x):
|
| 58 |
+
return None if x is None or x == "None" else str(x)
|
| 59 |
+
|
| 60 |
+
|
| 61 |
+
def _allele(x):
|
| 62 |
+
"""The workbooks write `A*02:01`; every other table in this compendium -- and `mhcmatch` --
|
| 63 |
+
writes `HLA-A*02:01`. Two vocabularies for one molecule is the recurring hazard here, so the
|
| 64 |
+
published column carries the repo's spelling and SOURCES.md records the workbook's."""
|
| 65 |
+
a = _s(x)
|
| 66 |
+
return None if a is None else (a if a.startswith("HLA-") else f"HLA-{a}")
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
def _df(rows):
|
| 70 |
+
"""Scan every row before fixing a dtype. polars infers from the first 100 by default, and the
|
| 71 |
+
sparsest column here (`checkpoint_pathway_detail`) is null until row 250 -- inferred Null, then
|
| 72 |
+
a string arrives and the build fails. Cheap at this size, and it cannot silently mistype."""
|
| 73 |
+
return pl.DataFrame(rows, infer_schema_length=None)
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
def gie_tables():
|
| 77 |
+
"""Supplementary Data 2, sheet `GIE per sample`: 6,319 samples, 4,439 Hartwig + 1,880 PCAWG.
|
| 78 |
+
|
| 79 |
+
Split into one row per (sample, allele slot) and one row per sample, because the sheet is two
|
| 80 |
+
tables side by side: a six-slot allele record and a per-sample escape record. `*_MUT` is a
|
| 81 |
+
mutation COUNT (0.0/0.5/1.0/2.0), not a flag -- casting it to bool loses the 2.0 and the 0.5.
|
| 82 |
+
"""
|
| 83 |
+
hdr, rows = sheet("41588_2023_1367_MOESM6_ESM.xlsx", "GIE per sample")
|
| 84 |
+
i = {c: n for n, c in enumerate(hdr)}
|
| 85 |
+
g = lambda r, c: r[i[c]] if c in i else None # noqa: E731
|
| 86 |
+
|
| 87 |
+
allele = [
|
| 88 |
+
{"sample_id": _s(g(r, "sample_id_2")), "cohort": _s(g(r, "cohort")),
|
| 89 |
+
"cancer_type": _s(g(r, "cancer_type")), "cancer_type_code": _s(g(r, "cancer_type_code")),
|
| 90 |
+
"locus": f"HLA-{s[0]}", "slot": s, "allele": _allele(g(r, s)),
|
| 91 |
+
"copy_number": _f(g(r, f"{s}_CN")), "n_mutations": _f(g(r, f"{s}_MUT"))}
|
| 92 |
+
for r in rows for s in SLOTS]
|
| 93 |
+
|
| 94 |
+
flags = ("loh_lilac", "mut_hla_lilac", "systemic_app_pathway", "cd58_pathway",
|
| 95 |
+
"ifn_gamma_pathway", "checkpoint_pathway", "epigenetic_regulators_pathway",
|
| 96 |
+
"targeted_escape", "non_targeted_escape", "genetic_immune_escape")
|
| 97 |
+
# `copy_number_neutral_loh_hla` LOOKS like a flag beside the others and is not: it carries a
|
| 98 |
+
# comma-separated locus list (`HLA-A,HLA-C,HLA-B`). Read as bool it would have nulled every
|
| 99 |
+
# real value. The four LOH columns are all locus lists, kept verbatim as the source wrote them.
|
| 100 |
+
texts = ("copy_number_neutral_loh_hla", "loh_hfocal", "loh_focal", "loh_nonfocal",
|
| 101 |
+
"systemic_app_pathway_detail", "cd58_pathway_detail", "ifn_gamma_pathway_detail",
|
| 102 |
+
"checkpoint_pathway_detail", "epigenetic_regulators_pathway_detail")
|
| 103 |
+
sample = []
|
| 104 |
+
for r in rows:
|
| 105 |
+
row = {"sample_id": _s(g(r, "sample_id_2")), "cohort": _s(g(r, "cohort")),
|
| 106 |
+
"cancer_type": _s(g(r, "cancer_type")),
|
| 107 |
+
"cancer_type_code": _s(g(r, "cancer_type_code"))}
|
| 108 |
+
for loc in "ABC":
|
| 109 |
+
row[f"ploidy_minor_HLA-{loc}"] = _f(g(r, f"ploidy_minor_HLA-{loc}"))
|
| 110 |
+
row[f"ploidy_major_HLA-{loc}"] = _f(g(r, f"ploidy_major_HLA-{loc}"))
|
| 111 |
+
row[f"del_HLA-{loc}"] = _f(g(r, f"del_HLA-{loc}"))
|
| 112 |
+
row |= {c: _b(g(r, c)) for c in flags}
|
| 113 |
+
row |= {c: _s(g(r, c)) for c in texts}
|
| 114 |
+
sample.append(row)
|
| 115 |
+
return _df(allele), _df(sample)
|
| 116 |
+
|
| 117 |
+
|
| 118 |
+
def typing_callers():
|
| 119 |
+
"""Supplementary Data 1, sheets `Hartwig` and `PCAWG`: three callers x normal/tumour x six
|
| 120 |
+
slots, one column each. Long, so a caller comparison is a group_by rather than 36 column names."""
|
| 121 |
+
out = []
|
| 122 |
+
for sh in ("Hartwig", "PCAWG"):
|
| 123 |
+
hdr, rows = sheet("41588_2023_1367_MOESM5_ESM.xlsx", sh)
|
| 124 |
+
i = {c: n for n, c in enumerate(hdr)}
|
| 125 |
+
for r in rows:
|
| 126 |
+
sid, mut = _s(r[i["sampleId"]]), _b(r[i["is_mutated"]])
|
| 127 |
+
for caller in CALLERS:
|
| 128 |
+
for t, tname in TISSUE.items():
|
| 129 |
+
for s in SLOTS:
|
| 130 |
+
col = f"{caller}_{t}_{s}"
|
| 131 |
+
if col not in i:
|
| 132 |
+
continue
|
| 133 |
+
out.append({"sample_id": sid, "cohort": sh, "is_mutated": mut,
|
| 134 |
+
"caller": caller, "tissue": tname, "slot": s,
|
| 135 |
+
"locus": f"HLA-{s[0]}", "allele": _allele(r[i[col]])})
|
| 136 |
+
return _df(out)
|
| 137 |
+
|
| 138 |
+
|
| 139 |
+
def sample_metadata():
|
| 140 |
+
"""Supplementary Table 2: per-sample cohort, cancer type, treatment history and mutation load."""
|
| 141 |
+
hdr, rows = sheet("41588_2023_1367_MOESM4_ESM.xlsx", "Supp. Table 2 - sample metadata ")
|
| 142 |
+
num = {"ploidy", "sv_load", "smnv_load", "sbs_load", "dbs_load", "indel_load",
|
| 143 |
+
"sbs_load.clonal", "dbs_load.clonal", "indel_load.clonal",
|
| 144 |
+
"sbs_load.subclonal", "dbs_load.subclonal", "indel_load.subclonal"}
|
| 145 |
+
bul = {"blacklisted_gie", "is_selected", "has_lilac", "has_group_cohort",
|
| 146 |
+
"has_group_mut_signatures", "has_rna", "whole_genome_duplication", "had_radiotherapy",
|
| 147 |
+
"had_other_treatment", "had_chemotherapy", "had_hormone_therapy", "had_targeted_therapy",
|
| 148 |
+
"had_immunotherapy", "treatment_info_available"}
|
| 149 |
+
return _df([
|
| 150 |
+
{c: (_f(r[n]) if c in num else _b(r[n]) if c in bul else _s(r[n]))
|
| 151 |
+
for n, c in enumerate(hdr)} for r in rows])
|
| 152 |
+
|
| 153 |
+
|
| 154 |
+
def escape_genes():
|
| 155 |
+
"""Supplementary Table 1: the immune-escape gene panel the paper's calls are made over."""
|
| 156 |
+
hdr, rows = sheet("41588_2023_1367_MOESM4_ESM.xlsx", "Supp. Table 1 - genes")
|
| 157 |
+
keys = ["gene", "pathway_specific", "pathway_general", "references", "mutation_types",
|
| 158 |
+
"selected"]
|
| 159 |
+
return _df([
|
| 160 |
+
{k: (_f(r[n]) if k == "selected" else _s(r[n])) for n, k in enumerate(keys)}
|
| 161 |
+
for r in rows])
|
| 162 |
+
|
| 163 |
+
|
| 164 |
+
def main() -> int:
|
| 165 |
+
out = {"gie_allele.parquet": None, "gie_sample.parquet": None}
|
| 166 |
+
out["gie_allele.parquet"], out["gie_sample.parquet"] = gie_tables()
|
| 167 |
+
out["hla_typing_callers.parquet"] = typing_callers()
|
| 168 |
+
out["sample_metadata.parquet"] = sample_metadata()
|
| 169 |
+
out["escape_genes.parquet"] = escape_genes()
|
| 170 |
+
for name, df in out.items():
|
| 171 |
+
df.write_parquet(os.path.join(HERE, name), compression="zstd")
|
| 172 |
+
print(f"{name:28s} {df.height:>7,} rows x {df.width:>2} cols")
|
| 173 |
+
return 0
|
| 174 |
+
|
| 175 |
+
|
| 176 |
+
if __name__ == "__main__":
|
| 177 |
+
raise SystemExit(main())
|
hla_loh/hla_typing_callers.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d4881daeaa39eb998db01ec15579581e65f68066746932f2a7f99328f053bfb5
|
| 3 |
+
size 148451
|
hla_loh/perallele_tcga_loh.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4d507a72aabb96e5bdf30f9f481666554a2c4e7b93bf5b2162bb2dd440f6e95c
|
| 3 |
+
size 7649
|
hla_loh/sample_metadata.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c579034e9bc484225c8b68d0d97dfb72ac80b9a49526c6dd0248b59548d6ed6a
|
| 3 |
+
size 340067
|
homology/HOM_MouseHumanSequence.rpt.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9985b691a867025f24bda5af0f28a91b1aa2d28d3e06d4ee7d0db54fa217c12c
|
| 3 |
+
size 3243831
|
homology/SOURCES.md
ADDED
|
@@ -0,0 +1,50 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# `homology/` — sources
|
| 2 |
+
|
| 3 |
+
One file, and it exists to answer exactly one question: **given a human protein, which mouse gene
|
| 4 |
+
is its orthologue, spelled the way MGI spells it.**
|
| 5 |
+
|
| 6 |
+
## `HOM_MouseHumanSequence.rpt.gz`
|
| 7 |
+
|
| 8 |
+
| field | value |
|
| 9 |
+
|---|---|
|
| 10 |
+
| origin | `https://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt` |
|
| 11 |
+
| format | tab-separated, 13 columns, gzipped here (3,243,831 bytes; 15,110,808 raw) |
|
| 12 |
+
| retrieved | 2026-09-04; the served file was stamped `last-modified: 2026-08-31` |
|
| 13 |
+
| provenance | **derived / computed — not experimental.** Ortholog assignments are *inferred*, not measured |
|
| 14 |
+
| rows | 46,521 data rows over **21,928 homology classes** |
|
| 15 |
+
| organisms | 24,592 `human` rows, 21,929 `mouse, laboratory` rows |
|
| 16 |
+
| distinct symbols | 19,366 human, 21,926 mouse |
|
| 17 |
+
| human rows carrying a SWISS_PROT ID | 23,569 of 24,592 (95.8 %) |
|
| 18 |
+
|
| 19 |
+
```zsh
|
| 20 |
+
curl -sL -o HOM_MouseHumanSequence.rpt \
|
| 21 |
+
https://www.informatics.jax.org/downloads/reports/HOM_MouseHumanSequence.rpt
|
| 22 |
+
gzip -9 HOM_MouseHumanSequence.rpt
|
| 23 |
+
```
|
| 24 |
+
|
| 25 |
+
**MGI is a redistributor here, not the source of the inference.** `informatics.jax.org/homology.shtml`
|
| 26 |
+
states that MGI "uses homology class data from the Alliance of Genome Resources", and does **not**
|
| 27 |
+
state the method by which the Alliance computes an ortholog assignment. So the right claim to make
|
| 28 |
+
downstream is "MGI/Alliance homology class", never "orthologue" as though it were measured — and a
|
| 29 |
+
one-to-many class is a real answer, not a failure to resolve.
|
| 30 |
+
|
| 31 |
+
**Why this file and not `HOM_ProteinCoding.rpt` or a UniProt round-trip.** Two properties, and both
|
| 32 |
+
are load-bearing:
|
| 33 |
+
|
| 34 |
+
1. **It carries `SWISS_PROT IDs`**, so a human UniProt accession reaches a mouse gene in *one* hop.
|
| 35 |
+
`HOM_ProteinCoding.rpt` has no accession column and would need the proteome FASTA first.
|
| 36 |
+
2. **Its `Symbol` column is the MGI official symbol**, which is the vocabulary
|
| 37 |
+
`expression/toil_matrix_mmu.npz` is keyed on (26,737 genes).
|
| 38 |
+
|
| 39 |
+
That second one is the whole reason a curated table was fetched at all. The obvious cheap route --
|
| 40 |
+
read `GN=` out of `proteome/{human,mouse}.fasta.gz` and match UniProt's shared entry-name mnemonic
|
| 41 |
+
(`P53_HUMAN` / `P53_MOUSE`) -- resolves 14 of 21 accessions and returns UniProt's `GN=Tp53` for
|
| 42 |
+
human TP53. **`Tp53` is absent from `toil_matrix_mmu.npz`; the MGI official symbol `Trp53` is
|
| 43 |
+
present.** A lookup that returns nothing and a lookup that returns the wrong thing fail the same way
|
| 44 |
+
when the layer below drops silently, which is this package's recurring hazard. Through this file the
|
| 45 |
+
same 21 accessions resolve 16 directly, and 21 of 21 once a human-symbol fallback is added.
|
| 46 |
+
|
| 47 |
+
**Two accessions on `source_species = human` rows of `neoantigens/neoag_tested_mmu.tsv.gz` are
|
| 48 |
+
*mouse* accessions** -- `O54916` (Reps1) and `Q8VDL4` (Adpgk). They are not xenoantigens; the
|
| 49 |
+
deposit's species label is wrong on those two rows. A resolver that reports which route answered
|
| 50 |
+
sees this; one that silently falls through does not.
|
icb_cohorts/COHORTS.md
ADDED
|
@@ -0,0 +1,77 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# The cohort table — what each one can carry, and what it is missing
|
| 2 |
+
|
| 3 |
+
Reconciled 2026-09-10 from the screen recorded in `2026-mhcmatch-benchmark/bench/results/cohort_availability.md`
|
| 4 |
+
and the provenance in `SOURCES.md` beside this file. The analysis plan that consumes this table is
|
| 5 |
+
`2026-mhcmatch-benchmark/REPLICATION_PLAN.md`.
|
| 6 |
+
|
| 7 |
+
**Three things are needed per patient**: somatic mutation calls at protein level (a tumour mutational
|
| 8 |
+
burden count is not enough), four-digit class-I HLA-A/B/C, and a clinical outcome. Tumour RNA is a
|
| 9 |
+
fourth that improves two of the nine EPIC terms and is not required — `expr_lvl` and `expr_norm` fall
|
| 10 |
+
back to the GTEx/TCGA tumour-type median with `expr_imputed` recording that they did.
|
| 11 |
+
|
| 12 |
+
**Typing is the scarce leg.** Of 200 distinct cohorts screened, 74 are blocked on typing alone.
|
| 13 |
+
`harmonized/typing.parquet` is the result of searching for it: 52,492 patients, 11 cohorts,
|
| 14 |
+
360 distinct allotypes.
|
| 15 |
+
|
| 16 |
+
## Tier 1 — mutations, true typing and outcome, all public
|
| 17 |
+
|
| 18 |
+
| cohort | patients | tumour | therapy | mutation calls | clone or CCF layer | typing source | tumour RNA | outcomes |
|
| 19 |
+
|---|--:|---|---|---|---|---|---|---|
|
| 20 |
+
| MSK-IMPACT checkpoint union | 2,655 | pan-cancer | PD-1, PD-L1, CTLA-4, combination | cBioPortal panel MAF | FACETS cancer-cell fraction | `msk_impact_50k_2026` germline, six alleles | none | overall survival 2,655; progression 1,699 |
|
| 21 |
+
| SU2C-MARK (Ravi 2023) | 309 | non-small-cell lung | anti-PD-(L)1 | exome MAF | PhyloGic cluster per mutation | Polysolver, Table S9 | subset | overall survival, progression-free survival, RECIST |
|
| 22 |
+
| Van Allen 2015 | 110 | melanoma | anti-CTLA-4 | exome MAF | none | cBioPortal sample attribute | none | overall survival, disease-free survival, best response |
|
| 23 |
+
| Anagnostou 2020 | 89 | non-small-cell lung | anti-PD-(L)1 | supplementary S3 | cancer-cell fraction per mutation | supplementary S11, with per-locus loss of heterozygosity | none | response, progression-free survival, overall survival |
|
| 24 |
+
| Hellmann 2018 | 75 | non-small-cell lung | nivolumab + ipilimumab | exome MAF | none | cBioPortal sample attribute | none | progression-free survival, best response, durable benefit |
|
| 25 |
+
| Riaz 2017 | 68 | melanoma | nivolumab | open exome `SRP095809` | none | Chowell Table S1 | yes | response, progression-free survival, overall survival |
|
| 26 |
+
| Snyder 2014 | 64 | melanoma | anti-CTLA-4 | exome MAF | none | cBioPortal sample attribute, class II too | none | overall survival, response, durable benefit |
|
| 27 |
+
| Hugo 2016 | 38 | melanoma | anti-PD-1 | exome MAF | none | cBioPortal sample attribute | yes | overall survival, response |
|
| 28 |
+
| Rizvi 2015 | 34 | non-small-cell lung | pembrolizumab | *Science* supplement | none | Chowell Table S1 | none | response, progression-free survival |
|
| 29 |
+
| Forde 2018 | 13 | non-small-cell lung, neoadjuvant | nivolumab | supplementary S6 | none | recoverable from supplementary S8 | none | pathological response |
|
| 30 |
+
|
| 31 |
+
The eight exome cohorts below SU2C-MARK pool to **491 patients** with deep calls and true typing.
|
| 32 |
+
That pool, not MSK, is the like-for-like replication of the SU2C-MARK arm.
|
| 33 |
+
|
| 34 |
+
## Tier 2 — mutations and outcome public, typing absent, imputation available
|
| 35 |
+
|
| 36 |
+
| cohort | patients with calls | patients with outcome | tumour | randomised comparison | mutation calls | clone or CCF layer | tumour RNA | outcomes |
|
| 37 |
+
|---|--:|--:|---|---|---|---|---|---|
|
| 38 |
+
| Jammihal 2025 (CheckMate-214 + JAVELIN Renal 101) | 953 | 950 | renal cell | 465 checkpoint against 488 tyrosine-kinase inhibitor | 129,726 mutations with `Protein_Change` | ABSOLUTE `ccf_hat` and a clonality call | 733 | objective response, RECIST, progression-free survival, overall survival |
|
| 39 |
+
| Braun 2020 (CheckMate 009/010/025) | 454 | 1,006 | renal cell | 609 nivolumab against 397 everolimus | 68,030 mutations, 26,434 missense | none | 311 | objective response, benefit, progression-free survival, overall survival, immune-related progression-free survival |
|
| 40 |
+
|
| 41 |
+
Both are blocked on typing alone and both carry a **randomised control arm**, which no tier-1 cohort
|
| 42 |
+
does. Under imputed genotypes the patient-level ordering survives at Spearman rho +0.9433 at k = 20
|
| 43 |
+
and the top-k peptide sets do not (mean Jaccard 0.143) — `bench/results/hla_impute_check.md`. So
|
| 44 |
+
these two carry an outcome analysis and not a design claim.
|
| 45 |
+
|
| 46 |
+
## Tier 3 — one email away
|
| 47 |
+
|
| 48 |
+
| ask | patients returned | who | what to request |
|
| 49 |
+
|---|--:|---|---|
|
| 50 |
+
| Jammihal and Braun typing | ~1,400 | David A. Braun (Yale), Sachet A. Shukla — senior authors of both | the POLYSOLVER class-I calls, keyed to `SUBJID` and `UUID`. Promotes both tier-2 rows to tier 1 |
|
| 51 |
+
| IMvigor210 | 348 | Genentech | class-I genotypes; exome MAF, RECIST and overall survival are public |
|
| 52 |
+
| IMmotion150 | 263 | David F. McDermott (BIDMC) | Polysolver calls; the apparently public typing is an MHCflurry argmax complete for 45 of 157 |
|
| 53 |
+
| Liu 2019 | 144 | Dana-Farber / Essen | the POLYSOLVER calls run for the paper's own loss-of-heterozygosity analysis, never published |
|
| 54 |
+
|
| 55 |
+
## Tier 4 — typing computable from open reads, no email needed
|
| 56 |
+
|
| 57 |
+
| cohort | patients | reads | already public |
|
| 58 |
+
|---|--:|---|---|
|
| 59 |
+
| MORRISON-1 (CheckMate 038/064/067) | 427 | `PRJNA923698`, 160 open exome runs | variant allele frequency, logCPM, RECIST at `ParkerICI/MORRISON-1-public` |
|
| 60 |
+
| Zhao 2019 glioblastoma | 66 | `PRJNA482620`, 91 of 91 runs open | mutations, response, survival |
|
| 61 |
+
| Kim 2018 gastric | 61 | `PRJEB25780`, 95 exomes and 71 RNA | mutations, objective response, progression-free survival |
|
| 62 |
+
| Hong 2022 hepatocellular | 60 | `PRJEB34724`, 89 exomes and 40 RNA | mutations, objective response, progression-free survival, overall survival |
|
| 63 |
+
|
| 64 |
+
## Tier 5 — typed and public, but not checkpoint blockade
|
| 65 |
+
|
| 66 |
+
Survival cohorts beside TCGA rather than beside SU2C-MARK: PCAWG / ICGC 1,028 donors closing all
|
| 67 |
+
three legs; TRACERx 421 with 89 typed and relapse-free survival; ICGC OV-AU ovarian 93 donors.
|
| 68 |
+
Hartwig 4,439 and PCAWG 1,839 carry LILAC typing in the open; Hartwig's mutations and outcome need
|
| 69 |
+
an application.
|
| 70 |
+
|
| 71 |
+
## Ruled out, so nobody looks twice
|
| 72 |
+
|
| 73 |
+
Gide 2019 and Kang/Lee 2024 sequenced no exome. IMmotion151 publishes no per-mutation calls.
|
| 74 |
+
Chowell Cohort 2 has typing and overall survival but its identifiers cannot reach per-mutation
|
| 75 |
+
calls. Litchfield CPI1000+ is anonymised. AACR GENIE BPC releases no germline. Cloughesy 2019,
|
| 76 |
+
Khan 2020, Rose 2021, MOSCATO-01 and TOPACIO release too little. The 525-patient NeoPrecis
|
| 77 |
+
compendium publishes no typing — it stays a directional-rule arm only, `bench/results/cassette_ici.md`.
|
icb_cohorts/SOURCES.md
ADDED
|
@@ -0,0 +1,45 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# ICB cohort assembly — sources
|
| 2 |
+
|
| 3 |
+
Staged 2026-09-10. **`raw/` is gitignored and not published** -- publisher supplementary workbooks,
|
| 4 |
+
copyrighted and not ours to redistribute. **`harmonized/` is published** (2026-09-11): tables we
|
| 5 |
+
derive from them -- our own six-allele genotypes over public typings -- carrying no publisher content.
|
| 6 |
+
|
| 7 |
+
The analysis being assembled for needs, per patient: somatic mutation calls at protein level,
|
| 8 |
+
four-digit class-I HLA-A/B/C, and a clinical outcome. **Typing is the scarce leg**, so it is
|
| 9 |
+
harmonised first, by `harmonize_typing.py`.
|
| 10 |
+
|
| 11 |
+
## `raw/` — what each file is, and where it came from
|
| 12 |
+
|
| 13 |
+
| file | bytes | source | what it contributes |
|
| 14 |
+
|---|--:|---|---|
|
| 15 |
+
| `chowell2018_TableS1.xlsx` | 196,759 | Chowell D et al., *Science* 2018;359:582-587, doi:10.1126/science.aao4572, PMID 29301960, PMC6057471 — Supplementary Table S1 (`NIHMS980063-supplement-Supplemental_Table_S1.xlsx`) | **experimental** typing. `Cohort 1` 371 patients (Van Allen 100, Riaz 68, Rizvi_CUMC 67, Snyder 64, Hugo 38, Rizvi 2015 34) with `HLA Class I Alleles`, `OS_Months`, `OS_Event`, `Drug Class`, `MutCnt`; `Cohort 2` 1,166 MSK-IMPACT with the same; `HLA-II` 304; a `TCR CDR3s` sheet besides. 370 of 371 and 1,166 of 1,166 carry six four-digit alleles |
|
| 16 |
+
| `forde2018_nejm_S4-S10.xlsx`, `forde2018_nejm_supp1.pdf` | 3,077,810 / 1,662,342 | Forde PM et al., *N Engl J Med* 2018;378:1976-1986 — neoadjuvant nivolumab in resectable NSCLC (`NIHMS980037`) | **experimental**. `S6` 1,609 somatic alterations over 13 samples with hg19 coordinates, `Amino Acid (protein)`, CCDS transcript and consequence; `S8` 19,585 neoantigen rows carrying `HLA Allele` — six alleles per sample, so the genotype is recoverable for all 13 |
|
| 17 |
+
| `anagnostou2020_natcancer_S.xlsx` | 9,649,675 | Anagnostou V et al., *Nat Cancer* 2020;1:99-111, doi:10.1038/s43018-019-0008-8, PMC7514475 — Supplementary Tables | **experimental**. `S11` *HLA class I genomic variation*: 89 patients x six four-digit alleles plus per-locus LOH; `S3` per-mutation calls with cancer cell fraction |
|
| 18 |
+
| `braun2020_natmed_MOESM2.xlsx` | 125,569,016 | Braun DA et al., *Nat Med* 2020;26:909-918, doi:10.1038/s41591-020-0839-y — CheckMate 009/010/025 ccRCC | **experimental**. `S1_Clinical_and_Immune_Data` 1,006 patients x 121 columns (`Arm` nivolumab 609 / everolimus 397; `Cohort` CM-025 803, CM-010 168, CM-009 35; `ORR`, `Benefit`, `PFS`+`PFS_CNSR`, `OS`+`OS_CNSR`, `irPFS`); `S2_WES_Data` 68,030 somatic mutations (26,434 missense) over 454 tumours with `Protein_Change`; `S4A_RNA_Expression` on 311. **No HLA sheet — read every sheet, 2026-09-10** |
|
| 19 |
+
| `jammihal2025_rcc_S1-S6.xlsx` | 195,455,480 | Jammihal T et al., *Nat Cancer* 2025;6:372-384, doi:10.1038/s43018-024-00896-w, PMID 39789182 — CheckMate-214 + JAVELIN Renal 101 | **experimental**. `Supp. Table 1` 1,958 patients (arms, ORR, RECIST, PFS/OS with censoring); `Supp. Table 4` 129,726 mutations over 953 patients with `Protein_Change` and ABSOLUTE `ccf_hat`/clonality; `Supp. Table 3` expression on 733. **No HLA sheet** |
|
| 20 |
+
| `mjimenez2023_hla_suppl1.xlsx` | 882,368 | Martinez-Jimenez F et al., *Nat Genet* 2023;55:820-831, doi:10.1038/s41588-023-01367-1, PMID 37165135 — Supplementary Data 1 | **derived from experimental reads**: LILAC / xHLA / Polysolver class-I typing on normal *and* tumour separately. Hartwig 4,439 samples, PCAWG 1,839, plus TRACERx typing (491 rows) and LOH sheets |
|
| 21 |
+
| `neoprecis_nci_alleles.csv` | 12,622 | `cartercompbio/NeoPrecis`, `data/nci_alleles.csv` | **experimental** typing for the 75 NCI Surgery Branch GI patients, class I and class II |
|
| 22 |
+
|
| 23 |
+
Fetched with `curl -A <browser UA>` from `static-content.springer.com`; PMC serves a bot challenge
|
| 24 |
+
for the same files and must not be used. Chowell and Forde are NIHMS deposits and arrived through
|
| 25 |
+
the author's own browser download.
|
| 26 |
+
|
| 27 |
+
## `harmonized/`
|
| 28 |
+
|
| 29 |
+
`typing.parquet` — one row per patient, columns `cohort`, `study`, `patient`, `A1 A2 B1 B2 C1 C2`,
|
| 30 |
+
every allele written `HLA-A*02:01`. A homozygous locus repeats its allele. Built 2026-09-10:
|
| 31 |
+
|
| 32 |
+
| source | patients | breakdown |
|
| 33 |
+
|---|--:|---|
|
| 34 |
+
| Chowell 2018 | 1,536 | `chowell_c1` 370, `chowell_c2` 1,166 |
|
| 35 |
+
| MSK-IMPACT 50K (cBioPortal API) | 44,227 | germline, full field, cut to two |
|
| 36 |
+
| Martinez-Jimenez 2023 | 6,278 | Hartwig 4,439, PCAWG 1,839 (LILAC **normal** calls, not tumour) |
|
| 37 |
+
| cBioPortal checkpoint-blockade studies | 287 | Van Allen 110, Hellmann 75, Snyder 64, Hugo 38 |
|
| 38 |
+
| Anagnostou 2020 | 89 | |
|
| 39 |
+
| NCI Surgery Branch | 75 | |
|
| 40 |
+
| **total** | **52,492** | 11 cohorts, 360 distinct allotypes |
|
| 41 |
+
|
| 42 |
+
Mutation and outcome tables are deliberately **not** built yet: the selection is open, and
|
| 43 |
+
harmonising 100k-row MAFs for cohorts that may be dropped is work spent before the decision.
|
| 44 |
+
|
| 45 |
+
The reconciled cohort table is `COHORTS.md` beside this file; the analysis plan is `2026-mhcmatch-benchmark/REPLICATION_PLAN.md`.
|
icb_cohorts/harmonize_typing.py
ADDED
|
@@ -0,0 +1,199 @@
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|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
# 2026-09-10 -- one class-I typing table across every cohort whose genotypes we can reach.
|
| 3 |
+
"""Class-I typing is the scarce leg of every checkpoint-blockade cohort, so it is harmonised first.
|
| 4 |
+
|
| 5 |
+
Writes `harmonized/typing.parquet` with one row per (cohort, patient, locus, allele_1, allele_2)
|
| 6 |
+
flattened to six columns, every allele in `HLA-A*02:01` form, plus `harmonized/manifest.tsv`
|
| 7 |
+
naming what each source contributes. Mutation and outcome tables are deliberately NOT built yet:
|
| 8 |
+
the manuscript's cohort selection is open, and harmonising 100k-row MAFs for cohorts that may be
|
| 9 |
+
dropped is work spent before the decision.
|
| 10 |
+
|
| 11 |
+
uv run --with openpyxl --with polars python3 harmonize_typing.py
|
| 12 |
+
"""
|
| 13 |
+
from __future__ import annotations
|
| 14 |
+
|
| 15 |
+
import collections
|
| 16 |
+
import csv
|
| 17 |
+
import json
|
| 18 |
+
import os
|
| 19 |
+
import re
|
| 20 |
+
import urllib.request
|
| 21 |
+
|
| 22 |
+
HERE = os.path.dirname(os.path.abspath(__file__))
|
| 23 |
+
RAW = os.path.join(HERE, "raw")
|
| 24 |
+
OUT = os.path.join(HERE, "harmonized")
|
| 25 |
+
LOCI = ("A", "B", "C")
|
| 26 |
+
|
| 27 |
+
|
| 28 |
+
def norm(a: str) -> str | None:
|
| 29 |
+
"""Every spelling seen across these sources -> ``HLA-A*02:01``, or None if it is not one."""
|
| 30 |
+
if a is None:
|
| 31 |
+
return None
|
| 32 |
+
s = str(a).strip()
|
| 33 |
+
if not s or s.upper() in ("NA", "NAN", "-", "NONE"):
|
| 34 |
+
return None
|
| 35 |
+
s = s.replace("HLA-", "").replace("hla_", "").replace("HLA", "")
|
| 36 |
+
m = re.match(r"^([ABC])\*?(\d{2}):?(\d{2})", s) # A*02:01 / A0201 / A02:01
|
| 37 |
+
if m:
|
| 38 |
+
return f"HLA-{m.group(1)}*{m.group(2)}:{m.group(3)}"
|
| 39 |
+
m = re.match(r"^_?([abc])_(\d{2})_(\d{2})", s) # hla_a_02_01_01_01
|
| 40 |
+
if m:
|
| 41 |
+
return f"HLA-{m.group(1).upper()}*{m.group(2)}:{m.group(3)}"
|
| 42 |
+
return None
|
| 43 |
+
|
| 44 |
+
|
| 45 |
+
def six(alleles: list[str]) -> dict | None:
|
| 46 |
+
"""Bucket a patient's calls by locus and require two per locus (a homozygote repeats)."""
|
| 47 |
+
by = collections.defaultdict(list)
|
| 48 |
+
for a in alleles:
|
| 49 |
+
n = norm(a)
|
| 50 |
+
if n:
|
| 51 |
+
by[n[4]].append(n)
|
| 52 |
+
row = {}
|
| 53 |
+
for L in LOCI:
|
| 54 |
+
v = sorted(set(by[L])) or []
|
| 55 |
+
if len(v) == 1:
|
| 56 |
+
v = v * 2
|
| 57 |
+
if len(v) != 2:
|
| 58 |
+
return None
|
| 59 |
+
row[f"{L}1"], row[f"{L}2"] = v
|
| 60 |
+
return row
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
def chowell() -> list[dict]:
|
| 64 |
+
import openpyxl
|
| 65 |
+
wb = openpyxl.load_workbook(f"{RAW}/chowell2018_TableS1.xlsx", read_only=True, data_only=True)
|
| 66 |
+
out = []
|
| 67 |
+
for sheet, tag in (("Cohort 1", "chowell_c1"), ("Cohort 2", "chowell_c2")):
|
| 68 |
+
ws = wb[sheet]
|
| 69 |
+
it = ws.iter_rows(values_only=True)
|
| 70 |
+
hdr = [h for h in next(it) if h]
|
| 71 |
+
ia, ir = hdr.index("HLA Class I Alleles"), (hdr.index("Reference") if "Reference" in hdr else None)
|
| 72 |
+
for r in it:
|
| 73 |
+
if not r or not r[0]:
|
| 74 |
+
continue
|
| 75 |
+
g = six(str(r[ia]).split(","))
|
| 76 |
+
if g:
|
| 77 |
+
out.append({"cohort": tag, "study": (r[ir] if ir is not None else tag),
|
| 78 |
+
"patient": str(r[0]), **g})
|
| 79 |
+
return out
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
def anagnostou() -> list[dict]:
|
| 83 |
+
import openpyxl
|
| 84 |
+
wb = openpyxl.load_workbook(f"{RAW}/anagnostou2020_natcancer_S.xlsx", read_only=True,
|
| 85 |
+
data_only=True)
|
| 86 |
+
ws = wb["S11"]
|
| 87 |
+
rows = [r for r in ws.iter_rows(values_only=True) if r and any(x is not None for x in r)]
|
| 88 |
+
hdr = [str(x) for x in rows[1]]
|
| 89 |
+
ip = hdr.index("Patient ID") if "Patient ID" in hdr else 0
|
| 90 |
+
cols = [i for i, h in enumerate(hdr) if h.startswith("HLA-") and "Allele" in h]
|
| 91 |
+
out = []
|
| 92 |
+
for r in rows[2:]:
|
| 93 |
+
g = six([r[i] for i in cols])
|
| 94 |
+
if g:
|
| 95 |
+
out.append({"cohort": "anagnostou2020", "study": "Anagnostou 2020 Nat Cancer",
|
| 96 |
+
"patient": str(r[ip]), **g})
|
| 97 |
+
return out
|
| 98 |
+
|
| 99 |
+
|
| 100 |
+
def hartwig_pcawg() -> list[dict]:
|
| 101 |
+
import openpyxl
|
| 102 |
+
wb = openpyxl.load_workbook(f"{RAW}/mjimenez2023_hla_suppl1.xlsx", read_only=True,
|
| 103 |
+
data_only=True)
|
| 104 |
+
out = []
|
| 105 |
+
for sheet, tag in (("Hartwig", "hartwig"), ("PCAWG", "pcawg")):
|
| 106 |
+
ws = wb[sheet]
|
| 107 |
+
it = ws.iter_rows(values_only=True)
|
| 108 |
+
hdr = [str(h) for h in next(it)]
|
| 109 |
+
cols = [i for i, h in enumerate(hdr) if h.startswith("LILAC_n_")] # normal, not tumour
|
| 110 |
+
for r in it:
|
| 111 |
+
if not r or not r[0]:
|
| 112 |
+
continue
|
| 113 |
+
g = six([r[i] for i in cols])
|
| 114 |
+
if g:
|
| 115 |
+
out.append({"cohort": tag, "study": "Martinez-Jimenez 2023 Nat Genet",
|
| 116 |
+
"patient": str(r[0]), **g})
|
| 117 |
+
return out
|
| 118 |
+
|
| 119 |
+
|
| 120 |
+
def nci() -> list[dict]:
|
| 121 |
+
out = []
|
| 122 |
+
with open(f"{RAW}/neoprecis_nci_alleles.csv") as f:
|
| 123 |
+
for row in csv.DictReader(f):
|
| 124 |
+
cols = [v for k, v in row.items() if k and re.match(r"^[ABC]_\d$", k)]
|
| 125 |
+
g = six(cols)
|
| 126 |
+
if g:
|
| 127 |
+
out.append({"cohort": "nci_gi", "study": "NCI Surgery Branch (via NeoPrecis)",
|
| 128 |
+
"patient": row.get("Patient") or row.get("sample") or "", **g})
|
| 129 |
+
return out
|
| 130 |
+
|
| 131 |
+
|
| 132 |
+
def cbioportal() -> list[dict]:
|
| 133 |
+
"""The four checkpoint-blockade studies whose typing is a SAMPLE-level attribute."""
|
| 134 |
+
api = "https://www.cbioportal.org/api/studies/{}/clinical-data?clinicalDataType=SAMPLE&projection=SUMMARY&pageSize=100000"
|
| 135 |
+
studies = {"skcm_dfci_2015": "Van Allen 2015", "skcm_mskcc_2014": "Snyder 2014",
|
| 136 |
+
"mel_ucla_2016": "Hugo 2016", "nsclc_mskcc_2018": "Hellmann 2018"}
|
| 137 |
+
out = []
|
| 138 |
+
for sid, name in studies.items():
|
| 139 |
+
with urllib.request.urlopen(api.format(sid), timeout=120) as fh:
|
| 140 |
+
data = json.load(fh)
|
| 141 |
+
S = collections.defaultdict(dict)
|
| 142 |
+
for r in data:
|
| 143 |
+
S[r["sampleId"]][r["clinicalAttributeId"]] = r["value"]
|
| 144 |
+
for sample, d in S.items():
|
| 145 |
+
vals = []
|
| 146 |
+
for k, v in d.items():
|
| 147 |
+
if k.startswith("HLA_") and "LOH" not in k:
|
| 148 |
+
vals += str(v).split("/")
|
| 149 |
+
g = six(vals)
|
| 150 |
+
if g:
|
| 151 |
+
out.append({"cohort": sid, "study": name, "patient": sample, **g})
|
| 152 |
+
return out
|
| 153 |
+
|
| 154 |
+
|
| 155 |
+
def msk50k() -> list[dict]:
|
| 156 |
+
api = ("https://www.cbioportal.org/api/studies/msk_impact_50k_2026/clinical-data"
|
| 157 |
+
"?clinicalDataType=PATIENT&attributeId={}&projection=SUMMARY&pageSize=200000")
|
| 158 |
+
H = collections.defaultdict(list)
|
| 159 |
+
for a in ("HLA-A1", "HLA-A2", "HLA-B1", "HLA-B2", "HLA-C1", "HLA-C2"):
|
| 160 |
+
with urllib.request.urlopen(api.format(a), timeout=300) as fh:
|
| 161 |
+
for r in json.load(fh):
|
| 162 |
+
H[r["patientId"]].append(r["value"])
|
| 163 |
+
out = []
|
| 164 |
+
for p, vals in H.items():
|
| 165 |
+
g = six(vals)
|
| 166 |
+
if g:
|
| 167 |
+
out.append({"cohort": "msk_impact_50k", "study": "MSK-IMPACT 50K", "patient": p, **g})
|
| 168 |
+
return out
|
| 169 |
+
|
| 170 |
+
|
| 171 |
+
SOURCES = {"chowell2018": chowell, "anagnostou2020": anagnostou,
|
| 172 |
+
"martinez_jimenez2023": hartwig_pcawg, "nci_neoprecis": nci,
|
| 173 |
+
"cbioportal_icb": cbioportal, "msk_impact_50k": msk50k}
|
| 174 |
+
|
| 175 |
+
|
| 176 |
+
def main() -> None:
|
| 177 |
+
import polars as pl
|
| 178 |
+
os.makedirs(OUT, exist_ok=True)
|
| 179 |
+
rows, man = [], []
|
| 180 |
+
for name, fn in SOURCES.items():
|
| 181 |
+
try:
|
| 182 |
+
got = fn()
|
| 183 |
+
except Exception as e: # noqa: BLE001
|
| 184 |
+
print(f"# {name}: FAILED {e}")
|
| 185 |
+
man.append({"source": name, "patients": 0, "note": f"failed: {e}"})
|
| 186 |
+
continue
|
| 187 |
+
rows += got
|
| 188 |
+
by = collections.Counter(g["cohort"] for g in got)
|
| 189 |
+
print(f"# {name}: {len(got):,} patients {dict(by)}")
|
| 190 |
+
man.append({"source": name, "patients": len(got), "note": json.dumps(dict(by))})
|
| 191 |
+
df = pl.DataFrame(rows)
|
| 192 |
+
df.write_parquet(f"{OUT}/typing.parquet")
|
| 193 |
+
pl.DataFrame(man).write_csv(f"{OUT}/manifest.tsv", separator="\t")
|
| 194 |
+
print(f"\n# typing.parquet: {df.height:,} patients over {df['cohort'].n_unique()} cohorts, "
|
| 195 |
+
f"{len(set(df['A1']) | set(df['A2']) | set(df['B1']) | set(df['B2']) | set(df['C1']) | set(df['C2']))} distinct allotypes")
|
| 196 |
+
|
| 197 |
+
|
| 198 |
+
if __name__ == "__main__":
|
| 199 |
+
main()
|
icb_cohorts/harmonized/manifest.tsv
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
source patients note
|
| 2 |
+
chowell2018 1536 "{""chowell_c1"": 370, ""chowell_c2"": 1166}"
|
| 3 |
+
anagnostou2020 89 "{""anagnostou2020"": 89}"
|
| 4 |
+
martinez_jimenez2023 6278 "{""hartwig"": 4439, ""pcawg"": 1839}"
|
| 5 |
+
nci_neoprecis 75 "{""nci_gi"": 75}"
|
| 6 |
+
cbioportal_icb 287 "{""skcm_dfci_2015"": 110, ""skcm_mskcc_2014"": 64, ""mel_ucla_2016"": 38, ""nsclc_mskcc_2018"": 75}"
|
| 7 |
+
msk_impact_50k 44227 "{""msk_impact_50k"": 44227}"
|
icb_cohorts/harmonized/typing.parquet
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:0b8a7494b2d3c73142a7f5cc707bb093732b9c6e23473b0d425842d0daa5669c
|
| 3 |
+
size 289060
|
immunogenicity/SOURCES.md
ADDED
|
@@ -0,0 +1,726 @@
|
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|
|
|
|
| 1 |
+
# `immunogenicity/` — sources, provenance, filtering and a complete audit
|
| 2 |
+
|
| 3 |
+
General immunogenicity corpora: **is this presented peptide seen by a T cell?** Neoantigen datasets
|
| 4 |
+
moved to [`../neoantigens/`](../neoantigens/SOURCES.md) — this directory no longer holds NCI,
|
| 5 |
+
`neoag_*` or any tumour-specific screen.
|
| 6 |
+
|
| 7 |
+
Every citation resolved against **PubMed**; PMIDs and DOIs given. Audit regenerated by
|
| 8 |
+
`bench/neoag/audit_pmhc_data.py` in `2026-mhcmatch-benchmark`; corpora rebuilt by
|
| 9 |
+
`bench/neoag/corpus_chowell.py` + `bench/neoag/deposit_corpora.py`.
|
| 10 |
+
|
| 11 |
+
Written 2026-08-17.
|
| 12 |
+
|
| 13 |
+
---
|
| 14 |
+
|
| 15 |
+
## Complete audit
|
| 16 |
+
|
| 17 |
+
Measured from the files, not copied from any prior description.
|
| 18 |
+
|
| 19 |
+
| file | rows | distinct peptides | pos | neg | prevalence | HLA alleles | length |
|
| 20 |
+
|---|--:|--:|--:|--:|--:|--:|:--:|
|
| 21 |
+
| `chowell_rebuilt.tsv.gz` | 511,301 | 315,191 | 19,866 | 491,435 | 0.0389 | 210 | 8–11 |
|
| 22 |
+
| `chowell_rebuilt_hla_matched.tsv.gz` | 115,592 | 99,763 | 19,866 | 95,726 | 0.1719 | 183 | 8–11 |
|
| 23 |
+
| `kesmir_rebuilt.tsv.gz` | 65,737 | 36,155 | 22,613 | 43,124 | 0.3440 | 196 | 8–11 |
|
| 24 |
+
| `kesmir_rebuilt_hla_matched.tsv.gz` | 59,941 | 35,202 | 22,613 | 37,328 | 0.3773 | 188 | 8–11 |
|
| 25 |
+
| `iedb_labeled.tsv.gz` | 790,868 | 516,092 | 34,362 | 756,506 | 0.0435 | 250 | 2–50 |
|
| 26 |
+
| `hla_pop_freqs.tsv.gz` | 7,171 | — | — | — | — | 4,234 | — |
|
| 27 |
+
|
| 28 |
+
No donor/patient identifier exists in any file here — these are peptide-level corpora aggregated
|
| 29 |
+
over studies, not per-donor cohorts. (Donor counts are meaningful in `../neoantigens/` and are
|
| 30 |
+
reported there.)
|
| 31 |
+
|
| 32 |
+
---
|
| 33 |
+
|
| 34 |
+
## `chowell_rebuilt.tsv.gz` — immunogenic vs presented **self**
|
| 35 |
+
|
| 36 |
+
Chowell's construction, rebuilt from the current IEDB dump rather than read off the 2015
|
| 37 |
+
spreadsheet, which is copyrighted supplementary material and frozen at 2015.
|
| 38 |
+
|
| 39 |
+
> Chowell D, Krishna S, Becker PD, Cocita C, Shu J, Tan X, Greenberg PD, Klavinskis LS,
|
| 40 |
+
> Blattman JN, Anderson KS. **TCR contact residue hydrophobicity is a hallmark of immunogenic CD8+
|
| 41 |
+
> T cell epitopes.** *Proc Natl Acad Sci USA* 2015;112(14):E1754–62.
|
| 42 |
+
> PMID [25831525](https://pubmed.ncbi.nlm.nih.gov/25831525/) ·
|
| 43 |
+
> doi:[10.1073/pnas.1500973112](https://doi.org/10.1073/pnas.1500973112)
|
| 44 |
+
|
| 45 |
+
### Filtering procedure, in order
|
| 46 |
+
|
| 47 |
+
1. **Source** — `iedb_labeled.tsv.gz` (see below), itself an IEDB export.
|
| 48 |
+
2. **Class** — `mhc_class == "MHCI"`.
|
| 49 |
+
3. **Host** — `host_species ∈ {human, mouse}`; kept as a column and **never pooled**. The two hosts
|
| 50 |
+
have different MHC and different thymic repertoires, so a fit across them without the split is
|
| 51 |
+
fitting a mixture.
|
| 52 |
+
4. **Peptide** — uppercased, whitespace-stripped; length **8–11**; canonical 20 amino acids only.
|
| 53 |
+
5. **Exclusions** — `SLLMWITQV`, `KLGGALQAK` (NY-ESO-1 / MAGE-A3 epitopes that appear on both sides
|
| 54 |
+
of the literature with contradictory annotation).
|
| 55 |
+
6. **Positives** — any peptide with a positive T-cell assay (`dataset_origin == "iedb_tcell"`).
|
| 56 |
+
7. **Negatives** — eluted ligands whose `source_species` equals the **host** (self), plus the HLA
|
| 57 |
+
Ligand Atlas thymus immunopeptidome for the human arm.
|
| 58 |
+
8. **Label is a property of the (peptide, host), not the row.** 1,075 peptides are both eluted and
|
| 59 |
+
positively assayed. Elution is evidence of *presentation*; it is never evidence against a T-cell
|
| 60 |
+
response. Assigning per row makes the same sequence positive under one allele and negative under
|
| 61 |
+
another — it sits on both sides of the fit. Computing the positive set once per (peptide, host)
|
| 62 |
+
and applying it everywhere raised self-sourced positives by **57%**.
|
| 63 |
+
9. **Aggregation** — one row per `(peptide, allele, host_species)`, label = max.
|
| 64 |
+
|
| 65 |
+
### Not controlled, deliberately
|
| 66 |
+
|
| 67 |
+
**Allele and length.** Immunogenic and eluted sets genuinely differ in both: some HLAs are
|
| 68 |
+
protective and some are not, and immunogenic peptides have a real length distribution that differs
|
| 69 |
+
from bulk ligands. Matching those away removes effects, not confounds. `allele` and `length` ship
|
| 70 |
+
as columns so a stratified analysis is a filter, not a rebuild — and the HLA-matched variant below
|
| 71 |
+
exists for when allele composition *is* the thing to remove.
|
| 72 |
+
|
| 73 |
+
---
|
| 74 |
+
|
| 75 |
+
## `kesmir_rebuilt.tsv.gz` — immunogenic vs presented **non-self**
|
| 76 |
+
|
| 77 |
+
The Calis/Kesmir contrast. Same shape, same rules, one difference: negatives are eluted ligands
|
| 78 |
+
from a source organism that is **not** the host.
|
| 79 |
+
|
| 80 |
+
> Calis JJA, Maybeno M, Greenbaum JA, Weiskopf D, De Silva AD, Sette A, Keşmir C, Peters B.
|
| 81 |
+
> **Properties of MHC class I presented peptides that enhance immunogenicity.**
|
| 82 |
+
> *PLoS Comput Biol* 2013;9(10):e1003266.
|
| 83 |
+
> PMID [24204222](https://pubmed.ncbi.nlm.nih.gov/24204222/) ·
|
| 84 |
+
> doi:[10.1371/journal.pcbi.1003266](https://doi.org/10.1371/journal.pcbi.1003266)
|
| 85 |
+
|
| 86 |
+
**This arm requires `../ligandome/viral_foreign_iedb.tsv.gz`, and is wrong without it.** Every
|
| 87 |
+
eluted-ligand negative in the IEDB table itself is self-sourced — 449,449 human-self and 41,986
|
| 88 |
+
mouse-self against 149 + 63 cross-species, and zero from any other organism. Filtering
|
| 89 |
+
`source_species != "human"` on a **mouse** host therefore selects mouse *self* ligands and rebuilds
|
| 90 |
+
Chowell under another name. The foreign ligandome supplies 41,294 genuine human non-self negatives.
|
| 91 |
+
|
| 92 |
+
Kesmir removes the self/non-self axis that Chowell confounds with immunogenicity, and is the harder
|
| 93 |
+
problem: prevalence 0.344 against Chowell's 0.039, and every negative is a foreign peptide that was
|
| 94 |
+
presented and still not attacked.
|
| 95 |
+
|
| 96 |
+
---
|
| 97 |
+
|
| 98 |
+
## The HLA-matched variants
|
| 99 |
+
|
| 100 |
+
`*_rebuilt_hla_matched.tsv.gz`, built by `deposit_corpora.py --ratio 5 --seed 20260817`.
|
| 101 |
+
|
| 102 |
+
There are 5–25× more negatives than positives and the two sides do not share an allele
|
| 103 |
+
distribution, so a classifier can separate them on **allele composition alone**. For each allele,
|
| 104 |
+
negatives are drawn without replacement to **5 per positive of that same allele**:
|
| 105 |
+
|
| 106 |
+
* alleles with no positives contribute **no** negatives — keeping them would reintroduce exactly the
|
| 107 |
+
imbalance being removed;
|
| 108 |
+
* alleles with too few negatives contribute all they have, and the shortfall stays visible in the
|
| 109 |
+
per-allele counts rather than being topped up from another allele;
|
| 110 |
+
* the draw is seeded (`20260817`), so the file is reproducible rather than one sample.
|
| 111 |
+
|
| 112 |
+
Effect: Chowell 491,435 → 95,726 negatives, 210 → 183 alleles, prevalence 0.039 → 0.172. Kesmir
|
| 113 |
+
43,124 → 37,328, 196 → 189 alleles.
|
| 114 |
+
|
| 115 |
+
Use the matched file when the question is about peptide sequence; use the full file when allele
|
| 116 |
+
composition is part of the signal you want. Neither is "the" corpus.
|
| 117 |
+
|
| 118 |
+
---
|
| 119 |
+
|
| 120 |
+
## Two residue-level artefacts, measured
|
| 121 |
+
|
| 122 |
+
Both are properties of how the data was generated. A model will fit either as if it were biology.
|
| 123 |
+
|
| 124 |
+
| corpus | Cys % positives | Cys % negatives | ratio | C-term R/K % pos | C-term R/K % neg |
|
| 125 |
+
|---|--:|--:|--:|--:|--:|
|
| 126 |
+
| `chowell_rebuilt` | 10.69 | **1.55** | **6.9×** | 9.21 | 9.50 |
|
| 127 |
+
| `kesmir_rebuilt` | 10.94 | 14.50 | 0.8× | 9.91 | **20.33** |
|
| 128 |
+
| `iedb_labeled` | 14.56 | **2.52** | **5.8×** | 10.90 | 13.98 |
|
| 129 |
+
|
| 130 |
+
### Cysteine — an MS detection artefact, and it hits Chowell hardest
|
| 131 |
+
|
| 132 |
+
Cysteine is systematically **under-detected in mass-spectrometry immunopeptidomics** unless samples
|
| 133 |
+
are alkylated. Where positives are T-cell-assayed (synthetic peptides, no MS step) and negatives are
|
| 134 |
+
MS-eluted, the corpus carries a Cys gradient with no biological content. Chowell shows **6.9×**;
|
| 135 |
+
finer-grained, the thymus MS subset runs at 0.17% against 11.59% in the assayed positives.
|
| 136 |
+
|
| 137 |
+
Fitted freely on the Chowell corpus, Cys took the **single largest coefficient** in a position-role
|
| 138 |
+
naive Bayes (+1.84 anchor / +2.05 TCR-facing). Masking it costs grouped-CV AUROC 0.712 → 0.690 and
|
| 139 |
+
*improves* transfer to neoantigen cohorts. The shipped `mhcmatch.posbayes` tables zero it.
|
| 140 |
+
|
| 141 |
+
**`kesmir_rebuilt` does not have this problem** (0.8×) — its negatives are IEDB-curated foreign
|
| 142 |
+
ligands rather than a single MS pipeline. That makes it the safer corpus for fitting residue-level
|
| 143 |
+
terms, and is a reason to run both.
|
| 144 |
+
|
| 145 |
+
### C-terminal Arg/Lys — genuine anchor preference, or tryptic carry-over
|
| 146 |
+
|
| 147 |
+
A basic C-terminus is the real anchor preference of **HLA-A\*03, A\*11, A\*31, A\*68 and B\*27** —
|
| 148 |
+
and Arg/Lys are exactly the residues **trypsin cleaves after**. An elevated rate can be either, and
|
| 149 |
+
the two are **not separable from sequence alone**.
|
| 150 |
+
|
| 151 |
+
`kesmir_rebuilt` negatives run at **20.33%** against 9.91% in positives — a 2× gradient in the
|
| 152 |
+
corpus that is otherwise the cleaner of the two. Before attributing anything to a C-terminal basic
|
| 153 |
+
residue on this corpus, stratify by allele: if the effect survives within A\*03/A\*11/B\*27 and
|
| 154 |
+
within non-basic-preferring alleles separately, it is anchor preference; if it only appears pooled,
|
| 155 |
+
it is composition or contamination.
|
| 156 |
+
|
| 157 |
+
---
|
| 158 |
+
|
| 159 |
+
## `iedb_labeled.tsv.gz`
|
| 160 |
+
|
| 161 |
+
The IEDB export both corpora are built from. **This is also the `ipred` training set**, so any
|
| 162 |
+
`ipred` figure quoted on it is in-sample.
|
| 163 |
+
|
| 164 |
+
790,868 rows · 516,092 distinct peptides · 34,362 immunogenic · 250 alleles · lengths 2–50
|
| 165 |
+
(unfiltered — the 8–11 restriction is applied downstream).
|
| 166 |
+
|
| 167 |
+
> Vita R, Mahajan S, Overton JA, Dhanda SK, Martini S, Cantrell JR, Wheeler DK, Sette A, Peters B.
|
| 168 |
+
> **The Immune Epitope Database (IEDB): 2018 update.** *Nucleic Acids Res* 2019;47(D1):D339–D343.
|
| 169 |
+
> PMID [30357391](https://pubmed.ncbi.nlm.nih.gov/30357391/) ·
|
| 170 |
+
> doi:[10.1093/nar/gky1006](https://doi.org/10.1093/nar/gky1006)
|
| 171 |
+
|
| 172 |
+
Provenance: **experimental** — curated T-cell assay and MHC-ligand records. Raw export kept at
|
| 173 |
+
`raw/immunogenicity/iedb_tcell_assays_29032026.tsv.gz`.
|
| 174 |
+
|
| 175 |
+
Thymus negatives come from the HLA Ligand Atlas (`thymus/thymus_immunopeptidome.tsv.gz`); its
|
| 176 |
+
`mhc_a` is null by design — the deposit is not allele-deconvolved.
|
| 177 |
+
|
| 178 |
+
---
|
| 179 |
+
|
| 180 |
+
## `hla_pop_freqs.tsv.gz`
|
| 181 |
+
|
| 182 |
+
Class-I allele frequencies (A/B/C) for EUR/ASN/AFR populations, 7,171 rows over 4,234 distinct
|
| 183 |
+
alleles. Source: Allele Frequency Net Database (AFND), <http://www.allelefrequencies.net>.
|
| 184 |
+
Provenance: **experimental** (population genotyping), aggregated.
|
| 185 |
+
|
| 186 |
+
---
|
| 187 |
+
|
| 188 |
+
## Regenerate
|
| 189 |
+
|
| 190 |
+
# in ~/vcs/projects/2026-mhcmatch-benchmark
|
| 191 |
+
python bench/neoag/corpus_chowell.py # plain build (Chowell arm)
|
| 192 |
+
python bench/neoag/corpus_chowell.py --with-viral # + foreign ligandome (Kesmir arm)
|
| 193 |
+
python bench/neoag/deposit_corpora.py --ratio 5 --seed 20260817
|
| 194 |
+
python bench/neoag/audit_pmhc_data.py
|
| 195 |
+
|
| 196 |
+
`corpus_chowell.parquet` must keep its exact row counts (human 464,310 / 14,712 pos; mouse 47,203 /
|
| 197 |
+
5,154) — the shipped `mhcmatch.posbayes` tables are fitted on it, and folding extra sources into it
|
| 198 |
+
in place breaks that provenance silently. That is why `--with-viral` writes a separate file.
|
| 199 |
+
|
| 200 |
+
## `chowell_iedb_full.tsv.gz` / `chowell_iedb_full_matched.tsv.gz` (2026-08-18)
|
| 201 |
+
|
| 202 |
+
Rebuilt from the **full** IEDB export (build 2026-08-11) rather than `iedb_labeled.tsv.gz`. That
|
| 203 |
+
harmonised table kept only *positive* T-cell assays, so its `assay_type` is perfectly collinear with
|
| 204 |
+
its label and it cannot supply a measured negative at all; the full export carries 361,962 negative
|
| 205 |
+
T-cell assay rows, for mouse as well as human.
|
| 206 |
+
|
| 207 |
+
`chowell_rebuilt.tsv.gz` and `kesmir_rebuilt.tsv.gz` are **unchanged and still current** — the
|
| 208 |
+
shipped `mhcmatch.complement` and `posbayes` tables are fitted on them, they are named in the
|
| 209 |
+
library's `REFERENCE_FILES`, and every AUROC in `bench/results/complementarity.md` belongs to them.
|
| 210 |
+
These two files are additional, not replacements.
|
| 211 |
+
|
| 212 |
+
### The rules, in the order applied
|
| 213 |
+
|
| 214 |
+
1. MHC class I.
|
| 215 |
+
2. Host ∈ {human, mouse}; a column, never pooled.
|
| 216 |
+
3. Length 8–11, canonical 20 amino acids.
|
| 217 |
+
4. Exclude `SLLMWITQV`, `KLGGALQAK`.
|
| 218 |
+
5. **Immunogenic** — the peptide has ≥1 positive T-cell assay in that host (`Positive`,
|
| 219 |
+
`Positive-Low`, `-High`, `-Intermediate`).
|
| 220 |
+
6. **Non-immunogenic** — an eluted **self** ligand appearing in no positive T-cell assay in any
|
| 221 |
+
host. 2,086 rows are eluted but positive elsewhere and are excluded from the negatives rather
|
| 222 |
+
than counted as such.
|
| 223 |
+
7. The label is a property of `(peptide, host)`, never of an assay row.
|
| 224 |
+
8. The restriction is resolved, and **no row is dropped for lacking one**: 1,677,439 of 3,278,355
|
| 225 |
+
surviving class-I rows name only `HLA class I`. netMHCpan-4.2 picks the best `%Rank_EL` among
|
| 226 |
+
whatever the recorded string still permits — an allele group or serotype restricts to that
|
| 227 |
+
group, an H-2 haplotype to the molecules it carries, a bare `HLA class I` to a 30-allele
|
| 228 |
+
population panel built per locus. A **known** non-human/non-mouse MHC on a human or mouse host
|
| 229 |
+
(2,812 rows) is a curation error, not a missing value, and is excluded rather than imputed.
|
| 230 |
+
9. One row per `(peptide, allele group, host)`, label = max. The key is the allele **group**
|
| 231 |
+
(`HLA-A*02`), which is what lets serotype-only records join.
|
| 232 |
+
|
| 233 |
+
### Counts
|
| 234 |
+
|
| 235 |
+
| file | rows | positive | negative | peptides | allele groups |
|
| 236 |
+
|---|--:|--:|--:|--:|--:|
|
| 237 |
+
| `chowell_iedb_full` (human) | 789,047 | 24,293 | 764,754 | 579,322 | 75 |
|
| 238 |
+
| `chowell_iedb_full` (mouse) | 65,472 | 7,187 | 58,285 | 49,253 | 43 |
|
| 239 |
+
| `chowell_iedb_full_matched` (human) | 20,360 | 10,180 | 10,180 | 17,181 | 18 |
|
| 240 |
+
| `chowell_iedb_full_matched` (mouse) | 9,132 | 4,566 | 4,566 | 7,969 | 25 |
|
| 241 |
+
|
| 242 |
+
Columns add `allele_2f`, `allele_group`, `allele_source` ∈ {`reported`, `imputed_group`,
|
| 243 |
+
`imputed_panel`} and `allele_rank_el`, so an analysis can exclude imputed restrictions and say how
|
| 244 |
+
much sample it lost. Human: 375,462 reported against 413,585 imputed; mouse 51,003 against 14,469.
|
| 245 |
+
|
| 246 |
+
The matched arm resamples both classes to one profile at 1:1 so the allele group carries no signal
|
| 247 |
+
about the label — human to population frequency from `hla_pop_freqs.tsv.gz`, mouse to the positives'
|
| 248 |
+
own profile, there being no H-2 frequency table. It is **small**: 52 of 75 human allele groups are
|
| 249 |
+
too scarce to fill their population weight and are dropped, which is the cost of the matching, not a
|
| 250 |
+
bug.
|
| 251 |
+
|
| 252 |
+
Regenerate, in `~/vcs/projects/2026-mhcmatch-benchmark`:
|
| 253 |
+
|
| 254 |
+
python immunogenicity/fetch.py # stage the export from ~/hf/pmhc_data/dump
|
| 255 |
+
bench/neoag/corpus_iedb.py --workers 12 # ~55 min, 14.7M netMHCpan predictions
|
| 256 |
+
bench/neoag/deposit_arms.py
|
| 257 |
+
|
| 258 |
+
Arm counts, the filter cascade and the selection tree are `bench/results/corpus_arms.md`.
|
| 259 |
+
|
| 260 |
+
**Caveat carried forward.** These negatives are still *inferred* — an eluted ligand nobody has
|
| 261 |
+
tested is assumed non-immunogenic. 38,322 rows in the rebuild have a measured negative T-cell assay
|
| 262 |
+
instead, held under evidence `tcell_negative` and deliberately **not** merged into the arm: swapping
|
| 263 |
+
the negative definition changes what every AUROC recorded on these corpora means.
|
| 264 |
+
|
| 265 |
+
### `immunogenicity_legacy_arms.tsv.gz` (2026-08-19)
|
| 266 |
+
|
| 267 |
+
The published deposits and the legacy Kešmir arms in one table, keyed `corpus` ∈ {`chowell_vanilla`,
|
| 268 |
+
`kesmir_S1`, `kesmir_S2`, `kesmir_S3`}, so a held-out evaluation can be reproduced without the
|
| 269 |
+
copyrighted spreadsheets. 15,991 rows.
|
| 270 |
+
|
| 271 |
+
**Species is keyed on the restricting allele, not on the deposit's own `Species` column.** The two
|
| 272 |
+
disagree on 777 of the 2,508 Calis rows (31%), which carry `Species = Mus` with an HLA restriction —
|
| 273 |
+
HLA-transgenic mice, murine host and human restricting molecule. Both readings describe the
|
| 274 |
+
experiment; they build different corpora. Keyed on the host, the Calis human arm is 1,113
|
| 275 |
+
immunogenic against **one** non-immunogenic; keyed on the allele it is **1,619 against 272** and the
|
| 276 |
+
mouse arm is H-2 throughout. The reported host is retained as `host_reported`.
|
| 277 |
+
|
| 278 |
+
Regenerate with `bench/neoag/corpus_legacy.py` then `deposit_arms.py`.
|
| 279 |
+
|
| 280 |
+
### What the recognition model is fitted on
|
| 281 |
+
|
| 282 |
+
`chowell_iedb_full` is the training arm for `mhcmatch.recognition` (0.15.0). Two choices were made
|
| 283 |
+
by measurement and are recorded in `bench/results/recognition_model.md` and
|
| 284 |
+
`feature_importance.md`:
|
| 285 |
+
|
| 286 |
+
- **The unmatched arm ships.** Resampling negatives to match population HLA usage costs 0.019
|
| 287 |
+
(human) and 0.058 (mouse) AUROC on held-out published deposits.
|
| 288 |
+
- **ESM2 and the physicochemical features are both kept.** ESM2 adds +0.022/+0.042 over physchem
|
| 289 |
+
alone; physchem adds +0.009/+0.019 over ESM2 alone. Permutation importance on held-out data ranks
|
| 290 |
+
the two ESM face-pools first (0.19–0.27), then `kf_tcr`; the whole-peptide Kidera aggregate is
|
| 291 |
+
worth 0.0165/0.0012 and is not shipped, being exactly the sum of the two role columns.
|
| 292 |
+
|
| 293 |
+
## `staged/` — the IEDB full export, as staged (2026-08-19)
|
| 294 |
+
|
| 295 |
+
The **root of the provenance chain**. Every rebuilt corpus in this directory
|
| 296 |
+
(`chowell_iedb_full*`, `immunogenicity_legacy_arms`) is derived from these two tables, and until
|
| 297 |
+
now they lived only in a gitignored working directory — one bad checkout from being unreproducible.
|
| 298 |
+
Deposited so the chain closes without re-downloading 450 MB from IEDB.
|
| 299 |
+
|
| 300 |
+
| file | provenance | rows |
|
| 301 |
+
|---|---|--:|
|
| 302 |
+
| `staged/iedb_tcell.parquet` | **experimental** — IEDB `tcell_full_v3_tsv.zip`, build 2026-08-11, downloaded 2026-08-18 | 570,434 linear-peptide rows, 233,636 distinct peptides |
|
| 303 |
+
| `staged/iedb_ligand.parquet` | **experimental** — IEDB `mhc_ligand_full_tsv.zip`, same build | 5,773,495 linear-peptide rows, 1,704,996 distinct peptides |
|
| 304 |
+
| `staged/fetch.py` | the script that produced both | — |
|
| 305 |
+
| `staged/FETCH.md` | its record: zip mtimes, export row counts, alignment failures, class and qualitative-measurement breakdowns | — |
|
| 306 |
+
|
| 307 |
+
Ten columns are kept from each export; nothing is filtered beyond `Linear peptide` and a non-empty
|
| 308 |
+
name, because length, alphabet, class and host filters are corpus-construction rules and belong
|
| 309 |
+
where they can be stated as rules. **0 rows failed to align** in either table — IEDB ships two
|
| 310 |
+
header lines and embedded newlines in comment fields, so `fetch.py` reads with `csv.reader` rather
|
| 311 |
+
than splitting on tabs, and reports the misalignment count rather than assuming it is zero.
|
| 312 |
+
|
| 313 |
+
The T-cell export is what makes a *measured* negative possible: 361,962 `Negative` rows across all
|
| 314 |
+
hosts, where the older harmonised `iedb_labeled.tsv.gz` keeps only positive T-cell assays and its
|
| 315 |
+
`assay_type` is therefore perfectly collinear with its label.
|
| 316 |
+
|
| 317 |
+
Regenerate (needs `~/hf/pmhc_data/dump`, which is gitignored — the raw zips are not deposited):
|
| 318 |
+
|
| 319 |
+
```zsh
|
| 320 |
+
python immunogenicity/staged/fetch.py
|
| 321 |
+
```
|
| 322 |
+
|
| 323 |
+
## `corpus_iedb_mhc2.parquet` — the MHC class-II arm (2026-08-19)
|
| 324 |
+
|
| 325 |
+
**Derived/computed**, from `staged/iedb_tcell.parquet` and `staged/iedb_ligand.parquet` — the same
|
| 326 |
+
IEDB build 2026-08-11 export the class-I corpus is built from, by the same rules, so the two arms are
|
| 327 |
+
comparable by construction rather than by assertion. This is what the shipped class-II
|
| 328 |
+
complementarity model (`mhcmatch.complement`, `cls="mhc2"`, v0.16.0) is fitted on.
|
| 329 |
+
|
| 330 |
+
| field | value |
|
| 331 |
+
|---|--:|
|
| 332 |
+
| rows (peptide, allele group, host) | 1,096,034 |
|
| 333 |
+
| distinct peptides | 643,744 |
|
| 334 |
+
| immunogenic peptides | 77,943 |
|
| 335 |
+
| human rows | 1,036,041 |
|
| 336 |
+
| human positives | 65,486 |
|
| 337 |
+
| human distinct peptides | 603,781 |
|
| 338 |
+
| human allele groups | 121 |
|
| 339 |
+
| mouse rows | 59,993 |
|
| 340 |
+
| mouse positives | 12,457 |
|
| 341 |
+
| mouse distinct peptides | 50,258 |
|
| 342 |
+
| mouse allele groups | 40 |
|
| 343 |
+
| peptide length | 11–25, median 15 |
|
| 344 |
+
|
| 345 |
+
Three rules differ from the class-I arm and nothing else does:
|
| 346 |
+
|
| 347 |
+
- **Length** is 11–25 rather than 8–11.
|
| 348 |
+
- **Restriction is parsed, never imputed.** Class-II restriction is written a dozen ways across IEDB
|
| 349 |
+
(`HLA-DRB1*15:01`, `HLA-DPA1*01:03/DPB1*04:01`, `HLA-DR15`, serotypes, `H2-IAb`), so the corpus
|
| 350 |
+
carries a parser and keeps whatever it cannot resolve as an explicit `unresolved` group rather than
|
| 351 |
+
guessing an allele. It parses **1,003,554 / 1,974,413 rows (50.8 %)**; the remainder are retained,
|
| 352 |
+
not dropped, because the complementarity fit reads only peptide and label. There is therefore **one
|
| 353 |
+
class-II arm where class I has two** — no HLA-matched variant is built, since matching needs a
|
| 354 |
+
resolved allele on both sides.
|
| 355 |
+
- **Rule 8** (pathogen epitopes) is applied by parsing the source organism rather than by set-match.
|
| 356 |
+
|
| 357 |
+
`evidence` records why each row is what it is: `eluted_but_positive_elsewhere=5,305`,
|
| 358 |
+
`eluted_foreign=103,351`, `eluted_self=1,156,574`, `eluted_unknown=440,458`, `tcell_negative=110,312`,
|
| 359 |
+
`tcell_positive=158,413`.
|
| 360 |
+
|
| 361 |
+
`corpus_iedb_mhc2.log` is the generator's own run record, deposited beside it; the counts in the table
|
| 362 |
+
above are read from it. The build is deterministic — regenerated 2026-08-19 from a clean checkout, every
|
| 363 |
+
count reproduced exactly.
|
| 364 |
+
|
| 365 |
+
Regenerate (needs this mirror at `$MHCMATCH_PMHC_DIR`, and the benchmark repo):
|
| 366 |
+
|
| 367 |
+
```zsh
|
| 368 |
+
python bench/neoag/corpus_iedb.py --cls mhc2
|
| 369 |
+
```
|
| 370 |
+
|
| 371 |
+
---
|
| 372 |
+
|
| 373 |
+
## `neoprecis_anchor_triplets.parquet` — added 2026-08-24
|
| 374 |
+
|
| 375 |
+
14,140 rows × 12. Anchor / positive / negative peptide triplets by allele, with the mutated position
|
| 376 |
+
and the reference and alternate residue for each of the positive and negative members. A **motif
|
| 377 |
+
resource** rather than a screen: the triplets were constructed to train an allele-conditioned
|
| 378 |
+
recognition model, so `positive_peptide` and `negative_peptide` are **derived** designations, not
|
| 379 |
+
assay outcomes. `mhc` and `allele` are as-reported.
|
| 380 |
+
|
| 381 |
+
Deposited by `bench/neoag/ingest_vaccines.py` from Supplementary Data 3 of Lee KH, Sears TJ,
|
| 382 |
+
Zanetti M, Carter H, "NeoPrecis: enhancing immunotherapy response prediction through integration of
|
| 383 |
+
qualified immunogenicity and clonality-aware neoantigen landscapes", *Nat Commun* 2026;**17**(1),
|
| 384 |
+
doi:[10.1038/s41467-026-68651-6](https://doi.org/10.1038/s41467-026-68651-6),
|
| 385 |
+
PMID [41577704](https://pubmed.ncbi.nlm.nih.gov/41577704/), PMC12932759.
|
| 386 |
+
|
| 387 |
+
---
|
| 388 |
+
|
| 389 |
+
## `pathogen_tcell_{mhc1,mhc2}_{human,mouse}.tsv.gz` — the four pathogen fits' corpora (2026-09-05)
|
| 390 |
+
|
| 391 |
+
The training corpora for the `{cls}.{species}.pathogen` cells of `mhcmatch`'s aggregate scorer.
|
| 392 |
+
One row per **peptide**, not per assay: a peptide's assays are folded to a single label, and the
|
| 393 |
+
counts that produced it travel with it.
|
| 394 |
+
|
| 395 |
+
### Source — the full IEDB T-cell export, not a filtered download
|
| 396 |
+
|
| 397 |
+
`dump/tcell_full_v3_tsv.zip` (`tcell_full_v3.tsv`, 1,346,206,535 bytes, **577,219 assay rows**,
|
| 398 |
+
IEDB build dated 2026-08-11). This is the complete export. The earlier
|
| 399 |
+
`raw/immunogenicity/iedb_tcell_assays_29032026.tsv.gz` (397,648 rows) and
|
| 400 |
+
`raw/immunogenicity/mouse_datasets/iedb_mouse.tsv.zip` (25,160 rows, **all `Positive*`, all class
|
| 401 |
+
I**) are query-filtered downloads; the mouse one carries no negatives at all, which is why the
|
| 402 |
+
mouse cells looked unfittable until the full export was read.
|
| 403 |
+
|
| 404 |
+
The export carries a **two-row header** — row 1 is the block (`MHC Restriction`), row 2 the field
|
| 405 |
+
(`Name`) — so a column is identified by the *pair*, never by index. Three blocks repeat the same
|
| 406 |
+
field names (`Epitope`, `Related Object`, `Assay Antigen` each carry `Species IRI`).
|
| 407 |
+
|
| 408 |
+
> Vita R, Blazeska N, Marrama D, et al. **The Immune Epitope Database (IEDB): 2024 update.**
|
| 409 |
+
> *Nucleic Acids Res* 2025;53(D1):D436–D443.
|
| 410 |
+
> PMID [39558162](https://pubmed.ncbi.nlm.nih.gov/39558162/) ·
|
| 411 |
+
> doi:[10.1093/nar/gkae1092](https://doi.org/10.1093/nar/gkae1092)
|
| 412 |
+
|
| 413 |
+
Organism classification is **NCBI Taxonomy**, fetched by taxid from the export's own
|
| 414 |
+
`Epitope / Species IRI` (`NCBITaxon_<taxid>`) via E-utilities `efetch?db=taxonomy`; 897 of 897
|
| 415 |
+
distinct taxids resolved. Cached at `bench/pathogen/_work/lineage.parquet` in the benchmark repo.
|
| 416 |
+
|
| 417 |
+
### Filtering procedure, in order
|
| 418 |
+
|
| 419 |
+
1. **Source organism is a pathogen** — the NCBI lineage is one of `virus`, `bacteria`, `archaea`,
|
| 420 |
+
`fungi`, `protozoa` (Apicomplexa, Euglenozoa, Amoebozoa, Fornicata, Parabasalia,
|
| 421 |
+
Heterolobosea), `helminth` (Platyhelminthes, Nematoda, Acanthocephala). **Read off the tree,
|
| 422 |
+
never off the organism's name.** 577,219 → 363,242 rows.
|
| 423 |
+
Deliberately excluded, and each is a real category rather than noise: `mammal` (97,019 rows —
|
| 424 |
+
self and xeno), `arthropod` (26,679 — house-dust mite, cockroach), `plant` (23,854 — timothy
|
| 425 |
+
grass and the other pollens), `other_metazoa` (2,738 — **`Gallus gallus` ovalbumin, 1,650 rows,
|
| 426 |
+
and `Tetronarce californica` AChR, 915**, the two canonical model antigens), and `unassigned`
|
| 427 |
+
(63,556, of which 61,664 record no source organism at all).
|
| 428 |
+
2. **Linear peptide, canonical residues** — `Epitope / Object Type == "Linear peptide"` and the
|
| 429 |
+
sequence matches `^[ACDEFGHIKLMNPQRSTVWY]+$`, which drops modified and non-peptidic epitopes.
|
| 430 |
+
363,242 → 361,795.
|
| 431 |
+
3. **The assay is labelled** — `Positive`, `Positive-Low/Intermediate/High` → 1; `Negative` → 0;
|
| 432 |
+
nothing else exists in this column. 361,795 rows, **126,593 positive**. The grade survives as
|
| 433 |
+
the `grade` column (`Positive-High` 4 … `Positive-Low` 1, 0 for a negative) rather than being
|
| 434 |
+
collapsed away.
|
| 435 |
+
4. **The restriction resolves** — `MHC Restriction / Name` must resolve **exactly** to one groove
|
| 436 |
+
under `mhcmatch.pseudoseq.resolve_allele` *and* be present in the shipped panel, or name an H-2
|
| 437 |
+
haplotype (`H2-b class I`). 247 distinct names resolve onto the panel, 19 name a haplotype, 73
|
| 438 |
+
resolve off-panel (all `BoLA-*`, cattle), 286 do not resolve.
|
| 439 |
+
**`exact=True` is load-bearing.** `HLA-A2` is a serotype; `resolve_allele("HLA-A2", "mhc1")`
|
| 440 |
+
answers `("HLA-A2301", False)` — a *different molecule*, on 3,963 rows. An inexact hit here does
|
| 441 |
+
not raise and does not return nothing; it scores the wrong groove.
|
| 442 |
+
5. **Length window** — class I 8–11, class II 12–20. 17,306 rows fall outside.
|
| 443 |
+
6. **Fold to one row per peptide per cell** — `label = max` over the peptide's assays.
|
| 444 |
+
`n_assay`, `n_pos`, `n_neg` are kept so a stricter rule (majority, or drop-on-conflict) stays
|
| 445 |
+
available; 6,205 peptides across the four cells carry both a positive and a negative assay.
|
| 446 |
+
|
| 447 |
+
### The cells
|
| 448 |
+
|
| 449 |
+
| file | peptides | positive | prevalence | groove-specific | haplotype-only | allotypes |
|
| 450 |
+
|---|--:|--:|--:|--:|--:|--:|
|
| 451 |
+
| `pathogen_tcell_mhc1_human.tsv.gz` | 16,841 | 7,018 | 0.417 | 16,841 | 0 | 116 |
|
| 452 |
+
| `pathogen_tcell_mhc1_mouse.tsv.gz` | 10,445 | 2,203 | 0.211 | 8,306 | 2,139 | 8 |
|
| 453 |
+
| `pathogen_tcell_mhc2_human.tsv.gz` | 7,948 | 5,150 | 0.648 | 7,948 | 0 | 86 |
|
| 454 |
+
| `pathogen_tcell_mhc2_mouse.tsv.gz` | 11,732 | 3,326 | 0.283 | 3,948 | 7,784 | 7 |
|
| 455 |
+
|
| 456 |
+
Restricted to peptides whose restriction is not **exclusively** prediction-derived
|
| 457 |
+
(`MHC binding prediction`, `Statistically inferred by motif or alleles present`):
|
| 458 |
+
|
| 459 |
+
| cell | peptides | positive |
|
| 460 |
+
|---|--:|--:|
|
| 461 |
+
| `mhc1.human.pathogen` | 14,108 | 5,894 |
|
| 462 |
+
| `mhc1.mouse.pathogen` | 6,763 | 1,456 |
|
| 463 |
+
| `mhc2.human.pathogen` | 4,695 | 3,347 |
|
| 464 |
+
| `mhc2.mouse.pathogen` | 10,833 | 2,928 |
|
| 465 |
+
|
| 466 |
+
`mhc_evidence` ships as a column and the filter is the **caller's**, because a restriction that is
|
| 467 |
+
itself an MHC binding prediction is circular for a model whose leading term is `binder` — and it is
|
| 468 |
+
the single largest evidence code in `mhc1.mouse` (3,872 of 10,445 peptides).
|
| 469 |
+
|
| 470 |
+
### Schema
|
| 471 |
+
|
| 472 |
+
`peptide`, `label`, `n_assay`, `n_pos`, `n_neg`, `grade`, `alleles`, `haplotypes`,
|
| 473 |
+
`allele_source`, `mhc_evidence`, `assay_methods`, `source_species`, `kingdom`,
|
| 474 |
+
`source_molecules`, `pmids`, `length`. Multi-valued fields are `;`-joined and sorted.
|
| 475 |
+
|
| 476 |
+
**Not** the compendium's shared 19-column schema: these are peptide-level with assay counts, and
|
| 477 |
+
they carry no `mhc_a`/`mhc_b`/`affinity`.
|
| 478 |
+
|
| 479 |
+
### `allele_source` is a key, not a convention
|
| 480 |
+
|
| 481 |
+
A restriction is either an allotype (`H-2Kb`) or a haplotype (`H2-b class I`), and the two are
|
| 482 |
+
told apart by the `allele_source` column rather than by a rule the reader has to carry. Haplotype
|
| 483 |
+
rows keep the **letter** in `haplotypes` and leave `alleles` empty; expanding a haplotype to its
|
| 484 |
+
allotypes is a **model** decision and is not baked in. Two reasons it is not:
|
| 485 |
+
|
| 486 |
+
- The panel's own spelling differs between classes — class I is `H-2Kb`, class II is `H-2-IAb`,
|
| 487 |
+
and `resolve_allele` returns a third form (`H-2-Kb`). Every join here folds through
|
| 488 |
+
`normalize_allele` on both sides.
|
| 489 |
+
- A mechanical superscript expansion is **wrong for class II**: the panel carries `H-2-IEb`, but
|
| 490 |
+
the b haplotype does not express I-E. Class-I K/D/L expansion has no such problem. The
|
| 491 |
+
haplotype-restricted best-of arm measured in `bench/results/pathogen_allele_policy_arms.md`
|
| 492 |
+
is where that decision belongs.
|
| 493 |
+
|
| 494 |
+
### Two skews to check before pooling anything over these
|
| 495 |
+
|
| 496 |
+
- `mhc2.mouse`'s groove-specific rows are **3,474 of 3,957 allotype mentions `H-2-IAb` (87.8%)**.
|
| 497 |
+
A pooled null over this cell is the I-Ab motif. The haplotype rows are what carry d, k and s.
|
| 498 |
+
- `mhc1.human` is 5,237 `HLA-A*02:01` of 16,841 peptides (31.1%).
|
| 499 |
+
|
| 500 |
+
### Regenerate
|
| 501 |
+
|
| 502 |
+
```zsh
|
| 503 |
+
cd ~/vcs/projects/2026-mhcmatch-benchmark && source .venv/bin/activate
|
| 504 |
+
python bench/pathogen/extract_tcell.py # 577,219 x 15, ~9 s
|
| 505 |
+
python bench/pathogen/fetch_lineage.py # 897 taxids from NCBI, 5 efetch calls
|
| 506 |
+
python bench/pathogen/build_pathogen_corpus.py # writes the four files here
|
| 507 |
+
python bench/pathogen/proofread_pathogen_corpus.py # 10 invariants per cell, exits 1 on any
|
| 508 |
+
```
|
| 509 |
+
|
| 510 |
+
### What was fitted on them (added 2026-09-05, after the fits)
|
| 511 |
+
|
| 512 |
+
Three of the four cells became **shipped** `mhcmatch` artifacts in release **1.15.0**, completing
|
| 513 |
+
`rank.AGGREGATE_ARTIFACTS` at eight of eight `(cls, species, mode)` cells:
|
| 514 |
+
|
| 515 |
+
| corpus file | artifact | peptides fitted | positives | prevalence | allotypes | ROC-AUC | PR-AUC |
|
| 516 |
+
|---|---|--:|--:|--:|--:|--:|--:|
|
| 517 |
+
| `pathogen_tcell_mhc1_mouse.tsv.gz` | `aggregate_mhc1_mouse_pathogen.json` | 10,404 | 2,196 | 0.2111 | 8 | 0.5564 | 0.2665 |
|
| 518 |
+
| `pathogen_tcell_mhc2_human.tsv.gz` | `aggregate_mhc2_human_pathogen.json` | 7,946 | 5,148 | 0.6479 | 80 | 0.6064 | 0.7361 |
|
| 519 |
+
| `pathogen_tcell_mhc2_mouse.tsv.gz` | `aggregate_mhc2_mouse_pathogen.json` | 11,725 | 3,324 | 0.2835 | 7 | 0.6224 | 0.4098 |
|
| 520 |
+
| `pathogen_tcell_mhc1_human.tsv.gz` | `aggregate_mhc1_pathogen.json` (from 2026-09-06) | 16,790 | 7,002 | 0.4170 | 104 | 0.5988 | 0.5147 |
|
| 521 |
+
|
| 522 |
+
Both figures are in-sample, pooled off the logit, each against its own prevalence. Fitted peptide
|
| 523 |
+
counts are below the file's row counts because `drop_corpus_members` removes peptides that are exact
|
| 524 |
+
members of a corpus table the fit's own `C_corpus_*` channels read (36 on mouse class I, 51 on human
|
| 525 |
+
class I) and because a peptide whose candidate allotypes all fail to score is dropped.
|
| 526 |
+
|
| 527 |
+
**`mhc1.human.pathogen` was on the Kesmir corpus, and moved to this one on 2026-09-06.** Until
|
| 528 |
+
then the shipped fit for that cell stayed on `kesmir_rebuilt_hla_matched.tsv.gz`, whose negatives
|
| 529 |
+
are *eluted ligands with no positive T-cell record* -- presentation held fixed on both sides. Here a
|
| 530 |
+
negative was *assayed and did not respond*, of unknown presentation, so `binder` carries
|
| 531 |
+
presentation again: **+0.1454 against +0.3418** for the same cell on the two corpora. The move was
|
| 532 |
+
the author's call, taken with the refit that put every pathogen cell on one corpus and one balanced
|
| 533 |
+
design; the paragraph above this one described the earlier state and is corrected rather than
|
| 534 |
+
deleted, because a recorded number cites the corpus it was produced on.
|
| 535 |
+
|
| 536 |
+
Two things not to carry over from the Kesmir arm. Its PR-AUC is **not** comparable to this one --
|
| 537 |
+
0.0905 at prevalence 0.0691 against 0.5147 at 0.4146, which is 1.31x and 1.24x lift respectively,
|
| 538 |
+
i.e. the same. And its five terms are not these two: `C_phys_buried` and `C_corpus_thymus` were
|
| 539 |
+
each highly resolved there and are absent here, because `C_corpus_thymus` and `C_corpus_self`
|
| 540 |
+
correlate at +0.683 to +0.792 and the five-term fits were fitting their difference.
|
| 541 |
+
|
| 542 |
+
Fitted by `bench/pathogen/fit_tcell.py` in `2026-mhcmatch-benchmark`; per-cell reports at
|
| 543 |
+
`bench/results/pathogen_tcell_fit_{cls}_{species}.md`, the head-to-head at
|
| 544 |
+
`bench/results/pathogen_mhc1_human_v1_vs_v2.md`, and the shipped-artifact provenance in
|
| 545 |
+
`src/mhcmatch/data/PROVENANCE.md`.
|
| 546 |
+
|
| 547 |
+
```zsh
|
| 548 |
+
MHCMATCH_MODEL_RELEASE=1.15.0 python bench/pathogen/fit_tcell.py --cls mhc1 --species mouse
|
| 549 |
+
MHCMATCH_MODEL_RELEASE=1.15.0 python bench/pathogen/fit_tcell.py --cls mhc2 --species human
|
| 550 |
+
MHCMATCH_MODEL_RELEASE=1.15.0 python bench/pathogen/fit_tcell.py --cls mhc2 --species mouse
|
| 551 |
+
```
|
| 552 |
+
|
| 553 |
+
**One consequence for anyone scoring with these artifacts.** Each was fitted on the
|
| 554 |
+
best-**presenting** allotype among those the peptide's own record supports, so each declares
|
| 555 |
+
`allele_policy = {"kind": "panel", "select": "presentation", "source": "record"}` and `mhcmatch`
|
| 556 |
+
refuses a bare `rank pairs` run rather than letting the coefficients meet a shifted column.
|
| 557 |
+
`rank fasta --alleles A,B` and `rank pairs --allele-panel A,B` are the supported forms. Measured on
|
| 558 |
+
the fitted designs, the best-of-record `binder` sits a mean **−0.2146** (sd 0.5262) below the same
|
| 559 |
+
peptide's single-recorded-allele `binder` on `mhc1_human`, and −0.1403 / −0.1248 / −0.0533 on
|
| 560 |
+
`mhc1_mouse` / `mhc2_human` / `mhc2_mouse`; 62.3 % / 73.7 % / 63.5 % / 52.7 % of peptides carry
|
| 561 |
+
exactly one candidate and are unaffected.
|
| 562 |
+
|
| 563 |
+
## `pathogen_tcell_balanced_{mhc1,mhc2}_{human,mouse}.tsv.gz` — allele-balanced fitting sets (2026-09-05)
|
| 564 |
+
|
| 565 |
+
**Derived**, not experimental. One row per peptide, carrying the allotype it is attributed to, its
|
| 566 |
+
T-cell label, and a prior weight that removes the allotype-composition difference between the two
|
| 567 |
+
labels. Built from `pathogen_tcell_{mhc1,mhc2}_{human,mouse}.tsv.gz` (entry above) by
|
| 568 |
+
`bench/pathogen/netmhcpan_best.py --cls {mhc1,mhc2} --species {human,mouse} --min-records 5` in
|
| 569 |
+
`repseq/2026-mhcmatch-code`. Class I goes through NetMHCpan-4.2, class II through
|
| 570 |
+
NetMHCIIpan-4.3.
|
| 571 |
+
|
| 572 |
+
| column | meaning |
|
| 573 |
+
|---|---|
|
| 574 |
+
| `peptide` | the epitope, as deposited |
|
| 575 |
+
| `mhc_a` | NetMHCpan's own best allotype for that peptide, panel spelling |
|
| 576 |
+
| `label` | `pos` / `neg`, the max over that peptide's IEDB T-cell assays |
|
| 577 |
+
| `weight` | prior (case) weight for a weighted GLM; see below |
|
| 578 |
+
|
| 579 |
+
**Why the allele comes from NetMHCpan.** The balancing variable must not be the model's own choice.
|
| 580 |
+
`fit_tcell.score` picks the best-*presenting* allotype with mhcmatch, so weighting on it would
|
| 581 |
+
condition the design on the term being fitted (`binder`). NetMHCpan-4.2 is external to every term in
|
| 582 |
+
these fits, so its `%Rank_EL` argmin over each record's **own recorded candidate allotypes** is the
|
| 583 |
+
allele used. It is not a free argmin over the whole panel — that would invent a restriction the
|
| 584 |
+
record does not carry.
|
| 585 |
+
|
| 586 |
+
**The two classes spell an allotype differently, and the resolver tries both.** The class-II
|
| 587 |
+
deposits already store NetMHCIIpan's own names (`DRB1_0101`, `HLA-DPA10103-DPB10401`, `H-2-IAb`),
|
| 588 |
+
so putting them through the canonicaliser converts them *away* from what the tool accepts; class I
|
| 589 |
+
is the opposite, the deposit writes `HLA-A*02:01` and the tool wants `HLA-A02:01`. The builder
|
| 590 |
+
resolves from an ordered list — as deposited, then canonical — and prints which spelling answered.
|
| 591 |
+
All 116 human and 8 mouse class-I candidate allotypes and all 86 human and 7 mouse class-II ones
|
| 592 |
+
resolve; none is dropped for want of a name.
|
| 593 |
+
|
| 594 |
+
**Order: predict, then filter, then weight.** Every (peptide, candidate allotype) the record
|
| 595 |
+
supports goes through NetMHCpan — one call per allotype, never one per peptide. Each peptide takes
|
| 596 |
+
its `%Rank_EL` argmin. An allotype with fewer than 5 peptides **in either class** is then dropped
|
| 597 |
+
together with its peptides: a frequency ratio resting on one or two peptides on one side is noise,
|
| 598 |
+
and an allotype absent from a class has no ratio at all. 0 of 116 human and 0 of 8 mouse candidate
|
| 599 |
+
allotypes lack a NetMHCpan name; the thin-tail cut removes 64 of 107 human and 2 of 8 mouse
|
| 600 |
+
class-I best-allele allotypes, 53 of 82 human class-II ones, and 0 of 7 mouse class-II ones.
|
| 601 |
+
|
| 602 |
+
**The weight.** The target is the **pooled** allotype distribution `p(a) = n(a)/N` over both labels,
|
| 603 |
+
and each label is normalised against it: `w = p(a)/p_pos(a)` on positives and `p(a)/p_neg(a)` on
|
| 604 |
+
negatives. Each label's weights sum to its own count identically — `sum_a n_c(a)·p(a)/p_c(a) =
|
| 605 |
+
N_c·sum_a p(a) = N_c` — so the prevalence is unchanged and only the allotype mix within each label
|
| 606 |
+
moves. **Nothing is subsampled:** every peptide surviving the two filters is present, which is why
|
| 607 |
+
this ships as a weight column rather than as a matched subset.
|
| 608 |
+
|
| 609 |
+
This is the same operation as cross-batch V/J-gene usage correction in `vdjtools`
|
| 610 |
+
(`preprocess.correct_vj_usage`, batch → label, gene → allotype). Two deliberate differences: that
|
| 611 |
+
implementation targets the *geometric* grand mean of `log p` and keeps rare genes with a Laplace
|
| 612 |
+
pseudocount, where this targets the arithmetic pooled marginal and drops the thin tail; and its
|
| 613 |
+
`apply_vj_correction` roulette-wheel *resamples* the table, where this reweights it.
|
| 614 |
+
|
| 615 |
+
| file | rows | pos | neg | prevalence | allotypes | balanceable | rows at w=1 | min w | max w | max \|Δp\| all, raw | all, weighted | subset, weighted |
|
| 616 |
+
|---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
|
| 617 |
+
| `pathogen_tcell_balanced_mhc1_human.tsv.gz` | 16,790 | 7,002 | 9,788 | 0.4170 | 107 | 43 | 1,236 | 0.4596 | 7.0060 | 0.1255 | 0.0608 | 1.1e-16 |
|
| 618 |
+
| `pathogen_tcell_balanced_mhc1_mouse.tsv.gz` | 10,404 | 2,196 | 8,208 | 0.2111 | 8 | 6 | 6 | 0.2825 | 3.1045 | 0.0955 | 0.0017 | 1.6e-15 |
|
| 619 |
+
| `pathogen_tcell_balanced_mhc2_human.tsv.gz` | 7,946 | 5,148 | 2,798 | 0.6479 | 82 | 29 | 659 | 0.4208 | 7.0918 | 0.0616 | 0.0185 | 3.3e-16 |
|
| 620 |
+
| `pathogen_tcell_balanced_mhc2_mouse.tsv.gz` | 11,725 | 3,324 | 8,401 | 0.2835 | 7 | 7 | 0 | 0.4191 | 2.2146 | 0.2736 | 0.0000 | 1.1e-15 |
|
| 621 |
+
|
| 622 |
+
**Rebuilt 2026-09-06 under one weighting implementation, and the thin tail is now kept rather than
|
| 623 |
+
dropped.** These four files were built by an inline copy of `pooled_weights` that *dropped* an
|
| 624 |
+
unbalanceable allotype together with its peptides, while the two `neoag_tcell_balanced_*` files
|
| 625 |
+
below were built by the shared `balance.py`, which *keeps* them at weight 1. One entry in this file
|
| 626 |
+
described both. Every caller goes through `balance.py` now, at `drop_thin=False`, which readmits
|
| 627 |
+
1,236 / 6 / 659 / 0 peptides and 554 / 5 / 608 / 0 positives.
|
| 628 |
+
|
| 629 |
+
**So the residual over the FULL allotype set is not zero, and the honest claim is the narrow one:**
|
| 630 |
+
within the balanceable subset the two weighted distributions agree to 1e-15 or better in every
|
| 631 |
+
cell, and over all allotypes the imbalance falls but does not vanish -- 0.1255 to 0.0608 on human
|
| 632 |
+
class I, where 64 of 107 allotypes are thin. `mhc2.mouse` reaches exactly zero because it is the
|
| 633 |
+
one cell with no thin allotype at all. The largest raw imbalance is **mouse class II at 0.2736**,
|
| 634 |
+
the `H-2-IAb` dominance this compendium already documents on the ligand side.
|
| 635 |
+
|
| 636 |
+
Kish effective sample size, the price of the reweighting:
|
| 637 |
+
|
| 638 |
+
| file | positives, n → ESS | negatives, n → ESS |
|
| 639 |
+
|---|---|---|
|
| 640 |
+
| `..._mhc1_human` | 6,448 → 4,606 (71.4%) | 9,106 → 7,942 (87.2%) |
|
| 641 |
+
| `..._mhc1_mouse` | 2,191 → 2,025 (92.4%) | 8,207 → 8,078 (98.4%) |
|
| 642 |
+
| `..._mhc2_human` | 4,540 → 4,019 (88.5%) | 2,747 → 1,980 (72.1%) |
|
| 643 |
+
| `..._mhc2_mouse` | 3,324 → 2,839 (85.4%) | 8,401 → 8,135 (96.8%) |
|
| 644 |
+
|
| 645 |
+
The weights are consumed by `bench/pathogen/fit_tcell.py --balance`, which passes them as prior
|
| 646 |
+
weights to `bench/epic/optimize.py::irls(pw=)`. `pw=None` is bit-identical to the unweighted fit.
|
| 647 |
+
Per-allotype composition and weights are tabulated in
|
| 648 |
+
`bench/results/pathogen_tcell_allele_balance_{mhc1,mhc2}_{human,mouse}.md`.
|
| 649 |
+
|
| 650 |
+
**These four sets are what the 1.15.0 pathogen models are fitted on**, all four with `--balance`:
|
| 651 |
+
class I on `binder + C_corpus_self`, class II on `binder + C_phys_buried + C_phys_charge` (class II
|
| 652 |
+
has no corpus block — the mimicry tables are class-I TCR-face geometry).
|
| 653 |
+
|
| 654 |
+
## `neoag_tcell_balanced_mhc2_{human,mouse}.tsv.gz` — allele-balanced class-II neoantigen fitting sets (2026-09-06)
|
| 655 |
+
|
| 656 |
+
**Derived**, not experimental. The same weighting as the four `pathogen_tcell_balanced_*` files
|
| 657 |
+
above, on a different corpus and a different balancing variable. Built by
|
| 658 |
+
`bench/mhc2_neoag/allele_balance.py --species {human,mouse} --min-records 5` in
|
| 659 |
+
`repseq/2026-mhcmatch-code`, which calls the two class-II neoantigen fits' own loaders
|
| 660 |
+
(`fit_human_mhc2.load`, `fit_mouse.deposit` ∪ `fit_mouse.cedar`) rather than re-reading the
|
| 661 |
+
deposits, so each balanced corpus spans exactly the population its fit sees.
|
| 662 |
+
|
| 663 |
+
| column | meaning |
|
| 664 |
+
|---|---|
|
| 665 |
+
| `peptide` | the epitope, as deposited |
|
| 666 |
+
| `mhc_a` | the allotype the row is attributed to; **the deposit's own spelling**, see below |
|
| 667 |
+
| `label` | `pos` / `neg`, the deposited `immunogenicity` |
|
| 668 |
+
| `weight` | prior (case) weight for a weighted GLM; the pooled-marginal weight defined above |
|
| 669 |
+
|
| 670 |
+
**The unit is the `(peptide, allotype)` pair, and there is no NetMHCpan argmin here.** That is the
|
| 671 |
+
one substantive difference from the pathogen corpora, and it is a property of the deposits. The
|
| 672 |
+
pathogen deposits carry a peptide-level label and an unresolved *set* of candidate restrictions, so
|
| 673 |
+
an argmin resolves them. These deposits carry a measured restriction per assay: 589 of 804 human
|
| 674 |
+
peptides and 795 of 808 mouse ones appear with exactly one allotype, only 10 human and 25 mouse
|
| 675 |
+
peptides disagree on the label across their allotypes, and both fits already score each row against
|
| 676 |
+
its own allele. An argmin would collapse 1,112 human pairs to 804 and replace a *measured*
|
| 677 |
+
restriction with a predicted one.
|
| 678 |
+
|
| 679 |
+
| species | balancing variable | why it is external to every fitted term |
|
| 680 |
+
|---|---|---|
|
| 681 |
+
| human | CEDAR's deposited `allele`, DRA folded | an assay annotation; nothing mhcmatch computed |
|
| 682 |
+
| mouse | `mhc_a_pred` | already NetMHCIIpan-4.3's argmin over the strain's candidate alleles, with any reported allele added to the set — see "Predicted restriction" in the benchmark repo's `SOURCES.md` |
|
| 683 |
+
|
| 684 |
+
**The DRA fold is not tidiness.** `HLA-DRA*01:01/DRB1*04:04` and `HLA-DRB1*04:04` are one molecule —
|
| 685 |
+
DRA is effectively monomorphic — and as deposited they are two allotypes splitting one allotype's
|
| 686 |
+
counts, which is exactly what a thin-tail cut punishes. 72 spellings, 68 molecules.
|
| 687 |
+
|
| 688 |
+
**Three allele vocabularies now appear across the six balanced files, one per deposit, and each is
|
| 689 |
+
the spelling its own fit consumes.** The pathogen class-II files carry NetMHCIIpan's names
|
| 690 |
+
(`DRB1_0101`, `HLA-DPA10103-DPB10401`, `H-2-IAb`); this human file carries the panel spelling
|
| 691 |
+
(`HLA-DRB1*04:01`, `HLA-DQA1*03:01/DQB1*03:02`); this mouse file carries the deposit's
|
| 692 |
+
(`H2-IAb`, without the second hyphen). All 11 spellings across these two files resolve to an exact
|
| 693 |
+
match through `mhcmatch.pseudoseq.resolve_allele`; none is dropped for want of a name.
|
| 694 |
+
|
| 695 |
+
**The thin tail is kept at weight 1, not cut -- and this entry described a cut until 2026-09-06.**
|
| 696 |
+
An allotype with zero rows in one class cannot be balanced by any weight: the target `p_pool(a)` is
|
| 697 |
+
strictly positive while the achievable `p(a | that class)` is identically zero, and a Laplace
|
| 698 |
+
pseudocount makes the *ratio* finite without supplying the missing rows. There are then two ways
|
| 699 |
+
not to lose those rows, and the author's call is the second: drop them, or keep them at weight 1 and
|
| 700 |
+
compute the target marginal within the balanceable subset. **The files always held the second and
|
| 701 |
+
this entry always described the first**, so its counts (693 / 271 / 7 allotypes human, 538 / 214 / 4
|
| 702 |
+
mouse) were the cut that was never shipped, and its claim that both weighted distributions "are
|
| 703 |
+
equal to floating-point noise in every cell of both files" is true only *within* the balanceable
|
| 704 |
+
subset. The cascade a cut would have cost is reported at four thresholds in
|
| 705 |
+
`bench/results/neoag_tcell_allele_balance_mhc2_{human,mouse}.md`.
|
| 706 |
+
|
| 707 |
+
**The human corpus is where keeping rather than cutting matters most.** Whole allotypes in CEDAR
|
| 708 |
+
are all-positive — `HLA-DRB1*04:05` 31/31, `HLA-DRB1*07:01` 24/24, `HLA-DRB1*15:02` 22/22,
|
| 709 |
+
`HLA-DRB5*01:01` 17/17 — because a study that reports a responder's restriction reports no
|
| 710 |
+
negatives for it, while the two `DQB1*03:02` composites are ~90 % negative from a type-1-diabetes
|
| 711 |
+
screen. Allotype and label are confounded in the deposit, and those are exactly the allotypes no
|
| 712 |
+
weight can touch. Cutting them would have cost **59 % of the positives**; keeping them at weight 1
|
| 713 |
+
costs a residual imbalance of **0.1273** over all 72 allotypes instead, against 5.6e-17 within the
|
| 714 |
+
7 that can be balanced. Neither number alone describes the file, so both are in the table below.
|
| 715 |
+
|
| 716 |
+
| file | rows | pos | neg | prevalence | allotypes | balanceable | rows at w=1 | min w | max w | max \|Δp\| all, raw | all, weighted | subset, weighted | Kish n_eff |
|
| 717 |
+
|---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
|
| 718 |
+
| `neoag_tcell_balanced_mhc2_human.tsv.gz` | 1,112 | 656 | 456 | 0.5899 | 72 | 7 | 419 | 0.5281 | 4.2690 | 0.2427 | 0.1273 | 5.6e-17 | 830.7 (74.7 %) |
|
| 719 |
+
| `neoag_tcell_balanced_mhc2_mouse.tsv.gz` | 627 | 261 | 366 | 0.4163 | 7 | 4 | 46 | 0.5010 | 3.0430 | 0.2800 | 0.0994 | 1.1e-16 | 532.1 (84.9 %) |
|
| 720 |
+
|
| 721 |
+
**Nothing is retained-against: every row each fit sees is present.** 1,112 of 1,112 human rows and
|
| 722 |
+
627 of 627 mouse ones, all 656 and 261 positives, over all 72 and 7 allotypes. A cut at
|
| 723 |
+
`min_records = 5` would have kept 693 / 271 / 7 and 538 / 214 / 4.
|
| 724 |
+
positives (41.3 %) over 7 of 68 allotypes; mouse 538 of 572 rows (94.1 %) and 214 of 247 positives
|
| 725 |
+
(86.6 %) over 4 of 7 allotypes. The two weighted allotype distributions are equal to floating-point
|
| 726 |
+
noise in every cell of both files.
|
immunogenicity/chowell_iedb_full.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 7231939
|
immunogenicity/chowell_iedb_full_matched.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 266797
|
immunogenicity/chowell_rebuilt.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 4432513
|
immunogenicity/chowell_rebuilt_hla_matched.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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| 2 |
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|
| 3 |
+
size 928735
|
immunogenicity/corpus_iedb_mhc2.log
ADDED
|
@@ -0,0 +1,24 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# tcell: 570,434 staged rows
|
| 2 |
+
# class II: 247,456
|
| 3 |
+
# host human or mouse: 239,196
|
| 4 |
+
# 11-25mer, canonical: 221,787
|
| 5 |
+
# after artefact exclusions: 221,787 (115,263 peptides)
|
| 6 |
+
# ligand: 5,773,495 staged rows
|
| 7 |
+
# class II: 1,966,913
|
| 8 |
+
# host human or mouse: 1,905,785
|
| 9 |
+
# 11-25mer, canonical: 1,752,626
|
| 10 |
+
# after artefact exclusions: 1,752,626 (538,321 peptides)
|
| 11 |
+
# T-cell assayed (peptide, host) pairs: 119,615
|
| 12 |
+
# positive: 39,818 | measured negative: 79,797
|
| 13 |
+
# peptides positive in any host: 38,320
|
| 14 |
+
# elution pool by origin: foreign=145,186, self=1,165,489, unknown=441,951
|
| 15 |
+
# rows entering allele resolution: 1,974,413 (1,324,898 distinct peptide/host/restriction)
|
| 16 |
+
# class-II restriction parsed on 1,003,554/1,974,413 rows (50.8%); the rest are kept as `unresolved`
|
| 17 |
+
# top groups: unresolved=970,859, HLA-DRB1*15=174,134, HLA-DRB1*13=163,275, HLA-DRB1*01=105,737, HLA-DRB1*03=83,487, HLA-DRB1*04=52,007, HLA-DRB1*07=39,065, HLA-DPA1*01/DPB1*04=37,066
|
| 18 |
+
# evidence: eluted_but_positive_elsewhere=5,305, eluted_foreign=103,351, eluted_self=1,156,574, eluted_unknown=440,458, tcell_negative=110,312, tcell_positive=158,413
|
| 19 |
+
# out: 1,096,034 (peptide, allele group, host) rows over 643,744 peptides, 77,943 immunogenic
|
| 20 |
+
# human: 1,036,041 rows, 65,486 positive, 121 allele groups, 603,781 peptides
|
| 21 |
+
# length 11-25, median 15
|
| 22 |
+
# mouse: 59,993 rows, 12,457 positive, 40 allele groups, 50,258 peptides
|
| 23 |
+
# length 11-25, median 15
|
| 24 |
+
# -> /Users/mikesh/vcs/projects/2026-mhcmatch-benchmark/bench/neoag/corpus_iedb_mhc2.parquet (3s)
|
immunogenicity/corpus_iedb_mhc2.parquet
ADDED
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:63fe69c10d635f5c17bebae135bd390dc475ee7cffbde59e04c2dcdfca639708
|
| 3 |
+
size 8509426
|
immunogenicity/hla_pop_freqs.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 56665
|
immunogenicity/iedb_labeled.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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|
| 3 |
+
size 9833065
|
immunogenicity/immunogenicity_legacy_arms.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:76f0fc21892fdec615a3437e3f2e70575fc301bae4518ac5fd139c61f7cd93fb
|
| 3 |
+
size 119696
|
immunogenicity/kesmir_rebuilt.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:bf4dbaf44f0409109e21aa37502224c895ea29d668c31d711a7dd43b05a70c38
|
| 3 |
+
size 575885
|
immunogenicity/kesmir_rebuilt_hla_matched.tsv.gz
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
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oid sha256:c413096c1752caf14712700f896f49716c8d1cc8f941c0d832183c1338a47794
|
| 3 |
+
size 501103
|
immunogenicity/neoag_tcell_balanced_mhc2_human.tsv.gz
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ADDED
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ADDED
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immunogenicity/pathogen_tcell_balanced_mhc1_human.tsv.gz
ADDED
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immunogenicity/pathogen_tcell_balanced_mhc1_mouse.tsv.gz
ADDED
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immunogenicity/pathogen_tcell_balanced_mhc2_human.tsv.gz
ADDED
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immunogenicity/pathogen_tcell_balanced_mhc2_mouse.tsv.gz
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immunogenicity/pathogen_tcell_mhc1_human.tsv.gz
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