expression/SOURCES.md

expression/ — sources

Two human tables. reference_expression.tsv.gz holds three references that are never merged — different measurements, different units, and a gene-keyed table and a peptide-keyed one are not the same object.

file	reference_expression.tsv.gz
rows	6,681,814
schema	key, key_type, source, context, median_tpm, q25_tpm, q75_tpm, n
provenance	experimental (all three sources are measured RNA-seq); the per-context median/IQR/n are computed summaries of it

source	key_type	rows	contexts	what a row is
gtex	gene	3,955,284	53	a gene's expression across normal tissue — the safety read
hpa_consensus	gene	1,025,751	51	the same read over HPA's tissue panel — the only one that contains thymus
tcga	peptide	1,700,779	19	a mutant peptide's expression by tumour type — the target read

Total 6,681,814 rows.

key_type is the join key and it differs by source on purpose: GTEx and HPA answer "is this gene on in normal tissue", TCGA answers "is this neoantigen transcribed in this tumour". A caller that treats them as one column is asking a question none of them answers. GTEx and HPA are both gene-keyed but are still not interchangeable — see the units caveat in the HPA section below.

Read through mhcmatch.expression (lookup, tissues, tumor_types, safety_profile), which keeps them apart. Fetched by mhcmatch bootstrap --reference (~115 MB).

---

reference_expression_toil.tsv.gz — GTEx and TCGA on one pipeline, in one unit

Added 2026-08-26. This is the table to use when a normal tissue and a tumour type have to be compared, or when one supplies a scale for the other.

rows	5,037,966
schema	key, key_type, source, context, median_tpm, q25_tpm, q75_tpm, n, unit
keys	58,581 gene symbols (key_type = gene on every row)
source / contexts	toil_gtex — 53 GTEx tissues, 3,104,793 rows · toil_tcga — 33 TCGA studies, 1,933,173 rows
unit	TPM on every row
size	38.6 MB
provenance	experimental — RSEM TPM from RNA-seq; the per-context median/IQR/n are computed summaries
built by	bench/neoag/expression_toil.py in 2026-mhcmatch-benchmark

Why it exists. reference_expression.tsv.gz carries GTEx in TPM and TCGA in RSEM upper-quartile normalised counts, both written into a column called median_tpm. They are each correct for their own question, but they cannot be put on one axis: a scale taken from one and divided into an abundance from the other is a units error no caller can detect, because the column name says TPM in both cases. This table removes the problem at the source rather than by converting one into the other — a conversion that is not available from the deposited matrix, since RSEM normalised counts are proportional to abundance x effective length and the effective lengths are not published with it.

Origin. The UCSC Xena / Toil recompute, which put GTEx, TCGA and TARGET through a single RSEM pipeline against one annotation (GENCODE v23) and publishes gene-level TPM for all of them, so a GTEx tissue median and a TCGA study median differ only in the samples that went in.

input	file	bytes
expression matrix	TcgaTargetGtex_rsem_gene_tpm.gz	1,323,254,426
sample phenotype	TcgaTargetGTEX_phenotype.txt.gz	135,753
gene annotation	probeMap/gencode.v23.annotation.gene.probemap	3,244,244

base=https://toil-xena-hub.s3.us-east-1.amazonaws.com/download
curl -L -o xena/TcgaTargetGtex_rsem_gene_tpm.gz  $base/TcgaTargetGtex_rsem_gene_tpm.gz
curl -L -o xena/TcgaTargetGTEX_phenotype.txt.gz  $base/TcgaTargetGTEX_phenotype.txt.gz
curl -L -o xena/gencode.v23.annotation.gene.probemap \\
     "$base/probeMap%2Fgencode.v23.annotation.gene.probemap"
python bench/neoag/expression_toil.py --out expression/reference_expression_toil.parquet

Vivian J, Rao AA, Nothaft FA, Ketchum C, Armstrong J, Novak A, Pfeil J, Narkizian J, Deran AD, Musselman-Brown A, Schmidt H, Amstutz P, Craft B, Goldman M, Rosenbloom K, Cline M, O'Connor B, Hanna M, Birger C, Kent WJ, Patterson DA, Joseph AD, Zhu J, Zaranek S, Getz G, Haussler D, Paten B. Toil enables reproducible, open source, big biomedical data analyses. Nat Biotechnol 2017;35(4):314-316. PMID 28398314 · PMC5546205 · doi:10.1038/nbt.3772

Goldman MJ, Craft B, Hastie M, Repecka K, McDade F, Kamath A, Banerjee A, Luo Y, Rogers D, Brooks AN, Zhu J, Haussler D. Visualizing and interpreting cancer genomics data via the Xena platform. Nat Biotechnol 2020;38(6):675-678. PMID 32444850 · PMC7386072 · doi:10.1038/s41587-020-0546-8

The TCGA study code is joined through the barcode, never through the disease name. Xena labels a TCGA sample with a disease name (Uveal Melanoma); the rest of this compendium keys on the study abbreviation (UVM). A hand-written name->code crosswalk can be silently wrong, so the code is taken from PanCanAtlas merged_sample_quality_annotations.tsv on the 15-character sample barcode, which is exact. 17,474 of 19,131 matrix columns are used: 9,616 TCGA tumour aliquots and 7,862 GTEx samples. TARGET (734 columns) is dropped -- paediatric, and none of its diseases has a matched normal here -- as are TCGA adjacent normals and aliquots the source flags Do_not_use.

Upstream values are log2(TPM + 0.001) and are inverted on build, so what is stored is TPM.

Caveats.

• The GTEx contexts are Toil-era, not v11. reference_expression.tsv.gz is GTEx v11; this is the earlier release Toil reprocessed, so the two GTEx halves are neither the same samples nor the same depth. They are also not spelled the same: Toil writes Skin - Sun Exposed (Lower Leg) and GTEx v11 writes Skin - Sun Exposed (Lower leg). A context string cannot be carried from one table to the other without checking.
• Three tissues are thin and are kept with their n rather than dropped: Bladder (9), Cervix - Ectocervix (6), Fallopian Tube (5). They are the matched normals of BLCA, OV and CESC, so dropping them would remove the safety read for three tumour types. Cervix - Endocervix has fewer than five samples and is absent; CESC still resolves through Ectocervix. Every TCGA study clears 36.
• 353 of 58,581 symbols carry more than one Ensembl gene id (0.6 %). The row kept per (symbol, context) is the one with the highest median, not a sum across loci: a lookup asks whether a gene is on in this context, and the dominant locus answers it without inventing a total across paralogues. 164,862 duplicate rows were collapsed this way.
• n is a sample count here, as in the GTEx half, and not the transcript count the mouse table's n is.

What a floor built from it looks like, as the 25th percentile of non-zero gene medians: TCGA runs 0.1200 (KICH) to 0.3700 (LAML) TPM, a 3.08x spread over 33 studies; GTEx runs 0.1000 (Whole Blood) to 0.4000 (Testis), 4.00x over 53 tissues. Pooled, TCGA is 0.1800 TPM over 882,959 non-zero gene rows and GTEx 0.2700 over 1,402,705. A tumour's own floor is consistently below its matched normal's -- SKCM 0.1600 against skin 0.3050 (0.52x), BLCA 0.1700 against bladder 0.3600 (0.47x), LUAD 0.2000 against lung 0.3500 (0.57x), BRCA 0.1900 against breast 0.3000 (0.63x).

Format: parquet, and the record is not in the bootstrap set. As gzipped TSV this table was 70.5 MB and 0.5 s to read with polars (5.2 s with the standard library); dictionary-encoded and zstd-compressed it is 38.6 MB and 0.04 s. On a link measured here at 154 kB/s that is three and a half minutes of download saved, which is the difference between a bootstrap that finishes and one that looks like it has hung. mhcmatch bootstrap --reference stages only the three companions below — 6.6 MB between them, carrying the same numbers — and never this file.

Label arrays in the .npz are fixed-width unicode, not dtype=object. An object array is pickled, and np.load needs allow_pickle=True to read it back; a pickled array in a file fetched over the network executes on load. The deposit is written so that flag is never needed.

Its three companions, and why scoring never reads the table itself

Added 2026-08-26 beside it. reference_expression_toil.tsv.gz is the record; these are what a program actually opens.

file	size	what it is
toil_matrix.npz	6,632,665 B	the whole table as a dense 58,581 gene x 86 context float32 matrix, contexts keyed `"
toil_floors.tsv	8,365 B	88 rows -- each context's abundance floor at q05, q10, q25, plus a pooled row per cohort
context_synonyms.tsv	29,334 B	448 rows, 184 aliases -- how a free-text origin becomes a context

The matrix exists so a lookup does not cost a whole table. Answering what is this gene in this tumour type from the TSV means building a five-million-entry dictionary of dictionaries: measured, 5.20 s and 3,168 MB resident. The matrix is 0.05 s and 29 MB -- 100x the speed on 1/109 of the memory -- and every lookup a resolution chain makes is one array index. Both return the same 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 measured in every context that cleared the sample threshold, so absence is silence, and silence is a measurement. Whether a gene is in the reference at all is a separate question and is reported separately -- silence and ignorance must not collapse into one number.

context_synonyms.tsv is derived, not hand-written. Long format, one target per row, so it sorts and joins like data rather than holding lists in cells:

alias_kind	where the aliases come from	rows
code	the 33 TCGA studies in the matrix	83
site	Xena _primary_site, joined to the PanCanAtlas study code on the 15-character barcode	218
disease	Xena detailed_category, joined the same way	83
gtex	the 53 GTEx contexts in the matrix	64

One alias resolves to two things and each is its own row, which is why the alias count and the row count differ: 150 rows carry target_kind = tcga_code and 298 carry gtex_context, so hepatocellular names both LIHC and Liver. 152 of the 448 rows are derived:* and 296 are curated:*, and the provenance column says which on every row. The curated majority is the matched-normal half and the adjectival forms a free-text origin actually arrives in (cervical, pancreatic, uterine corpus); the derived half is what the matrix and the barcode join supply on their own. Hand-writing this would have been wrong in the places nobody checks: an organ is more than one study more often than not -- Lung is LUAD and LUSC, Kidney is three -- and a crosswalk that picks one is silently wrong for the rest. The tumour-to-matched-normal half is carried from mhcmatch.expression.TUMOR_TISSUE against the spellings this matrix uses, because Toil writes Brain - Frontal Cortex (Ba9) where GTEx v11 writes (BA9) and an exact match drops it.

An alias that does not resolve raises. Falling back to the pooled reference would return a plausible number computed from the wrong distribution with nothing downstream able to tell.

A note on what these floors are for. c is a property of a transcriptome, and the numerator is one candidate's abundance. Where the numerator is not in reference TPM the two stop cancelling, and the obvious repair -- a median of ratios against the reference -- must not be estimated from a candidate list. A mutation reaches such a list only if it was called, and only where the gene was seen in RNA, so the ratio measures that conditioning instead of the library: on three screens whose columns are all deposited as TPM it returns 1.78, 2.18 and 3.15, never below 1. mhcmatch gates it on coverage of the reference transcriptome for that reason, and a candidate list cannot clear it.

---

Mouse — reference_expression_mmu.tsv.gz and protein_abundance_mmu.tsv.gz

Added 2026-08-21. The compendium's normal-tissue safety read was GTEx, and GTEx is human, so a mouse epitope had no reference to be scored against. These two files are the mouse counterpart.

They are two files, not one, and that is deliberate. The human table's value column is literally median_tpm. RNA and protein are different measurements in different units, and writing a SILAC abundance into a _tpm column would be a units lie no caller could detect.

reference_expression_mmu.tsv.gz — the GTEx analogue

rows	659,050
schema	key, key_type, source, context, median_tpm, q25_tpm, q75_tpm, n — column for column identical to the human file
keys	18,830 gene symbols (key_type = gene)
contexts	35 adult tissues
source	fantom5_mouse
provenance	experimental — CAGE tag counts; the per-gene median across transcripts and the quantile summary are computed

Origin. EBI Expression Atlas E-MTAB-3579, the RIKEN FANTOM5 mouse CAGE atlas. q25_tpm / median_tpm / q75_tpm are taken from the Atlas' own five-number summary per gene × group; they are not re-derived here.

Lizio M, Harshbarger J, Shimoji H, Severin J, Kasukawa T, Sahin S, Abugessaisa I, Fukuda S, Hori F, Ishikawa-Kato S, Mungall CJ, Arner E, Baillie JK, Bertin N, Bono H, de Hoon M, Diehl AD, Dimont E, Freeman TC, Fujieda K, Hide W, Kaliyaperumal R, Katayama T, Lassmann T, Meehan TF, Nishikata K, Ono H, Rehli M, Sandelin A, Schultes EA, 't Hoen PAC, Tatum Z, Thompson M, Toyoda T, Wright DW, Daub CO, Itoh M, Carninci P, Hayashizaki Y, Forrest ARR, Kawaji H. Gateways to the FANTOM5 promoter level mammalian expression atlas. Genome Biol 2015;16:22. PMID 25723102 · doi:10.1186/s13059-014-0560-6

Only plain adult groups are kept — 35 of the deposit's 170. GTEx is adult normal tissue; folding an E14 embryo, a pregnancy timepoint or a lactating mammary gland into a "is this gene on in normal tissue" safety read answers a different question. The other stages are in the source and are recoverable by re-running without the filter.

A parsing trap worth recording. Atlas group labels are two ;-separated parts and the order is not fixed: 136 of the 170 are stage-first (adult; thymus) and 32 are tissue-first (adrenal gland; adult). Taking either position blindly mislabels a third of the table — the first build of this file produced contexts named adult and whole body that way. The stage is matched by vocabulary wherever it sits, and the tissue is whatever remains.

Checked against known biology, not assumed. Of 35 tissues, thymus ranks first for Prss16 (31 TPM), Psmb11 (88), Dntt (961), Rag1, Cd8a (131) and Lck (12) — thymic epithelium and thymocyte genes. As a negative control Cela1 peaks in pancreas at 225,010 TPM against 0.3 in thymus.

Caveats.

• n is 1 for every row. FANTOM5 provides one library per adult tissue, so q25/q75 describe the spread across transcripts of a gene, not across animals. GTEx n runs to several hundred donors. Do not read the two n columns as the same quantity.
• Aire is 0 TPM in thymus here, and that is a detection floor, not biology. Aire is expressed in a rare mTEC subset; bulk CAGE at this depth cannot see it. Any promiscuous-expression question needs sorted mTEC data, not this file.
• CAGE measures 5′-capped tag density. It is reported as TPM and behaves like expression, but it is not RNA-seq TPM and the two are not numerically interchangeable across sources.

tumor_expression_mmu.tsv.gz — the mouse TUMOUR read, and why it is a separate file

Added 2026-09-04. reference_expression_mmu.tsv.gz answers the safety half of the expression question -- is this gene on in normal tissue -- and there was no counterpart for the ranking half. Mouse has no TCGA, so a mouse neoantigen's own abundance fell back to the gene's normal-tissue median, which is the number expr_norm already carries. Measured on the 923 class-I rows of the IEDB mouse neoantigen deposit, expr_lvl and expr_norm came out identical on 100 % of the rows lacking a deposited abundance: two fitted terms on one column.

rows	149,640
schema	key, key_type, source, context, median_tpm, q25_tpm, q75_tpm, n — column for column identical to the two files above
keys	24,940 gene symbols (key_type = gene)
contexts	6 syngeneic tumour models: B16F10, CT26, E0771, LLC, MC38, Panc02
source	gse245293_syngeneic
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
provenance	experimental — RNA-seq counts; the length normalisation, the per-model quantiles and the gene-level aggregation are computed

Origin. NCBI GEO GSE245293, supplementary file GSE245293_Normalized_count_table_tumor.txt.gz (2.9 MB), staged at raw/expression_mouse/. 23 samples in the series; the 18 *_tumor_rep* columns are used and the 8 CAF (cancer-associated fibroblast) columns are not -- those are stroma, not the tumour transcriptome, and folding them in would answer a different question.

curl -O https://ftp.ncbi.nlm.nih.gov/geo/series/GSE245nnn/GSE245293/suppl/GSE245293_Normalized_count_table_tumor.txt.gz
python src/build_tumor_expression_mmu.py     # in ~/vcs/projects/public_expression_peptidomes

Comparison of syngeneic tumor models reveals different tumor microenvironments with distinct subsets of cancer-associated fibroblasts polarized by infiltrating T cells. GEO GSE245293, deposited 2023-10-13, released 2024-01-29. Mus musculus, expression profiling by high throughput sequencing.

Units are computed and the file says so. The deposit is normalised counts with a Length column, not TPM. Length-normalising each sample and rescaling to a million gives a TPM-like quantity comparable within this table; it is not interchangeable with FANTOM5 CAGE TPM or with GTEx RSEM TPM. mhcmatch.rank.expr_level is unit-free when its floor is a quantile of the same column, which is exactly why a floor for these contexts must be taken from this file.

Checked against known biology, not assumed. Three independent melanocytic markers separate the melanoma line by three orders of magnitude: Tyr 398.9, Dct 4,611.8 and Pmel 4,636.7 TPM in B16F10 against ≤ 4.3 in all five other models. They would all move together if the sample-to-model mapping were wrong.

Colon markers were tried and do not work here; do not re-try them. Muc2 reads ~0 in every model -- CT26 is an undifferentiated colon carcinoma and makes no goblet-cell mucin -- and Cdx1/Cdx2 do not separate CT26 and MC38 from the rest (Cdx2 is highest in Panc02, 8.8 against CT26's 1.4). The expectations were wrong, not the data.

Coverage of the IEDB mouse neoantigen deposit, joining on model × gene symbol: 347 of 968 class-I rows (36 %) and 304 of 522 class-II rows (58 %). The class-II figure is the reason this file exists -- that arm previously had a deposited abundance on 13 % of its rows.

Two models it does not carry that the neoantigen deposit names often: P815 (89 rows) and the T3/CMS5/Meth A fibrosarcomas (150 rows between them). Those still take the pooled floor.

PRJNA1271699 — located, not fetched

PRJNA1271699, "Mouse syngeneic model RNAseq sequencing" (CrownBio), submitted 2025-06-04: 120 runs over 12 models × 10 replicates -- A20, B16BL6, B16F10, CT26, EMT6, H22, Hepa16, LL2, MC38, Pan02, RM1, Renca -- paired-end Illumina NovaSeq 6000, 418 GB of raw reads. It would cover Renca and EMT6, which nothing here does.

NCBI carries raw reads only; there is no processed matrix. The BioProject abstract advertises an interactive resource at https://muscreen.crownbio.com/syngeneic_omics, and as of 2026-09-04 that URL is dead -- /syngeneic_omics returns 404 and the host root returns a bare Apache default page (403). So the processed tables the abstract promises are not currently retrievable, and using this project means downloading 418 GB and running an alignment pipeline. Recorded here so the cost is known rather than rediscovered.

This is the source to re-process if these models are wanted here -- it is the one that covers P815, Renca and EMT6, which tumor_expression_mmu.tsv.gz does not.

# metadata only, no reads:
curl -sS "https://trace.ncbi.nlm.nih.gov/Traces/sra-db-be/runinfo?acc=PRJNA1271699"

toil_matrix_mmu.npz + toil_floors_mmu.tsv — the harmonised mouse compendium

Added 2026-09-04. The mouse counterpart of toil_matrix.npz / toil_floors.tsv, same array names and the same source|context key shape, so code written against the human pair reads this one.

shape	26,737 genes x 68 contexts, dense float32
genes	official NCBI symbol, synonyms resolved via Ensembl release-100 (= GENCODE M25) and NCBI Gene
contexts	fantom5_mouse|<tissue> x 35 normal, gse245293|<model> x 6 tumour, prjna1271699+|<model> x 27 tumour
values	linear TPM-like, harmonised across sources; NaN = the gene is absent from that source, which is not the same as measured at zero
n_samples	3 for GSE245293 (replicate mean), 1 for FANTOM5, 0 where the source reports none
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

Sources. FANTOM5 mouse CAGE (EBI E-MTAB-3579); GEO GSE245293 (6 syngeneic models x 3 replicates); and the CrownBio syngeneic panel, labelled prjna1271699+ for NCBI BioProject PRJNA1271699 plus the further models that panel carries beyond the 12 the accession lists. GSE245293 wins the five models both cover. The 27 filled models include P815, Renca and EMT6, which no other deposit here reaches.

Harmonised, and here is what that means. The three sources sat on three scales -- pooled mean log2(1 + TPM) of 1.721, 2.513 and 2.059. Each source's non-zero values are quantile-mapped onto the mean of the three sources' quantile functions; per source, not per context, so between-context structure survives. Zero maps to zero. The map is monotone and its grid spans each source's support, so every within-context rank is preserved exactly -- which is what mhcmatch.rank.expr_level consumes. Non-zero spread falls 0.577 -> 0.087 log2 units, and the pooled q25 floor agrees to 0.714--0.717 across all three.

Two things it does not do. It cannot correct a gene-specific platform effect: Actb reads 3,032 in B16F10 and 10.4 in FANTOM5 thymus after harmonisation, because CAGE at that promoter genuinely ranks it mid-distribution. And it cannot correct the detection-rate difference (65.2 / 78.1 / 65.0 % non-zero), because a monotone map that respects zero cannot move it.

Absolute values are no longer any one source's own number. That is the trade: comparability across all 68 contexts, at the cost of each source's internal scale. The unharmonised per-source tables are not deposited; tumor_expression_mmu.tsv.gz and reference_expression_mmu.tsv.gz above remain the unharmonised reads of two of the three sources.

Built by src/build_deposit_mmu.py in ~/vcs/projects/public_expression_peptidomes; the protocol and its numbers are results/expression_harmonization.md there.

For comparison, the human pair needed none of this. Same measurement on toil_matrix.npz: toil_gtex and toil_tcga pooled means differ by 0.011 log2 units, against 0.565 for the mouse CAGE-vs-RNA-seq seam. Both human halves went through one Toil RSEM recompute, and it worked.

protein_abundance_mmu.tsv.gz — the protein-level read

rows	133,848
schema	key, key_type, source, context, abundance, unit, n
keys	5,148 gene symbols
contexts	26 tissues, including thymus
source / unit	geiger_silac_mouse / ppb_ibaq
provenance	experimental — SILAC-normalised MS abundance

Origin. EBI Expression Atlas E-PROT-11.

Geiger T, Velic A, Macek B, Lundberg E, Kampf C, Nagaraj N, Uhlen M, Cox J, Mann M. Initial quantitative proteomic map of 28 mouse tissues using the SILAC mouse. Mol Cell Proteomics 2013;12(6):1709–1722. PMID 23436904 · doi:10.1074/mcp.M112.024919

Why keep a protein table at all. A peptide can only be presented if the protein was translated, so for immunopeptidome work protein abundance is the more direct evidence and RNA is a proxy. It is much shallower — 5,148 genes against 18,830 — so it complements the RNA table rather than replacing it.

Why not E-PROT-13

E-PROT-13 (Huttlin's mouse phosphorylation and expression atlas) was the first candidate considered. It covers 9 tissues and does not include thymus, so it cannot serve either purpose here. Of the 65 mouse baseline experiments in Expression Atlas, only six contain thymus; E-MTAB-3579 (35 adult tissues) and E-PROT-11 (26) are the only two of those with broad tissue coverage.

Regenerate

cd ~/vcs/projects/public_expression_peptidomes && ./src/build_expression_mmu.py

---

Human — the hpa_consensus arm, and why GTEx alone was not enough

Added 2026-08-21, in the same pass that built the mouse references.

GTEx has no thymus. Its 53 contexts contain no thymus, no lymph node and no bone marrow — adult thymus involutes and is not routinely sampled, so the normal-tissue safety read was blind to the one tissue central tolerance actually happens in. Expression Atlas is no better: of its 123 human baseline experiments only three carry any thymus group, and all three are compromised for this purpose — E-MTAB-3871 is fetal tissue from fetuses with congenital defects, E-MTAB-3827 is sorted thymocyte subsets rather than tissue, and E-MTAB-9641 is thymic epithelium cultured on a 3T3-J2 feeder layer.

HPA samples thymus directly, so its consensus tissue set is what fills the hole.

rows	1,025,751
keys	20,151 gene symbols (key_type = gene)
contexts	51 tissues, including thymus
source	hpa_consensus
provenance	experimental — RNA-seq; the HPA consensus normalisation is computed

Origin. rna_tissue_consensus.tsv.zip from the Human Protein Atlas.

Uhlén M, Fagerberg L, Hallström BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson Å, Kampf C, Sjöstedt E, Asplund A, Olsson I, Edlund K, Lundberg E, Navani S, Szigyarto CA, Odeberg J, Djureinovic D, Takanen JO, Hober S, Alm T, Edqvist PH, Berling H, Tegel H, Mulder J, Rockberg J, Nilsson P, Schwenk JM, Hamsten M, von Feilitzen K, Forsberg M, Persson L, Johansson F, Zwahlen M, von Heijne G, Nielsen J, Pontén F. Tissue-based map of the human proteome. Science 2015;347(6220):1260419. PMID 25613900 · doi:10.1126/science.1260419

It is added as a third source, not merged into GTEx and not replacing it. The schema was designed with a source column for exactly this; every existing gtex and tcga row is untouched.

Checked against known biology. Of 51 tissues, thymus ranks first for PRSS16 (770 nTPM), PSMB11 (28.5), DNTT (306), CD1A (296), RAG1 (368), CCL25 (718), CD3E (596) and LCK (326). Negative controls behave: ALB peaks in liver at 198,524 nTPM against 0.3 in thymus, INS in pancreas, MYH7 in skeletal muscle.

Caveats.

• nTPM is not GTEx TPM. Both are TPM-family units but the normalisation differs, so compare within a source, not across. This is what the source column is for.
• n is 1 — HPA consensus publishes one value per gene × tissue, so q25_tpm and q75_tpm equal median_tpm and carry no spread. GTEx n runs to several hundred donors; the two n columns are not the same quantity.
• AIRE is detectable here (1.2 nTPM, rank 2 of 51) but reads 0 in the mouse CAGE atlas. That is a difference in assay depth, not in biology — do not read it as a species difference.