Archive 4 checkpoints (1-4)
Browse files- paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/README.md +126 -0
- paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/results/LARGE_OUTPUTS_SHA256.tsv +641 -0
- paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/results/TABLE_cv.csv +6 -0
- paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/tools/score_bp.py +377 -0
paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/README.md
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| 1 |
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# Bulk deconvolution with the same paired donors (BayesPrism, CIBERSORTx) vs PBISC LoRA
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Started 2026-10-06; results final 2026-10-08. CPU only, no GPU.
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User requests:
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- 2026-10-06: compare BayesPrism; then add CIBERSORTx and the paired COVID cohort (Imperial). The paired COVID cohort
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already in the benchmark was judged enough.
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- 2026-10-06 user decision: deconvolution methods go to the paper's Supplementary only, because PBISC generates single
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cells, a different task.
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Deck: two Supplementary slides in `presentations/2026-09-30--paper-summary--r00`, made by `tools/secS_deconvolution.py`:
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"Deconvolution with the same pairs: composition" and "...: disease signal".
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## Question and design
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Can standard bulk deconvolution, given the same paired donors that PBISC LoRA fine-tunes on, recover what PBISC
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recovers on the paired benchmark (`plans/paired_finetune_intradataset_20260927`)?
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| 19 |
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Everything except the method is the benchmark's own:
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| 20 |
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- **Splits** (`tools/common.py split_rows`)
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- 5-donor draws: reference = the draw's 5 training donors.
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- 5-fold CV: reference = each fold's LoRA training plus inner-validation rows (from its RUN.json).
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- Mixture = the test samples' real bulk raw counts.
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- Only paired samples are used.
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- **Truth and scorer**: score_ft.py functions imported unchanged, on the same donors, observed cells, case/control
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groups and `--exclude-genes` (Giroux: sex-linked genes excluded, from `scores_nosex/`).
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- **Gates** (fail closed):
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- The observed composition equals the stored per_donor values.
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- The recomputed bulk-broadcast logFC r and top-50 DEG overlap equal the stored values for every split.
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- Older pooled scorings have no DEG rows; for those, only the r gate applies.
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- **Cohorts**: ImMicro (healthy; composition only), sepsis (Ye 2026), Ji ChILI, Imperial ESKD COVID, Giroux.
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### Methods
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**BayesPrism** (`tools/run_bp.R`; R library `utility_studies_final6k_20260925/deconv/Rlib`):
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- Settings:
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- input.type GEP.
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- States = "six-type gate | AIFI L2", with states under 20 cells merged into "type|minor".
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- outlier.cut 0.01, outlier.fraction 0.1.
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| 40 |
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- Gene modes:
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| 41 |
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- `full` (main): BayesPrism's recommended set, canonical protein-coding minus chrX/Y/M and RPS/RPL/MRPS/MRPL,
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| 42 |
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measured by the sc assay; 15-19k genes.
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| 43 |
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- `score` (sensitivity): the scorer's ~5.5-6k scoring genes. This handicaps composition.
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| 44 |
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- Outputs: θ initial ("first", Gibbs) and θ updated ("final", after the reference update), plus Z (posterior
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| 45 |
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cell-type expression).
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- θ is in mRNA units. It is converted to cell units by dividing by each type's mean UMI per reference cell, then
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| 47 |
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renormalising.
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| 48 |
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**CIBERSORTx** (`tools/build_cibersortx_inputs.py`, `tools/run_cibersortx.sh`):
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| 50 |
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- Runs from the official Docker images `cibersortx/fractions` and `cibersortx/hires`.
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- The images are amd64-only. On server_1 (aarch64) they run under qemu binfmt emulation, registered with
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`tonistiigi/binfmt` (user-approved 2026-10-06; undo with `--uninstall amd64`).
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| 53 |
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- **Fractions** (single-cell reference mode with S-mode batch correction):
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| 54 |
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- Reference: up to 400 cells per type, seeded.
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| 55 |
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- Flags: `--fraction 0 --replicates 5 --sampling 0.5 --G.min 300 --G.max 500 --q.value 0.01 --QN FALSE`.
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| 56 |
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- **HiRes**:
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| 57 |
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- Runs on the scoring genes, with the Fractions signature and S-mode source GEPs.
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- HiRes regresses each gene on the 6 type fractions, so splits with 8 or fewer test samples are skipped. That
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| 59 |
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affects the Ji and Giroux CV folds.
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- HiRes imputes a different gene subset per type and writes exactly 1 in every sample for the other genes. The
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| 61 |
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scorer treats those rows as not expressed (zero) and not covered (`score_bp.py load_cx`).
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| 62 |
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- In 3 of 20 disease draws (Ji draw 3, Giroux draws 1 and 4), HiRes imputed no gene of the four contrast types.
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Those draws have no CIBERSORTx disease score.
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- **Credentials**:
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- Read at run time from `/home/sj_server_1/cibersortx.key` into a mode-600 env file that only the container reads.
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- The binary echoes its options, so its output is redacted before logging.
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- Checked 2026-10-08: no token or username in any plan file (counts only).
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| 69 |
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## Results (main: BayesPrism gene mode full)
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Full tables:
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- `results/RESULTS_full.md` (BayesPrism, main)
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| 73 |
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- `results/RESULTS_score.md` (BayesPrism, score-gene sensitivity)
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| 74 |
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- `results/RESULTS_cibersortx.md`
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| 75 |
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- Machine-readable versions: `results/TABLE_{fewshot,cv}_{full,score,cibersortx}.csv`
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- Per split: `results/<cohort>/<comparison>/{summary.json, per_donor.csv, contrast.csv}`
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| 77 |
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| 78 |
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Five-donor draws (25 draws; 20 with a case/control contrast). Number of draws where LoRA-5 beats the method:
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| 80 |
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| | composition L1 | logFC r | partial r beyond bulk | top-50 DEG |
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| 81 |
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|---|---|---|---|---|
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| BayesPrism (better θ) | 22/25 | 15/20 | 18/20 | 6/20 (BayesPrism ahead 8, ties 6) |
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| CIBERSORTx | 24/25 | 17/17 | 17/17 | 6/17 (many ties at 0) |
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- **Composition.** Deconvolution is far off in most cohorts. For example, in Giroux BayesPrism's L1 is 1.46 vs
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LoRA-5's 0.26, and CIBERSORTx's is 0.67.
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- **Pipeline check.** On sc pseudobulk mixtures, BayesPrism recovers the observed mRNA shares (Giroux T 0.386 vs
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| 88 |
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0.375; ImMicro T 0.54 vs 0.60). The error therefore comes from real bulk, not from the pipeline.
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- **Disease signal.** BayesPrism Z is ahead of LoRA-5 on logFC r only in Giroux, where the plain bulk broadcast is
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best of all. CIBERSORTx HiRes is below the broadcast everywhere.
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- **DEG.** Both deconvolutions are ahead on top-50 DEG in sepsis (27 and 25 vs 16); LoRA-5 is ahead in Imperial (114
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vs 72 and 31).
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- **5-fold CV, pooled.**
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- LoRA has the lowest composition L1 in ImMicro, sepsis, Ji and Imperial.
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- In Giroux, scSemiProfiler is slightly lower (0.243 vs 0.258).
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- scSemiProfiler's 5-fold column is filled from `scores[_nosex]/<ds>/scsemi_cv_20261002/pooled` as each cohort
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finishes; Imperial was still running on server_2 on 2026-10-08.
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| 98 |
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| 99 |
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## Rebuild
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```bash
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| 102 |
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P=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/bayesprism_paired_20261006
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| 103 |
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PY=/home/sj_server_1/miniforge3/envs/cxg_corpus/bin/python
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| 104 |
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# per cohort: aggregates and split inputs (CPU)
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| 105 |
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CUDA_VISIBLE_DEVICES= $PY $P/tools/build_aggregates.py DATASET full
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| 106 |
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CUDA_VISIBLE_DEVICES= $PY $P/tools/build_split.py DATASET {fewshot|cv} IDX full
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| 107 |
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GENES=full bash $P/tools/run_all.sh LANE CORES DATASET ... # BayesPrism, bounded units
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| 108 |
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CUDA_VISIBLE_DEVICES= $PY $P/tools/build_cibersortx_inputs.py DATASET
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| 109 |
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bash $P/tools/run_cibersortx.sh LANE CORES DATASET ... # CIBERSORTx (qemu on aarch64; hours per split)
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| 110 |
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bash $P/tools/finalize_full.sh # score + tables, BayesPrism (full and score modes)
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| 111 |
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RESCORE_FULL_CV="cohorts with new scSemiProfiler CV" bash $P/tools/finalize_cx.sh # CIBERSORTx (+ refresh CV)
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| 112 |
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```
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| 113 |
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| 114 |
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`remote_cache/` holds sha-checked copies of inputs that live only on spark-0110: condition NPZs and the Imperial
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| 115 |
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few-shot RUN.json (`common.local_copy`).
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| 117 |
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## Changes
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| 118 |
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- 2026-10-08:
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- `score_bp.py`:
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- HiRes placeholder rows (all exactly 1) are no longer read as expression.
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| 122 |
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- The covered variant is per type for CIBERSORTx.
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- Giroux's scSemiProfiler CV is read from `scores_nosex/`, matching LoRA's gene exclusion.
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| 124 |
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- The BayesPrism metrics were rechecked identical after the change.
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| 125 |
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- `aggregate_bp.py`: labels its table by method.
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| 126 |
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- New: `finalize_cx.sh`.
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paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/results/LARGE_OUTPUTS_SHA256.tsv
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| 1 |
+
path bytes sha256
|
| 2 |
+
cibersortx/giroux2022/fewshot0/out_hires/CIBERSORTxHiRes_NA_Bcell_Window7.txt 1312993 eaa56ecf767e2329391c6b2e59db3bce34fa5cd9750bb67e9872fe3eaf13945a
|
| 3 |
+
cibersortx/giroux2022/fewshot0/out_hires/CIBERSORTxHiRes_NA_DC_Window7.txt 1312993 eaa56ecf767e2329391c6b2e59db3bce34fa5cd9750bb67e9872fe3eaf13945a
|
| 4 |
+
cibersortx/giroux2022/fewshot0/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window7.txt 1327167 19ae1791ab65206dba654d70c1119f35bd5c010317c51ed015c4f48a5421e119
|
| 5 |
+
cibersortx/giroux2022/fewshot0/out_hires/CIBERSORTxHiRes_NA_NKcell_Window7.txt 1312993 eaa56ecf767e2329391c6b2e59db3bce34fa5cd9750bb67e9872fe3eaf13945a
|
| 6 |
+
cibersortx/giroux2022/fewshot0/out_hires/CIBERSORTxHiRes_NA_Other_Window7.txt 1312993 eaa56ecf767e2329391c6b2e59db3bce34fa5cd9750bb67e9872fe3eaf13945a
|
| 7 |
+
cibersortx/giroux2022/fewshot0/out_hires/CIBERSORTxHiRes_NA_Tcell_Window7.txt 1312993 eaa56ecf767e2329391c6b2e59db3bce34fa5cd9750bb67e9872fe3eaf13945a
|
| 8 |
+
cibersortx/giroux2022/fewshot1/out_hires/CIBERSORTxHiRes_NA_Bcell_Window7.txt 1201723 37575b31448559e05c96848ed6ee9ea35bfd4eb0b397cf560f7eb7253067eda1
|
| 9 |
+
cibersortx/giroux2022/fewshot1/out_hires/CIBERSORTxHiRes_NA_DC_Window7.txt 1201723 37575b31448559e05c96848ed6ee9ea35bfd4eb0b397cf560f7eb7253067eda1
|
| 10 |
+
cibersortx/giroux2022/fewshot1/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window7.txt 1201723 37575b31448559e05c96848ed6ee9ea35bfd4eb0b397cf560f7eb7253067eda1
|
| 11 |
+
cibersortx/giroux2022/fewshot1/out_hires/CIBERSORTxHiRes_NA_NKcell_Window7.txt 1201723 37575b31448559e05c96848ed6ee9ea35bfd4eb0b397cf560f7eb7253067eda1
|
| 12 |
+
cibersortx/giroux2022/fewshot1/out_hires/CIBERSORTxHiRes_NA_Other_Window7.txt 1201723 37575b31448559e05c96848ed6ee9ea35bfd4eb0b397cf560f7eb7253067eda1
|
| 13 |
+
cibersortx/giroux2022/fewshot1/out_hires/CIBERSORTxHiRes_NA_Tcell_Window7.txt 1201723 37575b31448559e05c96848ed6ee9ea35bfd4eb0b397cf560f7eb7253067eda1
|
| 14 |
+
cibersortx/giroux2022/fewshot2/out_hires/CIBERSORTxHiRes_NA_Bcell_Window7.txt 1424263 41fde745be73a8d9e68f620e3a39a5746ee3ff827c5c4f3b91b8e17968d9f640
|
| 15 |
+
cibersortx/giroux2022/fewshot2/out_hires/CIBERSORTxHiRes_NA_DC_Window7.txt 1424263 41fde745be73a8d9e68f620e3a39a5746ee3ff827c5c4f3b91b8e17968d9f640
|
| 16 |
+
cibersortx/giroux2022/fewshot2/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window7.txt 1424263 41fde745be73a8d9e68f620e3a39a5746ee3ff827c5c4f3b91b8e17968d9f640
|
| 17 |
+
cibersortx/giroux2022/fewshot2/out_hires/CIBERSORTxHiRes_NA_NKcell_Window7.txt 1424263 41fde745be73a8d9e68f620e3a39a5746ee3ff827c5c4f3b91b8e17968d9f640
|
| 18 |
+
cibersortx/giroux2022/fewshot2/out_hires/CIBERSORTxHiRes_NA_Other_Window7.txt 1424263 41fde745be73a8d9e68f620e3a39a5746ee3ff827c5c4f3b91b8e17968d9f640
|
| 19 |
+
cibersortx/giroux2022/fewshot2/out_hires/CIBERSORTxHiRes_NA_Tcell_Window7.txt 1467508 048a8e7151447051bc514a6814bbef92109cff2e3412e32492aff39dae91c1ca
|
| 20 |
+
cibersortx/giroux2022/fewshot3/out_hires/CIBERSORTxHiRes_NA_Bcell_Window7.txt 1312993 f364d5a1eda2675c189478349b76545f01d9eec229da0d9a3b0d73efac296bbb
|
| 21 |
+
cibersortx/giroux2022/fewshot3/out_hires/CIBERSORTxHiRes_NA_DC_Window7.txt 1312993 f364d5a1eda2675c189478349b76545f01d9eec229da0d9a3b0d73efac296bbb
|
| 22 |
+
cibersortx/giroux2022/fewshot3/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window7.txt 1329812 a945f8b4a1c527bdadb3d5a71f814a143925026f2f2e43ece6d8f01df4169b87
|
| 23 |
+
cibersortx/giroux2022/fewshot3/out_hires/CIBERSORTxHiRes_NA_NKcell_Window7.txt 1312993 f364d5a1eda2675c189478349b76545f01d9eec229da0d9a3b0d73efac296bbb
|
| 24 |
+
cibersortx/giroux2022/fewshot3/out_hires/CIBERSORTxHiRes_NA_Other_Window7.txt 1312993 f364d5a1eda2675c189478349b76545f01d9eec229da0d9a3b0d73efac296bbb
|
| 25 |
+
cibersortx/giroux2022/fewshot3/out_hires/CIBERSORTxHiRes_NA_Tcell_Window7.txt 1359854 8949165a4299cee1a48dd778e6a2c480dfb3e93208b246376d8aaf7969b441f4
|
| 26 |
+
cibersortx/giroux2022/fewshot4/out_hires/CIBERSORTxHiRes_NA_Bcell_Window7.txt 1201722 9ad2e64f8ea78c70fecf180bae241ab739b3843e63cf0ac2019ff94b8011c3ba
|
| 27 |
+
cibersortx/giroux2022/fewshot4/out_hires/CIBERSORTxHiRes_NA_DC_Window7.txt 1201722 9ad2e64f8ea78c70fecf180bae241ab739b3843e63cf0ac2019ff94b8011c3ba
|
| 28 |
+
cibersortx/giroux2022/fewshot4/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window7.txt 1201722 9ad2e64f8ea78c70fecf180bae241ab739b3843e63cf0ac2019ff94b8011c3ba
|
| 29 |
+
cibersortx/giroux2022/fewshot4/out_hires/CIBERSORTxHiRes_NA_NKcell_Window7.txt 1201722 9ad2e64f8ea78c70fecf180bae241ab739b3843e63cf0ac2019ff94b8011c3ba
|
| 30 |
+
cibersortx/giroux2022/fewshot4/out_hires/CIBERSORTxHiRes_NA_Other_Window7.txt 1201722 9ad2e64f8ea78c70fecf180bae241ab739b3843e63cf0ac2019ff94b8011c3ba
|
| 31 |
+
cibersortx/giroux2022/fewshot4/out_hires/CIBERSORTxHiRes_NA_Tcell_Window7.txt 1201722 9ad2e64f8ea78c70fecf180bae241ab739b3843e63cf0ac2019ff94b8011c3ba
|
| 32 |
+
cibersortx/imperial_eskd/cv0/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 6008303 a1ed189a103ff5f9581ffbdd1b00e7baf1d6646f01edb9eaeed3e47ac67982b9
|
| 33 |
+
cibersortx/imperial_eskd/cv0/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 5781743 4f1b7e43bd150e0d67c60baba018905ea4b3e3fa6eeb5ff01b1dc039e5f5b26b
|
| 34 |
+
cibersortx/imperial_eskd/cv0/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 5892794 a246a5c9aaa9087ea35563d16183ca7f5b3bc69b17cfa04e3798b478ebeded3f
|
| 35 |
+
cibersortx/imperial_eskd/cv0/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 5773464 be84c09125fcbfdddc215aa686e48cbec5880c85cc389a4190f6ed1ac8218910
|
| 36 |
+
cibersortx/imperial_eskd/cv0/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 5707974 76e3adc5f6ba4fb42e7ab52909bdb8fc058c1ef03329e19444cb2a10cf9ff4f3
|
| 37 |
+
cibersortx/imperial_eskd/cv0/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 6184483 2c306f23969db8f4220641d68f977f337bbdeaa9e01518ace6b8ca703749f39c
|
| 38 |
+
cibersortx/imperial_eskd/cv1/out_hires/CIBERSORTxHiRes_NA_Bcell_Window13.txt 3058589 08f8f18d641bfa610b1a14d73b0576584e4773f4c49affe7cae33c6321f29443
|
| 39 |
+
cibersortx/imperial_eskd/cv1/out_hires/CIBERSORTxHiRes_NA_DC_Window13.txt 3190732 6c44943ecf6f7e09c819058572b903d50990777a46053390cd07898a60075665
|
| 40 |
+
cibersortx/imperial_eskd/cv1/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window13.txt 3165725 433706ca866bb79e751324602b0199b9c1a52aa85a549bb17e5bc23c18ef03b8
|
| 41 |
+
cibersortx/imperial_eskd/cv1/out_hires/CIBERSORTxHiRes_NA_NKcell_Window13.txt 3160306 b4cb6036073b5b013f449b3bb020682b70850d83e4641d61a1c7789ebcdb00c0
|
| 42 |
+
cibersortx/imperial_eskd/cv1/out_hires/CIBERSORTxHiRes_NA_Other_Window13.txt 3111841 ea74d6bc3a6b0f59b9ee3dcb013104a0b579f33a57953d8be558d48a16edc70e
|
| 43 |
+
cibersortx/imperial_eskd/cv1/out_hires/CIBERSORTxHiRes_NA_Tcell_Window13.txt 3319937 0b07087bed833d5e5f29ccc2c37214cf7d723dc8190303ec63482e02e1b73f94
|
| 44 |
+
cibersortx/imperial_eskd/cv2/out_hires/CIBERSORTxHiRes_NA_Bcell_Window15.txt 3562404 b0166be9b50d502c5e05cdd60bd74fb135b87dccf639e587f4047334c178e126
|
| 45 |
+
cibersortx/imperial_eskd/cv2/out_hires/CIBERSORTxHiRes_NA_DC_Window15.txt 3532787 fc029dc1bf65352bd740d3cb40a22a797ae01f1fa66bdd7d21375ab6a6716ca1
|
| 46 |
+
cibersortx/imperial_eskd/cv2/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window15.txt 3735236 6bc53c097ba00ec262242a322902bd676d9528b262f1c69670adb5438d9dfffe
|
| 47 |
+
cibersortx/imperial_eskd/cv2/out_hires/CIBERSORTxHiRes_NA_NKcell_Window15.txt 3540640 e642712dd2ed3f22ac90f7436b5d0a4860564ea5d5e30933629ee7eb48c19d71
|
| 48 |
+
cibersortx/imperial_eskd/cv2/out_hires/CIBERSORTxHiRes_NA_Other_Window15.txt 3600368 405f38ec55ea331b9ee53744f841f36e486ce0c2ccbdc00c762ddbe667c7ed37
|
| 49 |
+
cibersortx/imperial_eskd/cv2/out_hires/CIBERSORTxHiRes_NA_Tcell_Window15.txt 3836566 1efbc9a526d820a0e6f04b5c2f14509c3f3c60526144d8a9b73214c71f93cc44
|
| 50 |
+
cibersortx/imperial_eskd/cv3/out_hires/CIBERSORTxHiRes_NA_Bcell_Window12.txt 3035142 70128206a40b6487585d4a2f5a0bb9b0b8c5a2e3c8d5cc50873996094a1d6d10
|
| 51 |
+
cibersortx/imperial_eskd/cv3/out_hires/CIBERSORTxHiRes_NA_DC_Window12.txt 3092502 e21d8a1d01525649034be5c306ea328313c8d35bcfbe3e6bf56e457739584fd8
|
| 52 |
+
cibersortx/imperial_eskd/cv3/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window12.txt 3019768 0712817cf71cb470ca701f96cff1256a676268eefd759f3858e1ba9e04913aef
|
| 53 |
+
cibersortx/imperial_eskd/cv3/out_hires/CIBERSORTxHiRes_NA_NKcell_Window12.txt 3027526 38f01c61d87922c88a8c32107e2ec9abcf9b38ae9c5f60317f40cf4e7e9e34c3
|
| 54 |
+
cibersortx/imperial_eskd/cv3/out_hires/CIBERSORTxHiRes_NA_Other_Window12.txt 2941584 d73792539bf8c323776fd83bb962bb8b1206b689d62cc0b0eb2ff0d34f87d3e6
|
| 55 |
+
cibersortx/imperial_eskd/cv3/out_hires/CIBERSORTxHiRes_NA_Tcell_Window12.txt 3176800 fb2b577e4ff2f231b5667d3caeb0fbbc9db6d6be6a6566bb2e71c5b8b9d4f5cf
|
| 56 |
+
cibersortx/imperial_eskd/cv4/out_hires/CIBERSORTxHiRes_NA_Bcell_Window16.txt 4147669 7336c10016c6b91046d340beeeb9f333ede71023dfcf452018f69b1f8da45922
|
| 57 |
+
cibersortx/imperial_eskd/cv4/out_hires/CIBERSORTxHiRes_NA_DC_Window16.txt 3926149 6c0c84540f77770d984cb47996f7bf66a243c6454c3513e95f4d357ec9a6a032
|
| 58 |
+
cibersortx/imperial_eskd/cv4/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window16.txt 4128731 1e092b26ce1ef101fd4dba942cda4c4a195997ec93a3a7a3f9183478827ce08c
|
| 59 |
+
cibersortx/imperial_eskd/cv4/out_hires/CIBERSORTxHiRes_NA_NKcell_Window16.txt 3917298 5f22b11e64446cabe58a146715adab956cee5f3f875c9835a2f0acd4aa3f0595
|
| 60 |
+
cibersortx/imperial_eskd/cv4/out_hires/CIBERSORTxHiRes_NA_Other_Window16.txt 3899452 ac3ba2651b62e65a512a6d8f85e5903b6a562c23a08ae063b9fbcb504abd1e8f
|
| 61 |
+
cibersortx/imperial_eskd/cv4/out_hires/CIBERSORTxHiRes_NA_Tcell_Window16.txt 4202953 ada0babcf51663d51b17212d3d1dc9d415d5f457e9f2885ad57722a068b11aee
|
| 62 |
+
cibersortx/imperial_eskd/fewshot0/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 17622085 5813216a3e2c2de82f44f92b0613b146d4118fd70493e310ae79270c6ee717af
|
| 63 |
+
cibersortx/imperial_eskd/fewshot0/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 17569343 466cdad9652eb43ef9029961070e1cb47bcc767fa5a2506ab2d66252beb4fffa
|
| 64 |
+
cibersortx/imperial_eskd/fewshot0/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 18111971 c990d7e35e4c91f47a4def38fc12995ae828f230e519708abc5e57d70f861aee
|
| 65 |
+
cibersortx/imperial_eskd/fewshot0/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 17653822 3dfc4912cb26f1f0adce6c8eed4475c129d7fe43d7aaa19ccfd1738ca488c8f1
|
| 66 |
+
cibersortx/imperial_eskd/fewshot0/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 17154500 f9cea4c94de7f3ed8d67c878150fbcb18c7e77e2cef4d153e5560925148d3617
|
| 67 |
+
cibersortx/imperial_eskd/fewshot0/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 18508072 377d151afd561be779cddd6b5f92b43a5676ef558d6d185bed74db5ba42bd24b
|
| 68 |
+
cibersortx/imperial_eskd/fewshot1/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 17864748 e7b9f7fbf463035a8ed2b06a5a70c71e810909e2cc596bda7cd0133b37864884
|
| 69 |
+
cibersortx/imperial_eskd/fewshot1/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 17773940 5379316db1708b008cd9433a7d419a2fe73cbb70be6b171dc859e641bd9e5398
|
| 70 |
+
cibersortx/imperial_eskd/fewshot1/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 18241543 35aac9f62770968b7811de468d1081de0f2f1f5e036c5606ae6d15bf66579da6
|
| 71 |
+
cibersortx/imperial_eskd/fewshot1/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 17765397 ebe2d9dc67b4bfc8f2f51a67bacb9a810652de5066783025b3c57b6155aebb82
|
| 72 |
+
cibersortx/imperial_eskd/fewshot1/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 17255622 e4166258763635bb3fe7df921aa0512cffac352ff4582d292f731bafb523a6d1
|
| 73 |
+
cibersortx/imperial_eskd/fewshot1/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 18666039 441a7f7b03d6c6485738a193345da82f5320c06b78f96e29bc36c84d8d811446
|
| 74 |
+
cibersortx/imperial_eskd/fewshot2/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 17885433 efcd91ede1ed6b6afda3b5f75af7880bf252b97daa6a85bd05cdfa924fe95be1
|
| 75 |
+
cibersortx/imperial_eskd/fewshot2/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 17655736 9e98f0ec7d11560f7c0baf4f48c99a37e1f2b87ef410544f210a63b19d200b6a
|
| 76 |
+
cibersortx/imperial_eskd/fewshot2/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 18061664 bc3ecff0f04f2b501f9a76f0d5b104ff459823acde065f6ece33fb4168a383f2
|
| 77 |
+
cibersortx/imperial_eskd/fewshot2/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 17808535 d3167277203705ab2e9ad526bc1de76d819f6754e38fb0f736cb3528fbb44a4f
|
| 78 |
+
cibersortx/imperial_eskd/fewshot2/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 17163057 9e1715478af8da3eaf2d5c1ccfdc4a8223d945f2514312efa63c54a7ab9d67e3
|
| 79 |
+
cibersortx/imperial_eskd/fewshot2/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 18453176 3c000911bc7b68d0b06c744e4f891df02c0a8fc89d9cc16b6ea91651179f0eb8
|
| 80 |
+
cibersortx/imperial_eskd/fewshot3/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 17929456 3245f08f670cf18502256f50e98cccbd1b668a7f0cb9939fc66c74d73b2d3ab0
|
| 81 |
+
cibersortx/imperial_eskd/fewshot3/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 18111943 191e958b9b2cfaab60facb1f5370fcfcb76cbe345911d261f4d56129667fc8c3
|
| 82 |
+
cibersortx/imperial_eskd/fewshot3/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 18445735 afe55612cbead8ca9b390139d1d00b60ca738f0a79f0377c1131fd50c66c8274
|
| 83 |
+
cibersortx/imperial_eskd/fewshot3/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 18304201 4f77e46d46d823f370c0c725863e07cd1b6834c1a7573a7dca003fcff12fa2ce
|
| 84 |
+
cibersortx/imperial_eskd/fewshot3/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 17494676 f28cdf4dfbc29c25daa2a4bf93739d2b1b295a1b14e026f6a9387832506c2d5b
|
| 85 |
+
cibersortx/imperial_eskd/fewshot3/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 18876385 e398053ff4a7e0407cf16e2c49624456850ebed20ff03e1371d43ccb94683f46
|
| 86 |
+
cibersortx/imperial_eskd/fewshot4/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 18031614 30eac6e697e52723a94cf6f79a83dd2e0e30ad748ed7fda6ce7aa2179fd8d9d6
|
| 87 |
+
cibersortx/imperial_eskd/fewshot4/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 17941388 3ace866bc8920517cb518efc862d6193b8875338a31aaa190b6c79d9991a8e59
|
| 88 |
+
cibersortx/imperial_eskd/fewshot4/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 18252280 237938ca8e3cafb921ae0560bf4f0b1ef5589be1393d7d1c9133e2ff4e820538
|
| 89 |
+
cibersortx/imperial_eskd/fewshot4/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 17922036 6cf8bbe5f0389114d2f971a7b75587f9bcc50594a29383cdf10f5fa769bc004d
|
| 90 |
+
cibersortx/imperial_eskd/fewshot4/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 17256313 e6c19d7953184a768a190a8b66fbd725d3bf53fcfb22400f9236f845819d0e34
|
| 91 |
+
cibersortx/imperial_eskd/fewshot4/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 18569148 321c55cca3d08df677783bb011ba3282c4cd9dad43c39e5ef043cdee9f6d20d0
|
| 92 |
+
cibersortx/ji2025_chili/fewshot0/out_hires/CIBERSORTxHiRes_NA_Bcell_Window13.txt 2968257 712521b90309c7935037cc9cbf2689ffb714b4a31820e4d6c6f881becc55461f
|
| 93 |
+
cibersortx/ji2025_chili/fewshot0/out_hires/CIBERSORTxHiRes_NA_DC_Window13.txt 3033306 4eb72549594f602abe62192dff1b156dd5664f4aaa14df78c05ac259fb4a1008
|
| 94 |
+
cibersortx/ji2025_chili/fewshot0/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window13.txt 3070850 913b3952e5116683d4d709a89d602e60d1f01b16f32e3bfd3d2737781071b776
|
| 95 |
+
cibersortx/ji2025_chili/fewshot0/out_hires/CIBERSORTxHiRes_NA_NKcell_Window13.txt 2968257 712521b90309c7935037cc9cbf2689ffb714b4a31820e4d6c6f881becc55461f
|
| 96 |
+
cibersortx/ji2025_chili/fewshot0/out_hires/CIBERSORTxHiRes_NA_Other_Window13.txt 2968257 712521b90309c7935037cc9cbf2689ffb714b4a31820e4d6c6f881becc55461f
|
| 97 |
+
cibersortx/ji2025_chili/fewshot0/out_hires/CIBERSORTxHiRes_NA_Tcell_Window13.txt 2968257 712521b90309c7935037cc9cbf2689ffb714b4a31820e4d6c6f881becc55461f
|
| 98 |
+
cibersortx/ji2025_chili/fewshot1/out_hires/CIBERSORTxHiRes_NA_Bcell_Window13.txt 3083306 6b58aa6bc6229f8858be8e9a3832477e5ba1a3775413001feeda8980e6785c6f
|
| 99 |
+
cibersortx/ji2025_chili/fewshot1/out_hires/CIBERSORTxHiRes_NA_DC_Window13.txt 3189138 7618328b66266c1f4a42e3cf3684050707c174228b61613deeb53b6dea6ee8bd
|
| 100 |
+
cibersortx/ji2025_chili/fewshot1/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window13.txt 3205716 046aad88a269b85e78857397548f697a21c3e87ac2adc8becdd77039e1167814
|
| 101 |
+
cibersortx/ji2025_chili/fewshot1/out_hires/CIBERSORTxHiRes_NA_NKcell_Window13.txt 3083306 6b58aa6bc6229f8858be8e9a3832477e5ba1a3775413001feeda8980e6785c6f
|
| 102 |
+
cibersortx/ji2025_chili/fewshot1/out_hires/CIBERSORTxHiRes_NA_Other_Window13.txt 3083306 6b58aa6bc6229f8858be8e9a3832477e5ba1a3775413001feeda8980e6785c6f
|
| 103 |
+
cibersortx/ji2025_chili/fewshot1/out_hires/CIBERSORTxHiRes_NA_Tcell_Window13.txt 3083306 6b58aa6bc6229f8858be8e9a3832477e5ba1a3775413001feeda8980e6785c6f
|
| 104 |
+
cibersortx/ji2025_chili/fewshot2/out_hires/CIBERSORTxHiRes_NA_Bcell_Window13.txt 3083307 69c71e9abcba7cfe42482fafb86474d7380ed168516653135a32c9a1e1e22aed
|
| 105 |
+
cibersortx/ji2025_chili/fewshot2/out_hires/CIBERSORTxHiRes_NA_DC_Window13.txt 3168457 0d1d706ab8b8441af1016a4569de8c73128f6047cb97dfe8ba3b6fdd3a9fa3d4
|
| 106 |
+
cibersortx/ji2025_chili/fewshot2/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window13.txt 3162505 d38c8ccc6fe138bfab6be5b3eab879bc2793798e59e27abf3cc18bc3f3b2a039
|
| 107 |
+
cibersortx/ji2025_chili/fewshot2/out_hires/CIBERSORTxHiRes_NA_NKcell_Window13.txt 3083307 69c71e9abcba7cfe42482fafb86474d7380ed168516653135a32c9a1e1e22aed
|
| 108 |
+
cibersortx/ji2025_chili/fewshot2/out_hires/CIBERSORTxHiRes_NA_Other_Window13.txt 3083307 69c71e9abcba7cfe42482fafb86474d7380ed168516653135a32c9a1e1e22aed
|
| 109 |
+
cibersortx/ji2025_chili/fewshot2/out_hires/CIBERSORTxHiRes_NA_Tcell_Window13.txt 3083307 69c71e9abcba7cfe42482fafb86474d7380ed168516653135a32c9a1e1e22aed
|
| 110 |
+
cibersortx/ji2025_chili/fewshot3/out_hires/CIBERSORTxHiRes_NA_Bcell_Window13.txt 2968258 8806f919f50ec9e32677d1b3275bfc17b3a8be34d2e18685354ddce22d9c96e4
|
| 111 |
+
cibersortx/ji2025_chili/fewshot3/out_hires/CIBERSORTxHiRes_NA_DC_Window13.txt 3048249 cdba0c8dfcd0ed1efa9c2579dcf48b88e1b39599f7821563094aef9c5e27a92e
|
| 112 |
+
cibersortx/ji2025_chili/fewshot3/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window13.txt 2968258 8806f919f50ec9e32677d1b3275bfc17b3a8be34d2e18685354ddce22d9c96e4
|
| 113 |
+
cibersortx/ji2025_chili/fewshot3/out_hires/CIBERSORTxHiRes_NA_NKcell_Window13.txt 2968258 8806f919f50ec9e32677d1b3275bfc17b3a8be34d2e18685354ddce22d9c96e4
|
| 114 |
+
cibersortx/ji2025_chili/fewshot3/out_hires/CIBERSORTxHiRes_NA_Other_Window13.txt 2968258 8806f919f50ec9e32677d1b3275bfc17b3a8be34d2e18685354ddce22d9c96e4
|
| 115 |
+
cibersortx/ji2025_chili/fewshot3/out_hires/CIBERSORTxHiRes_NA_Tcell_Window13.txt 2968258 8806f919f50ec9e32677d1b3275bfc17b3a8be34d2e18685354ddce22d9c96e4
|
| 116 |
+
cibersortx/ji2025_chili/fewshot4/out_hires/CIBERSORTxHiRes_NA_Bcell_Window12.txt 2853209 802068b0d2307c697cbacc2e4be24dc6e994681312887af93152932ff59a33d2
|
| 117 |
+
cibersortx/ji2025_chili/fewshot4/out_hires/CIBERSORTxHiRes_NA_DC_Window12.txt 2853209 802068b0d2307c697cbacc2e4be24dc6e994681312887af93152932ff59a33d2
|
| 118 |
+
cibersortx/ji2025_chili/fewshot4/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window12.txt 2961429 4c25e190ba596104fab28c94d3b533b752a41936e688fa3decc41ee19eaa8620
|
| 119 |
+
cibersortx/ji2025_chili/fewshot4/out_hires/CIBERSORTxHiRes_NA_NKcell_Window12.txt 2853209 802068b0d2307c697cbacc2e4be24dc6e994681312887af93152932ff59a33d2
|
| 120 |
+
cibersortx/ji2025_chili/fewshot4/out_hires/CIBERSORTxHiRes_NA_Other_Window12.txt 2853209 802068b0d2307c697cbacc2e4be24dc6e994681312887af93152932ff59a33d2
|
| 121 |
+
cibersortx/ji2025_chili/fewshot4/out_hires/CIBERSORTxHiRes_NA_Tcell_Window12.txt 2863620 9c000c50d32fad3457ed58e4210434ce49ac139dc84568099e64868c4689f8e4
|
| 122 |
+
cibersortx/ye2026_sepsis/cv0/out_hires/CIBERSORTxHiRes_NA_Bcell_Window9.txt 2127242 ef84c2e829e275ae45ec0187a88c72a6f4fb5ceadeb2f34a1f4e49a8cb2acc77
|
| 123 |
+
cibersortx/ye2026_sepsis/cv0/out_hires/CIBERSORTxHiRes_NA_DC_Window9.txt 2127514 d3bd563408f9906b7e295842858032d978e57d06e716c0fe6b795504b3cae098
|
| 124 |
+
cibersortx/ye2026_sepsis/cv0/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window9.txt 2129856 d0942fb5d5dfc41de5343348c1a447a0df27174085b77b1e623e0758880867f5
|
| 125 |
+
cibersortx/ye2026_sepsis/cv0/out_hires/CIBERSORTxHiRes_NA_NKcell_Window9.txt 2127898 c87721a8840af5a39385315a016aa3c8df7d73715110f9642c14d4c8366d359d
|
| 126 |
+
cibersortx/ye2026_sepsis/cv0/out_hires/CIBERSORTxHiRes_NA_Other_Window9.txt 2131080 e161b27a8b8212c77ba3524d3ad3b8b87272042b7b49e73275a84fd4f670aa00
|
| 127 |
+
cibersortx/ye2026_sepsis/cv0/out_hires/CIBERSORTxHiRes_NA_Tcell_Window9.txt 2241539 74784cbe1bb1831b5c87f95415cfb0563e534d8ccad1f1c45841a582de6b4d91
|
| 128 |
+
cibersortx/ye2026_sepsis/cv1/out_hires/CIBERSORTxHiRes_NA_Bcell_Window9.txt 2247197 b627415fcdc4c991f8e05529bc79c09ff709ed64e7b576219cd23e40a5a8c13f
|
| 129 |
+
cibersortx/ye2026_sepsis/cv1/out_hires/CIBERSORTxHiRes_NA_DC_Window9.txt 2186421 f46f02c894a85cb94aca292829807089fdf6af3f4963e20646577f163052cbd8
|
| 130 |
+
cibersortx/ye2026_sepsis/cv1/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window9.txt 2140002 ebe9763366e9c0b38da027384e106ad1837634bbd8c41d3639f4a248c810724e
|
| 131 |
+
cibersortx/ye2026_sepsis/cv1/out_hires/CIBERSORTxHiRes_NA_NKcell_Window9.txt 2163805 714b930e5893ba4bea6785ea6350e1a919571f2d51212bf8dca3ab21acc405fb
|
| 132 |
+
cibersortx/ye2026_sepsis/cv1/out_hires/CIBERSORTxHiRes_NA_Other_Window9.txt 2147548 608785e0af169ba267d497f6179d6fe0838dbee373655e6c05a9d55934bf2319
|
| 133 |
+
cibersortx/ye2026_sepsis/cv1/out_hires/CIBERSORTxHiRes_NA_Tcell_Window9.txt 2239641 e65b2d7db603761c8ae074f5fc58c845443e5a13cd1a04db62a657299df16d2d
|
| 134 |
+
cibersortx/ye2026_sepsis/cv2/out_hires/CIBERSORTxHiRes_NA_Bcell_Window8.txt 2007738 df3e70b224a8ae9de129f59e20cb49effbd0cabf3e43cf4ecd316cb4d9ef4b76
|
| 135 |
+
cibersortx/ye2026_sepsis/cv2/out_hires/CIBERSORTxHiRes_NA_DC_Window8.txt 2061784 71688ecc9ada2e13ff1e475e25150c4bdadddb630cc27cf94aa9aecc77bc4698
|
| 136 |
+
cibersortx/ye2026_sepsis/cv2/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window8.txt 2065594 179e3b78ffb711a51162abe2de1e2f0abcc68afd6adabfc004788a11e222b74e
|
| 137 |
+
cibersortx/ye2026_sepsis/cv2/out_hires/CIBERSORTxHiRes_NA_NKcell_Window8.txt 2017582 046550156da535e9be2e4b0e0bcb86ed4c6c37310f7c55a33a7c32c58cbad643
|
| 138 |
+
cibersortx/ye2026_sepsis/cv2/out_hires/CIBERSORTxHiRes_NA_Other_Window8.txt 2007882 39683fdfb097df6c4672db47a3820b55965ac91c2fc3a98228a89396b261a515
|
| 139 |
+
cibersortx/ye2026_sepsis/cv2/out_hires/CIBERSORTxHiRes_NA_Tcell_Window8.txt 2144277 6ebb8b69c3f5939c521a9aa71cbd6915991b7bb213c54da131b588233834e8a4
|
| 140 |
+
cibersortx/ye2026_sepsis/cv3/out_hires/CIBERSORTxHiRes_NA_Bcell_Window8.txt 2011374 40a0a93aab1d121dcd072f087e065726eb29f9eb67ddbfefc577b0adf4b602e8
|
| 141 |
+
cibersortx/ye2026_sepsis/cv3/out_hires/CIBERSORTxHiRes_NA_DC_Window8.txt 2007741 84d3ea8ff88233beb286ec38b86c057762b889c6eb5067f6ba3826a7d92b2252
|
| 142 |
+
cibersortx/ye2026_sepsis/cv3/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window8.txt 2024324 23fe25e75d5115077567b861fe06774c91cd6b7580e211571e71f81b7d278614
|
| 143 |
+
cibersortx/ye2026_sepsis/cv3/out_hires/CIBERSORTxHiRes_NA_NKcell_Window8.txt 2035871 aa4941f6070f6356d84ba190b1b6421d7187014cde6cdf09dd2386ecd6657b17
|
| 144 |
+
cibersortx/ye2026_sepsis/cv3/out_hires/CIBERSORTxHiRes_NA_Other_Window8.txt 2021291 6f97bc1f6753bdde3058dbe63d75f22748662128613296e32ce7e12831c41d6a
|
| 145 |
+
cibersortx/ye2026_sepsis/cv3/out_hires/CIBERSORTxHiRes_NA_Tcell_Window8.txt 2133954 d6bb9a94b9ee74261c249a6491e419758c91363a945015d614c26d220fa504c3
|
| 146 |
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cibersortx/ye2026_sepsis/cv4/out_hires/CIBERSORTxHiRes_NA_Bcell_Window8.txt 2102804 4941a318ffb0eab997d94695317706c56a3f0c3b646f46eff0276843e78de6a1
|
| 147 |
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cibersortx/ye2026_sepsis/cv4/out_hires/CIBERSORTxHiRes_NA_DC_Window8.txt 2031032 23bb3cc4a39d17c8d1939a03c1d5f50fd8464ae8b02829d1dd3a440edabbfbee
|
| 148 |
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cibersortx/ye2026_sepsis/cv4/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window8.txt 2058062 ffb8f3a3b75bf58e13fccde1641bcafce9c857043d20a68ba1d916b6fc95725b
|
| 149 |
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cibersortx/ye2026_sepsis/cv4/out_hires/CIBERSORTxHiRes_NA_NKcell_Window8.txt 2080439 f259566d4b2329a20824bda20f99e7c20026a50d7dca75a6aa67018917f1a661
|
| 150 |
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cibersortx/ye2026_sepsis/cv4/out_hires/CIBERSORTxHiRes_NA_Other_Window8.txt 2032563 24893ad65428c69a740e0dd88e3cebf472394ba0284c85e7549700a7bc0933ed
|
| 151 |
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cibersortx/ye2026_sepsis/cv4/out_hires/CIBERSORTxHiRes_NA_Tcell_Window8.txt 2121070 e69efea514b9b0c6068c6a743b7c2236c4bc67d25bbb6b589e82edab2295c7ac
|
| 152 |
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cibersortx/ye2026_sepsis/fewshot0/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 9738925 6b9c9d9b808caf7c50bb08ef949e8e486d2cee1521635c00de1460f83e8800bd
|
| 153 |
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cibersortx/ye2026_sepsis/fewshot0/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 9737025 5111732e2074b51484ae5a1e9942cf9432a74f3007ac0149341a46ecc0c8c3bb
|
| 154 |
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cibersortx/ye2026_sepsis/fewshot0/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 9579014 4059f5edb07fd645066f75e0d74a1234933bc6cfb2031580d5ab6448ff06c465
|
| 155 |
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cibersortx/ye2026_sepsis/fewshot0/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 9727554 1a6da4ad90bd5a318a1f591dbcbc8eeda71484caac7884398a9bb190945e3f75
|
| 156 |
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cibersortx/ye2026_sepsis/fewshot0/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 9424867 6dc5f292af3581088f36a8cfea7e30adf8d849e923059008756f47f637e21d7d
|
| 157 |
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cibersortx/ye2026_sepsis/fewshot0/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 9947784 81a417e72be8c58abfab47e66601ab3a40866d7e8e73b95043f3ef4b708cc998
|
| 158 |
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cibersortx/ye2026_sepsis/fewshot1/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 9600608 048e2038c5db69385591fca032d1446f02159ccfa3a062760a5dfcf2536676cc
|
| 159 |
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cibersortx/ye2026_sepsis/fewshot1/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 9466291 3938a905601768a7f43c81f17e10b99911d76ab803f44e2559518e8dfc824121
|
| 160 |
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cibersortx/ye2026_sepsis/fewshot1/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 9728814 66ca1473b78ac29303ae5699d94bb6d45b05c4ef3234abd4b5a484ff664d6642
|
| 161 |
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cibersortx/ye2026_sepsis/fewshot1/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 9689695 a2435730bcae78be13cd90ca6ca4aa17399e494964be7497b77a6582f26bd216
|
| 162 |
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cibersortx/ye2026_sepsis/fewshot1/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 9424452 c6db97e327c7d29686e1686c920629407b34d32c2bbf77640811cd5cd2d634a4
|
| 163 |
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cibersortx/ye2026_sepsis/fewshot1/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 9944087 0bd76af759ba7382a3a04e839699183e21d7ea29bc18dc5ad78fb0cf62d8aaf1
|
| 164 |
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cibersortx/ye2026_sepsis/fewshot2/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 9644167 8d39eefe6951ccf09d6c4ab95e03fcfdff2e652fbd3edb41886097c9928ef6e7
|
| 165 |
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cibersortx/ye2026_sepsis/fewshot2/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 9518230 c7063e40347e2eb503442e1a5b84e524ab4c00cb3ef620ebb01d5259a92af45a
|
| 166 |
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cibersortx/ye2026_sepsis/fewshot2/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 9726367 1af00a93ce56ae71228c34338165fd043070342adba2beeb01a6a74409a3874e
|
| 167 |
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cibersortx/ye2026_sepsis/fewshot2/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 9725881 7661135c37764cf636712cf95eadf84da793759450086bddf538f9928653dfe5
|
| 168 |
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cibersortx/ye2026_sepsis/fewshot2/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 9416276 99e967f9ab63d9ecf8afe168d0896f1836531ca0022ce78989553843294dd13e
|
| 169 |
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cibersortx/ye2026_sepsis/fewshot2/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 9955803 90f833c764a8b396b1cb37b407989f069d3e6b0951b6a092e57c3c02d7be302b
|
| 170 |
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cibersortx/ye2026_sepsis/fewshot3/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 9692230 b1264cd175954b6778de34364f2f97a2a33634ce15955c349c67ee2a68bc0c87
|
| 171 |
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cibersortx/ye2026_sepsis/fewshot3/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 9646380 8e2bdaa265f6188978fac25795ba54c01c766179892f80df9010cfaa5e0774e1
|
| 172 |
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cibersortx/ye2026_sepsis/fewshot3/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 9623196 fe416faebc95b1d74bbb01464fd11604a78e7d6aa190b091b2781b2a80e4b1d7
|
| 173 |
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cibersortx/ye2026_sepsis/fewshot3/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 9704118 241781114768d6b944b96407008e04645c26354e849cfa5338b1dc83ac76be1a
|
| 174 |
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cibersortx/ye2026_sepsis/fewshot3/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 9463935 d4de8358770e2b304185b54f4530ee482b8bd18c147534e1bd437f10bfc26449
|
| 175 |
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cibersortx/ye2026_sepsis/fewshot3/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 9956193 f4ce014561b37221cf64e7a57c0a834401b7fe6c0256ba6d8650880414b1b9c8
|
| 176 |
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cibersortx/ye2026_sepsis/fewshot4/out_hires/CIBERSORTxHiRes_NA_Bcell_Window24.txt 9634578 b0d93413cf7eac603e8f588840872aabbed9d5bdaec5ce4f0e9d01911a8aad2c
|
| 177 |
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cibersortx/ye2026_sepsis/fewshot4/out_hires/CIBERSORTxHiRes_NA_DC_Window24.txt 9707883 677368d13afb0d7f2eef4e6586626cb12b605965f1a98905a468f33e2f09e7f1
|
| 178 |
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cibersortx/ye2026_sepsis/fewshot4/out_hires/CIBERSORTxHiRes_NA_Monocyte_Window24.txt 9646046 68ef59ce240b29ba7c062d0fea069c98fe246cf2800bee0ebc81f6c17c063e49
|
| 179 |
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cibersortx/ye2026_sepsis/fewshot4/out_hires/CIBERSORTxHiRes_NA_NKcell_Window24.txt 9571411 f76e822e99f0754a83a0f8984ab38a4774c7eddd3409d4c06d1e43b086a2211d
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cibersortx/ye2026_sepsis/fewshot4/out_hires/CIBERSORTxHiRes_NA_Other_Window24.txt 9433820 1d659c80810685018711037eef5220dd0669e958e11f00896fbce7c600a9f5a2
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cibersortx/ye2026_sepsis/fewshot4/out_hires/CIBERSORTxHiRes_NA_Tcell_Window24.txt 9967943 b139bafa19ea6c2e388fbdb5d916a4707ea6e7d7049e49b5d075586132e86121
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data/giroux2022/aggregates.npz 4382547 9b80e097aafc8e9f46bba6166b50af98391afa05d8584acf7066a697762db5c7
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data/giroux2022/aggregates_full.npz 14166370 6c0178da65a0929edf63875236d62080254279829c59603e489edeb64ae86e2f
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data/immicro/aggregates.npz 13704611 7b6b34edb120956685ca3bf8081babec375584c1dfc8c8eb3800fb1154fe598f
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data/immicro/aggregates_full.npz 40221745 4215e1c95c8ff835ea4867157ac47c5ae1b7cc8c51c4f4b5457e4c2e537b9aca
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data/imperial_eskd/aggregates.npz 18051666 80b9c3401d8d4c724088317828a5c5095a5157ebc66d099e8399b6f948e507f8
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data/imperial_eskd/aggregates_full.npz 54751515 681a49cc9eb86b0d8b313bc417eef9b11c122894922181cf17a62765612ad1d4
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data/ji2025_chili/aggregates.npz 4912330 ab2d1c650eea9381badef93975c96714399d8f538655888c433321af04663247
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data/ji2025_chili/aggregates_full.npz 14305421 0dfbed64479372c0b0cca489dd54f65c6290d9a89da4f1a09d4accaa7140d35f
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data/ye2026_sepsis/aggregates.npz 13474067 d38afdb00f5616ada109babfb6fc0f16bc37b33a434807d263d0163895e1f9de
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data/ye2026_sepsis/aggregates_full.npz 40921275 1c866cbb8990359c870f46804e55cbae4a38cb82b5e0bf5f47bd9a552ada966a
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| 540 |
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cibersortx/ye2026_sepsis/fewshot3/out_fractions/CIBERSORTx_refsample_inferred_refsample.txt 12510187 40af983bde1a481de3e8a4e1bda64cff30fb6dc9fbdbcda9b777fad216bf6b66
|
| 541 |
+
cibersortx/ye2026_sepsis/fewshot4/out_fractions/CIBERSORTx_refsample_inferred_refsample.txt 12511970 d4a67dd39265f05425bdf40d92e9e1ec58c153a24feade28dffbe9d0854acbda
|
| 542 |
+
cibersortx/giroux2022/cv0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1612565 0eb79fbef5f6453f8a07bdfa896e44aec2632240686dccdb7a4876dbe9007c49
|
| 543 |
+
cibersortx/giroux2022/cv1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1607057 701685dfcfb65c197c42210c7d25dd6b2a84af032db5b7cc2ab6384e11bbde4f
|
| 544 |
+
cibersortx/giroux2022/cv2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1609836 1932b83f086442aed9ee679a0590dd72c5fc41194fb0a7c893d7bd837e742eff
|
| 545 |
+
cibersortx/giroux2022/cv3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1621402 c046957966746c7a3c1c3ed710d7ee505415440e58f1edcf955436d8a72c7dc2
|
| 546 |
+
cibersortx/giroux2022/cv4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1616468 111e6aea1418d71a9b2bd1100d93dd845daa151e5528b8cc1d7777d9b516f9e7
|
| 547 |
+
cibersortx/giroux2022/fewshot0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 2762790 5fa36d2f1358a0a89340e636b1ae3a1231decee9da36fbc615f8a2260af049c8
|
| 548 |
+
cibersortx/giroux2022/fewshot1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 2527371 c2921f9c30b16707275145663d93bb1e46bd60e5ecd5d72a66c59141359b8459
|
| 549 |
+
cibersortx/giroux2022/fewshot2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 2992062 8d7879ac0a4279d7ec5d08f3b0a323948b96b2c8078559c5bc9c419c0d449b7f
|
| 550 |
+
cibersortx/giroux2022/fewshot3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 2765533 9262631274012113b5ca19c222c0e2d60307e792e4402db5c149ad50c52717dc
|
| 551 |
+
cibersortx/giroux2022/fewshot4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 2544660 f3cbcb6b7efcfffa9765113973c77739dc4b8f0f73bede48267084a5ea0ac644
|
| 552 |
+
cibersortx/immicro/cv0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 5566548 b15ad9010fcf1c62c09ff4bb106806e43e867bb6b9c974605fbac94224c1aa8f
|
| 553 |
+
cibersortx/immicro/cv1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 5577410 6fb96ee26c5ce854badc97fbb0858ed210960e1fa37c4b89bc9e36fe3833a19e
|
| 554 |
+
cibersortx/immicro/cv2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 5310878 80f71321308f785e3298a8a24d4f6581ad53c17d21a5993c66871e9a04359c88
|
| 555 |
+
cibersortx/immicro/cv3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 5324217 c4c9a8726488dd0c5e041de7794c0a56c5e2df27e0495afa2e059efc164d48b7
|
| 556 |
+
cibersortx/immicro/cv4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 5333570 bd76646382020c71b68fde7d92cd1e03a1e3025ff1597a2582c86f3a8506e706
|
| 557 |
+
cibersortx/immicro/fewshot0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 24243731 2b392d66eec586a828a8ccb90b6b1801c89bd11ea13e6c3726d7102f9da5fb46
|
| 558 |
+
cibersortx/immicro/fewshot1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 24752486 63f6865d1a687e704b5ef1c4442e1b32849c0ffec97670ccb025bdde0dc81ead
|
| 559 |
+
cibersortx/immicro/fewshot2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 24672341 ae9f5bc03ecce911737533686c99c438b45842d92e22063b450fedd092293c9b
|
| 560 |
+
cibersortx/immicro/fewshot3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 24838497 ebed884d93e2efbd782785aa406e7f40bd904349b0fea4299ed0c852e1418419
|
| 561 |
+
cibersortx/immicro/fewshot4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 24650496 c81f7d79977b39832d886b888d7d9228869e5bfe8b3b21aa3347eb15a47ce98d
|
| 562 |
+
cibersortx/imperial_eskd/cv0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 12022630 371bfdbb151d2fec4380388fb2e28865f38bd64a4d9268f57c4af975a5425f82
|
| 563 |
+
cibersortx/imperial_eskd/cv1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 6498908 1dca05ee9f9b3d424fa4f16d2ec1de25bdcfc5c98f2a25ef6494ac1974233fde
|
| 564 |
+
cibersortx/imperial_eskd/cv2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 7518732 6ba18b6d5634b8c476d8259601e1321ad29db81ec2de99a6d74ae04fb7c3361b
|
| 565 |
+
cibersortx/imperial_eskd/cv3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 6314113 00d29df12e715c4a2cb76a64422604e1f2697c7169c634ba301d6d05fcab0c02
|
| 566 |
+
cibersortx/imperial_eskd/cv4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 8294072 8450d4fcacdee3e81b1b378a32ed41008bb06d340ce5fe093feac36336ed9bf9
|
| 567 |
+
cibersortx/imperial_eskd/fewshot0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 35597055 7837c1d7c5b5dbbc9398fa769e6947b246a65df7d6cc894f89c0e0f208cc5c6d
|
| 568 |
+
cibersortx/imperial_eskd/fewshot1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 36281125 858323791d5220fa3d30625fa7d49a882d9d2d4e8d33bfa1cfb6e0030c1028cd
|
| 569 |
+
cibersortx/imperial_eskd/fewshot2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 35500087 3f2a6b01a3f72c6a976d0197d43da306283760a66227fb882009a0b1a6c1a69d
|
| 570 |
+
cibersortx/imperial_eskd/fewshot3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 36760002 ce6f690c2935eb16611c1dbb7368869ee167ef7ee214724620cbb62c73f6898b
|
| 571 |
+
cibersortx/imperial_eskd/fewshot4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 35803825 730d9aecfb72f78b5b0f2dafe794f6fbd96957a6ec666b2e0280f5247fd9aaf6
|
| 572 |
+
cibersortx/ji2025_chili/cv0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 2018968 ee8e7ee104ce2d0d4367ca402ff76f8892df0fb21b6d20eae123305cf6082ce3
|
| 573 |
+
cibersortx/ji2025_chili/cv1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1759443 2973800482dfb7d6857845c4f598d31303e8fe2853335e8431a9be99cdd7f15a
|
| 574 |
+
cibersortx/ji2025_chili/cv2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1762515 5405e56a46e826f56742c9724077e83d56f82244dc03f536770d46f7c88207e0
|
| 575 |
+
cibersortx/ji2025_chili/cv3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 2282822 c31548a998afce0de4f2bc6fd003c306c5c9e251523244092840e7ab041d2e43
|
| 576 |
+
cibersortx/ji2025_chili/cv4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 1769125 c74381465eac0e94461eb52085fad5620cedf90241d013990d4b31fcc03a0ffb
|
| 577 |
+
cibersortx/ji2025_chili/fewshot0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 6543949 aae32ff30c28df146b6f22e97f24aaf6e224ddc294fb7ee0ef2bd8370ff12c52
|
| 578 |
+
cibersortx/ji2025_chili/fewshot1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 6872019 5057542a381e8ca83864d83ca984f8341929a5e4877716db8e2a395aed9561df
|
| 579 |
+
cibersortx/ji2025_chili/fewshot2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 6762781 a8352982c7b11064775d3833140d95e962955c81886b3f4cb70a9df3841a783a
|
| 580 |
+
cibersortx/ji2025_chili/fewshot3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 6615900 322e68fb09da50b462c3b1d1602c4fca57d7e0e4948860584e7f717ca14030dc
|
| 581 |
+
cibersortx/ji2025_chili/fewshot4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 6332137 6db93094211889c32a58470e099a9085a2cabfc95982ca3c7e268071ba22e5d4
|
| 582 |
+
cibersortx/ye2026_sepsis/cv0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 4607194 af6ac9c248c84e39b6fa73b3577e17f834a28a7fe41d1ecfc16b0b822299b5e3
|
| 583 |
+
cibersortx/ye2026_sepsis/cv1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 4597497 8c203c520389a564a3020902c3013437c7698960239ff4d44aeec44d48c70687
|
| 584 |
+
cibersortx/ye2026_sepsis/cv2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 4312805 4aa0f654b0bb118f723205d3b0db50d398fa2b7d4169a183bb06f982f9e0a0eb
|
| 585 |
+
cibersortx/ye2026_sepsis/cv3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 4337075 a068747925adef1582588c6428b3a649bddc75a1d59d5ed00d637acb2dfa40eb
|
| 586 |
+
cibersortx/ye2026_sepsis/cv4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 4334184 688ba10487a63b5d6b7076beaf6439866b60e794c87eb5bdc776b2bbcc34d99f
|
| 587 |
+
cibersortx/ye2026_sepsis/fewshot0/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 19984428 2d0e378e3093633461b75cb21a80a317fbbe293f35382eeaae55a4e215e03d14
|
| 588 |
+
cibersortx/ye2026_sepsis/fewshot1/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 19798730 e19f4094345032459bf12873d0e758454afba3e67fc09bb3d0b5e3b507f6397d
|
| 589 |
+
cibersortx/ye2026_sepsis/fewshot2/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 19686613 1037a41e8b4291eb161b7fa5e4f6fb5fdd8a95a6c922abd996fe102d6c8468d0
|
| 590 |
+
cibersortx/ye2026_sepsis/fewshot3/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 19695454 cc7a9093323c4c51f4196197d00ebaf14a0c6b6497a11108f458a3d3985f8a87
|
| 591 |
+
cibersortx/ye2026_sepsis/fewshot4/out_fractions/CIBERSORTx_Mixtures_Adjusted.txt 19675555 ef084e95ad565eba14ed1ec93702e587cf5deecfafb83b6191c94e14ec1c2ac8
|
| 592 |
+
cibersortx/giroux2022/cv0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548216 6e1a55c67ddb3b0519d23ab7974b96803c92c33332377c8ec2cf5a8b8a4318b1
|
| 593 |
+
cibersortx/giroux2022/cv1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548518 cb66c6dd8253d2f46b3c8954a2e6ba3ce51678c388647ec636a67c95f93efdc8
|
| 594 |
+
cibersortx/giroux2022/cv2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548828 52ac39d203f08e140a8a5127103d74c4be6ad7fcc6c20d9b779e04161a10d8e4
|
| 595 |
+
cibersortx/giroux2022/cv3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548595 f006a2dd137b49d12696b02f55e37b5b6e4eb69b444e2d6a3dcbf059cd536d8c
|
| 596 |
+
cibersortx/giroux2022/cv4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548336 141d855a9ad0f5a7b22a82662b60f4e9fb92ac6cb59424ead05deed22ac5f5cd
|
| 597 |
+
cibersortx/giroux2022/fewshot0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548998 a1a61ca5ffa71f3bdb9873dc298f17bfaaca000c8cf08a60ba12f8b8f2e3bd96
|
| 598 |
+
cibersortx/giroux2022/fewshot1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548216 b184cef075d2e437f343b2971b361dc4c503dd53efd17f33f560e9e106771cc8
|
| 599 |
+
cibersortx/giroux2022/fewshot2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2549113 45678c9c0608f0dec30f8df2239903c78104c42e25e44f17d951e9761f1feba9
|
| 600 |
+
cibersortx/giroux2022/fewshot3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548669 eaca9dba8198d002bf948f59d24e1fd369d41159c879d236bc8ae2dd89166718
|
| 601 |
+
cibersortx/giroux2022/fewshot4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2548110 41f11c051de07c0dc9dfeb4bdbdd0fbf01a24be439463e723f9eb97dcbdf7cad
|
| 602 |
+
cibersortx/immicro/cv0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2244408 61310eae8e09736a2e310b40e76a51219a71d2869ac59d2faae242521c1a8aaa
|
| 603 |
+
cibersortx/immicro/cv1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2244256 2462526af3b45d3b5274b7417054e5ebf8fdc2c0a10b46edb3cb510f47065856
|
| 604 |
+
cibersortx/immicro/cv2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2244527 b8cf05915b036739b51e42b4af8a3d359fe06bf35242b3c38f13ceb922a55f26
|
| 605 |
+
cibersortx/immicro/cv3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2244508 b8e6d790142fc2dc94ababfc96ef9ec1c0d302a10dce29f652b6dd54f235cb92
|
| 606 |
+
cibersortx/immicro/cv4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2244510 1aaec042b0d1790d2ceceffeb84003c614e676c1a28ff8f44eac13fb9be4b335
|
| 607 |
+
cibersortx/immicro/fewshot0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2243285 cc4dbb51b95c3761e8b4d30532856f0a4368afce7029ed747411aafb22f1dae6
|
| 608 |
+
cibersortx/immicro/fewshot1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2245060 f9cff5a99301c01f3b00b3e67a3c03001fb41ec160fefc1ac4247f39d6cccb80
|
| 609 |
+
cibersortx/immicro/fewshot2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2245013 e4c6dcabd8c4a5f9539c7948a277c7669b57c923418e544594a17041f4289a6b
|
| 610 |
+
cibersortx/immicro/fewshot3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2243985 a4e3d8647afa9339f10b3f705e4c2c295218f09cdbd9531bfe1b0a09dec11add
|
| 611 |
+
cibersortx/immicro/fewshot4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2244156 c4c7ba5280429a1be7091ef32bc9112dba4c3bad9d749e55f323dba6440d3b16
|
| 612 |
+
cibersortx/imperial_eskd/cv0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2728359 45b79475811f28bd26ffe25d51a2bf73cdf9c213a89faca662d6adfa998d26d1
|
| 613 |
+
cibersortx/imperial_eskd/cv1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2729425 c4516085ab5c5e028c292bb8eff096f526162402c110041d55c2db2ccb4a7041
|
| 614 |
+
cibersortx/imperial_eskd/cv2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2728835 149a2ff458baad65613a6c45dfefb58bd47682686816de38cf36fcb76bef038a
|
| 615 |
+
cibersortx/imperial_eskd/cv3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2729582 f3b808153e0044f439e535af84e7db911f5e3195ef2c6080f439a6a12853320b
|
| 616 |
+
cibersortx/imperial_eskd/cv4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2728902 82a95ff520578fa9c3310f16f8c097c60261164a7b72be3d39cadfbe4b29c058
|
| 617 |
+
cibersortx/imperial_eskd/fewshot0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2728234 1d2982e9fec1bd8c9046e10f0d7e92e49228e5902060a2e0d13e38e68de48572
|
| 618 |
+
cibersortx/imperial_eskd/fewshot1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2729786 1d68c624c4157b700f3529012c5862ca0c8d75aaa6ecebd2d5092ca68b45f814
|
| 619 |
+
cibersortx/imperial_eskd/fewshot2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2730216 1c4fde478622659216808d7d2ce84446c647fffbae032e3f697922ed3cb3a71e
|
| 620 |
+
cibersortx/imperial_eskd/fewshot3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2728991 2f2ae3ead306957289f1a32909a32cfbc6c5cd658523aff0c0558c567eb3948b
|
| 621 |
+
cibersortx/imperial_eskd/fewshot4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2730307 b127ca1ef50e9542cfc2ea65cb00e688b0061b3b96524400342ac0dca33f3dca
|
| 622 |
+
cibersortx/ji2025_chili/cv0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2664216 b988a7e518e08c11840525c8cf631ba4979af968bcbcf65ff2562d73b4e1b8e8
|
| 623 |
+
cibersortx/ji2025_chili/cv1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2663854 1ee493ca083a0ea2169353e37e899d123e992042bd76bd15dfbb9493c8513d93
|
| 624 |
+
cibersortx/ji2025_chili/cv2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2664027 53a302d3453c5e7ca67bb1559877a84a0353da60db3a8e5176001e30ce06943b
|
| 625 |
+
cibersortx/ji2025_chili/cv3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2663702 ece0576406d83ab13027d577a625f787ceab0e2e2168787881863e9adfd28383
|
| 626 |
+
cibersortx/ji2025_chili/cv4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2664732 9955e340c79dd823972d0e700fde211b4f17351655471826543d7f44ee554b12
|
| 627 |
+
cibersortx/ji2025_chili/fewshot0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2664252 0c70a9cbd843e6e81898e81325c329e9b9f04edc9710bb435b6cff3c6cdb1d83
|
| 628 |
+
cibersortx/ji2025_chili/fewshot1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2660742 398af00f2ad7eda8e295d53f5bbea20d599e5748a0f627e577213dcceaebb57d
|
| 629 |
+
cibersortx/ji2025_chili/fewshot2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2663551 0268af1ab18bcbd3728f7331935cfd50fe248d6c58f6cd42568427583700892a
|
| 630 |
+
cibersortx/ji2025_chili/fewshot3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2663412 8924bf105e93bc852cb87119a3eec24e1defee61060a1a6fb47a8c9caa47eaa2
|
| 631 |
+
cibersortx/ji2025_chili/fewshot4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2665977 e225e1654fe5e3b6a712a205d879e7b493d19563cd71cdc416556829210419cf
|
| 632 |
+
cibersortx/ye2026_sepsis/cv0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2758409 775181905330774c34513dd282ce700670feda48d314f58aec465edbc0b8adfb
|
| 633 |
+
cibersortx/ye2026_sepsis/cv1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2758051 6804146492b5c34c07b882b8ab6bc76c22b9c58cb69f8eaec58e893f47f06515
|
| 634 |
+
cibersortx/ye2026_sepsis/cv2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2757909 a43c8838ff057b7fcd318ead7cb6f0145be7a5b696bc7ad7a66e36c7e21670fa
|
| 635 |
+
cibersortx/ye2026_sepsis/cv3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2758515 165a028c29eb100320440480571e42fcbff8c1ee1bb1d7f8b76d5f1bacc01059
|
| 636 |
+
cibersortx/ye2026_sepsis/cv4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2758054 ae1d833b5c6a8c7a8fb50669de23745c9adc2851809caf68b1ce2d9787044af7
|
| 637 |
+
cibersortx/ye2026_sepsis/fewshot0/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2757745 830d79e0b1516cf38b7a30eb850026bedc83fe3c3289a7fc91bf5c47f09bf3c8
|
| 638 |
+
cibersortx/ye2026_sepsis/fewshot1/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2756700 e5313c6241944388016d7459a48478d36ecf6f5aedc8e52616acba3ef7f5dcf9
|
| 639 |
+
cibersortx/ye2026_sepsis/fewshot2/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2756956 b8ebc0622ca3b3e57ad95e1bc56259a403d60d7582db8deb465b9045f9ea849b
|
| 640 |
+
cibersortx/ye2026_sepsis/fewshot3/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2758341 e1cf07104c8c519551dffdb974933c56d6a4ad7fa7d3e3ffd65f1dad9363c677
|
| 641 |
+
cibersortx/ye2026_sepsis/fewshot4/out_fractions/CIBERSORTx_cell_type_sourceGEP.txt 2758734 5c2eded429a519fea296319233aea292b29e4bb67fee574bea98711c9dff7aaa
|
paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/results/TABLE_cv.csv
ADDED
|
@@ -0,0 +1,6 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
| 1 |
+
cohort,bp_l1_final,bp_l1_first,lora_l1,bp_r,bp_r_covered,lora_r,broadcast_r,broadcast_r_covered,bp_partial,lora_partial,bp_deg,lora_deg,broadcast_deg,scsemi_l1,scsemi_r,scsemi_deg
|
| 2 |
+
immicro,1.3896436381034112,1.2471449944879396,0.16172890350786212,,,,,,,,,,,0.2170087237986556,,
|
| 3 |
+
ye2026_sepsis,1.1949134942702533,0.8976692163119185,0.3618246783833512,0.27273639395517935,0.2677112222593618,0.5267612711229187,0.31594581626202867,0.2722574527032429,0.13318246124588148,0.46874787884206276,33.0,23.0,21.0,,,
|
| 4 |
+
ji2025_chili,1.0978294301798797,1.0647439954894382,0.5044992874562503,0.24055359091687248,0.2505351616532235,0.47928798340225065,0.2398746224459366,0.22569844870512668,0.07660192688042304,0.45204372964017614,4.0,15.0,4.0,0.575388570715078,0.2730887293849816,0.0
|
| 5 |
+
imperial_eskd,1.2520012986063231,1.199848612879087,0.37638560875745664,0.643234589352544,0.6483916675055967,0.8187448084273437,0.6606053132085035,0.6582477222834011,0.2982601573161649,0.6899314750539278,98.0,143.0,84.0,,,
|
| 6 |
+
giroux2022,1.0130932525218068,0.5495314839645408,0.2580442754044374,0.4619774813232209,0.4653101345055838,0.46094848304079133,0.5752659929360795,0.5757467455051679,0.057257053963693766,0.3230347269696575,4.0,5.0,5.0,,,
|
paper-releases/pbisc-diffusion/updates/20261008T022410Z-deconv-scsemi-deck/diffusion/dataset/server_1/model8/plans/bayesprism_paired_20261006/tools/score_bp.py
ADDED
|
@@ -0,0 +1,377 @@
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|
| 1 |
+
#!/usr/bin/env python3
|
| 2 |
+
"""Score BayesPrism next to LoRA on the paired benchmark's own donors, truth and statistics (CPU only; 2026-10-06).
|
| 3 |
+
|
| 4 |
+
For one cohort and one comparison:
|
| 5 |
+
fewshot draw j BayesPrism of inputs/<ds>/fewshot<j> vs arm lora5 (and zero-shot arms) of the benchmark's stored
|
| 6 |
+
scores/<ds>/fewshot_n5/draw<j>/ (scores_nosex/ for Giroux, as the deck)
|
| 7 |
+
cv BayesPrism of inputs/<ds>/cv0..cv4 pooled over the folds vs arm real_lora__raw of scores/<ds>/pooled/
|
| 8 |
+
(scores_nosex/ for Giroux) and, when present, scsemi__raw of scsemi_cv_20261002/pooled*/
|
| 9 |
+
The observed side is the scorer's own (paired_finetune_intradataset_20260927/tools/score_ft.py, imported): reference
|
| 10 |
+
cache, scored donors and their order, case/control vectors and --exclude-genes of the stored summary.json. Metrics use
|
| 11 |
+
the scorer's functions on BayesPrism's outputs, treating each donor x type as one profile, exactly as the scorer's bulk
|
| 12 |
+
broadcast treats each donor's bulk:
|
| 13 |
+
composition L1 vs the observed composition (F.IC.l1), per donor, mean; headline = theta_final in cell units
|
| 14 |
+
(mRNA fraction / the type's mean UMI per reference cell, renormalised: the 'cellsize' unit of
|
| 15 |
+
utility_studies_final6k_20260925/deconv); also theta_first and the raw mRNA units
|
| 16 |
+
logFC r per type (T, B, Mono, NK): Z on the scoring genes -> per-donor CP10K (mean_cp10k) and summed counts
|
| 17 |
+
(pseudobulk_log1p_cpm) -> F.lfc_* -> F.contrast_scores against the observed logFC, with the bulk
|
| 18 |
+
broadcast of the stored arm's condition NPZ; genes = the scorer's detected-gene rule
|
| 19 |
+
top-50 DEG F.deg_top on Z per type (one profile per donor, as the broadcast) vs the observed lists; sum over types
|
| 20 |
+
Genes absent from Z (no reference UMI, or BayesPrism's outlier filter) are zero in the "zero-filled" variant and
|
| 21 |
+
excluded in the "covered" variant (where the broadcast is recomputed on the same genes).
|
| 22 |
+
Gates (fail closed): the observed composition equals the stored per_donor obs_*, and the recomputed broadcast r and
|
| 23 |
+
broadcast top-50 overlap equal the stored values for every type.
|
| 24 |
+
Output: results/<ds>/<comparison>/{summary.json, per_donor.csv, contrast.csv}.
|
| 25 |
+
usage: CUDA_VISIBLE_DEVICES='' score_bp.py DATASET {fewshot DRAW | cv} [score|full|cibersortx]
|
| 26 |
+
(cibersortx: CIBERSORTx Fractions + HiRes of cibersortx/<ds>/<split>/ in place of BayesPrism; load_cx.)
|
| 27 |
+
(ImMicro has no case/control contrast: composition only. Gene mode full reads the <split>_full BayesPrism runs.)
|
| 28 |
+
"""
|
| 29 |
+
from __future__ import annotations
|
| 30 |
+
|
| 31 |
+
import json
|
| 32 |
+
import os
|
| 33 |
+
import sys
|
| 34 |
+
import time
|
| 35 |
+
from pathlib import Path
|
| 36 |
+
|
| 37 |
+
if os.environ.get("CUDA_VISIBLE_DEVICES") != "":
|
| 38 |
+
raise SystemExit("run with CUDA_VISIBLE_DEVICES=''")
|
| 39 |
+
|
| 40 |
+
import numpy as np
|
| 41 |
+
import pandas as pd
|
| 42 |
+
|
| 43 |
+
sys.path.insert(0, str(Path(__file__).resolve().parent))
|
| 44 |
+
import common as C # noqa: E402
|
| 45 |
+
sys.path.insert(0, str(C.FT / "tools"))
|
| 46 |
+
import score_ft as SF # noqa: E402 (the benchmark scorer, imported unchanged)
|
| 47 |
+
F = SF.F
|
| 48 |
+
NOSEX = {"giroux2022"} # the deck's Giroux disease metrics exclude sex-linked genes (scores_nosex/)
|
| 49 |
+
|
| 50 |
+
|
| 51 |
+
def light_view(ref: dict, donors: list[str]) -> dict:
|
| 52 |
+
"""score_ft.make_view's observed-side fields (same selection), without the PCA used for precision/coverage."""
|
| 53 |
+
pos = {d: i for i, d in enumerate(ref["donors"].astype(str))}
|
| 54 |
+
sel = np.asarray([pos[d] for d in donors], dtype=np.int64)
|
| 55 |
+
v = {"donors": np.asarray(donors, dtype=str), "sel": sel, "groups": ref["groups"][sel].astype(str),
|
| 56 |
+
"comp": ref["composition"][sel], "score_ids": [str(g) for g in ref["score_ids"]]}
|
| 57 |
+
for k in ("n", "sum", "cp10k", "det"):
|
| 58 |
+
v[f"agg_{k}"] = ref[f"agg_{k}"][sel].astype(np.float64 if k != "n" else np.int64)
|
| 59 |
+
v["deg_types"] = tuple(ref["deg"]["types"])
|
| 60 |
+
v["deg_n"], v["deg_sum"] = ref["deg"]["n"][sel], ref["deg"]["sum"][sel]
|
| 61 |
+
return v
|
| 62 |
+
|
| 63 |
+
|
| 64 |
+
def stored_dir(ds: str, kind: str, j: int | None) -> Path:
|
| 65 |
+
root = C.FT / ("scores_nosex" if ds in NOSEX else "scores") / ds
|
| 66 |
+
return root / "fewshot_n5" / f"draw{j}" if kind == "fewshot" else root / "pooled"
|
| 67 |
+
|
| 68 |
+
|
| 69 |
+
def load_bp(ds: str, splits: list[str], donors: np.ndarray, score_ids: list[str]) -> dict:
|
| 70 |
+
"""theta (donors x GATE) and Z (donors x types x scoring genes, 0 where absent) + coverage mask per donor."""
|
| 71 |
+
S, G = donors.size, len(score_ids)
|
| 72 |
+
gpos = {g: i for i, g in enumerate(score_ids)}
|
| 73 |
+
theta = {k: np.full((S, len(F.GATE)), np.nan) for k in ("final", "first")}
|
| 74 |
+
size = np.full((S, len(F.GATE)), np.nan) # mean UMI per reference cell of each type on the split's genes
|
| 75 |
+
Z = np.zeros((S, len(F.GATE), G))
|
| 76 |
+
cov = np.zeros((S, G), dtype=bool)
|
| 77 |
+
dpos = {d: i for i, d in enumerate(donors)}
|
| 78 |
+
seen = set()
|
| 79 |
+
for sp_ in splits:
|
| 80 |
+
d = C.PLAN / "inputs" / ds / sp_ / "bp"
|
| 81 |
+
if not (d / "DONE").exists():
|
| 82 |
+
raise SystemExit(f"{d}: BayesPrism not finished")
|
| 83 |
+
ths = {k: pd.read_csv(d / f"theta_{k}.csv", index_col=0) for k in ("final", "first")}
|
| 84 |
+
ref = pd.read_csv(d.parent / "reference.tsv.gz", sep="\t", index_col=0)
|
| 85 |
+
st = pd.read_csv(d.parent / "state_types.tsv", sep="\t").set_index("state").loc[ref.index]
|
| 86 |
+
umi = ref.sum(1).groupby(st["type"]).sum()
|
| 87 |
+
ncell = st.groupby("type")["n_cells"].sum()
|
| 88 |
+
sz = [float(umi[t] / ncell[t]) if t in umi.index else np.nan for t in F.GATE]
|
| 89 |
+
for s in ths["final"].index.astype(str):
|
| 90 |
+
if s in dpos:
|
| 91 |
+
if s in seen:
|
| 92 |
+
raise SystemExit(f"{s} is a mixture sample of two splits")
|
| 93 |
+
seen.add(s)
|
| 94 |
+
for k, th in ths.items():
|
| 95 |
+
theta[k][dpos[s]] = [th.loc[s, t] if t in th.columns else 0.0 for t in F.GATE]
|
| 96 |
+
size[dpos[s]] = sz
|
| 97 |
+
for ti, t in enumerate(F.GATE):
|
| 98 |
+
f = d / f"Z_type_{t.replace(' ', '_')}.tsv.gz"
|
| 99 |
+
if not f.exists():
|
| 100 |
+
continue
|
| 101 |
+
z = pd.read_csv(f, sep="\t", index_col=0)
|
| 102 |
+
cols = np.asarray([gpos.get(g, -1) for g in z.columns.astype(str)])
|
| 103 |
+
ok = cols >= 0
|
| 104 |
+
for s in z.index.astype(str):
|
| 105 |
+
if s in dpos:
|
| 106 |
+
Z[dpos[s], ti, cols[ok]] = z.loc[s].to_numpy()[ok]
|
| 107 |
+
cov[dpos[s], cols[ok]] = True
|
| 108 |
+
if np.isnan(theta["final"]).any():
|
| 109 |
+
raise SystemExit(f"{int(np.isnan(theta['final']).any(1).sum())} scored donors without a BayesPrism estimate")
|
| 110 |
+
return {"theta": theta, "size": size, "Z": Z, "covered": cov}
|
| 111 |
+
|
| 112 |
+
|
| 113 |
+
def load_cx(ds: str, splits: list[str], donors: np.ndarray, score_ids: list[str]) -> dict:
|
| 114 |
+
"""CIBERSORTx (tools/run_cibersortx.sh): fractions = out_fractions/CIBERSORTx_Adjusted.txt (S-mode adjusted);
|
| 115 |
+
per-type size = mean UMI per reference cell of each type in the split's refsample.txt; Z from HiRes output files
|
| 116 |
+
(out_hires/*<type>*.txt, genes x samples) when present, else zeros with covered = False (composition only).
|
| 117 |
+
HiRes imputes a different gene subset per type and writes exactly 1 in every sample for the other genes; such rows
|
| 118 |
+
are not expression: they stay zero in Z and are not covered (covered_type = per type, per donor)."""
|
| 119 |
+
S, G = donors.size, len(score_ids)
|
| 120 |
+
gpos = {g: i for i, g in enumerate(score_ids)}
|
| 121 |
+
theta = np.full((S, len(F.GATE)), np.nan)
|
| 122 |
+
size = np.full((S, len(F.GATE)), np.nan)
|
| 123 |
+
Z = np.zeros((S, len(F.GATE), G))
|
| 124 |
+
cov = np.zeros((S, G), dtype=bool)
|
| 125 |
+
cov_t = np.zeros((S, len(F.GATE), G), dtype=bool)
|
| 126 |
+
imputed = {}
|
| 127 |
+
dpos = {d: i for i, d in enumerate(donors)}
|
| 128 |
+
has_z = True
|
| 129 |
+
for sp_ in splits:
|
| 130 |
+
d = C.PLAN / "cibersortx" / ds / sp_
|
| 131 |
+
if not (d / "out_fractions" / "DONE").exists():
|
| 132 |
+
raise SystemExit(f"{d}: CIBERSORTx fractions not finished")
|
| 133 |
+
fr = pd.read_csv(d / "out_fractions" / "CIBERSORTx_Adjusted.txt", sep="\t", index_col=0)
|
| 134 |
+
ref = pd.read_csv(d / "refsample.txt", sep="\t", index_col=0)
|
| 135 |
+
lib = ref.sum(0)
|
| 136 |
+
labs = pd.Series([next((g for g in F.GATE if str(c).startswith(g)), str(c)) for c in ref.columns])
|
| 137 |
+
sz = lib.groupby(labs.to_numpy()).mean()
|
| 138 |
+
szv = [float(sz[t]) if t in sz.index else np.nan for t in F.GATE]
|
| 139 |
+
for s in fr.index.astype(str):
|
| 140 |
+
if s in dpos:
|
| 141 |
+
theta[dpos[s]] = [fr.loc[s, t] if t in fr.columns else 0.0 for t in F.GATE]
|
| 142 |
+
size[dpos[s]] = szv
|
| 143 |
+
hd = d / "out_hires"
|
| 144 |
+
# HiRes names its outputs CIBERSORTxHiRes_NA_<type without spaces>_Window<w>.txt
|
| 145 |
+
files = {t: next(iter(sorted(hd.glob(f"CIBERSORTxHiRes_*_{t.replace(' ', '')}_Window*.txt"))), None)
|
| 146 |
+
if hd.exists() else None for t in F.GATE}
|
| 147 |
+
if not (hd / "DONE").exists() or not all(files[t] for t in F.CONTRAST_TYPES):
|
| 148 |
+
has_z = False
|
| 149 |
+
continue
|
| 150 |
+
for ti, t in enumerate(F.GATE):
|
| 151 |
+
if files[t] is None:
|
| 152 |
+
continue
|
| 153 |
+
z = pd.read_csv(files[t], sep="\t", index_col=0) # genes x samples
|
| 154 |
+
rows = np.asarray([gpos.get(g, -1) for g in z.index.astype(str)])
|
| 155 |
+
ok = (rows >= 0) & ~(z.to_numpy(np.float64) == 1.0).all(1) # imputed genes only
|
| 156 |
+
imputed[f"{sp_}|{t}"] = int(ok.sum())
|
| 157 |
+
for s in z.columns.astype(str):
|
| 158 |
+
if s in dpos:
|
| 159 |
+
v = np.nan_to_num(z[s].to_numpy(np.float64))
|
| 160 |
+
Z[dpos[s], ti, rows[ok]] = v[ok]
|
| 161 |
+
cov[dpos[s], rows[ok]] = True
|
| 162 |
+
cov_t[dpos[s], ti, rows[ok]] = True
|
| 163 |
+
if np.isnan(theta).any():
|
| 164 |
+
raise SystemExit(f"{int(np.isnan(theta).any(1).sum())} scored donors without a CIBERSORTx estimate")
|
| 165 |
+
th = theta / theta.sum(1, keepdims=True)
|
| 166 |
+
return {"theta": {"final": th, "first": th}, "size": size, "Z": Z, "covered": cov, "covered_type": cov_t,
|
| 167 |
+
"imputed_genes": imputed, "has_z": has_z}
|
| 168 |
+
|
| 169 |
+
|
| 170 |
+
def to_cells(theta: np.ndarray, size: np.ndarray) -> np.ndarray:
|
| 171 |
+
"""mRNA fractions -> cell fractions: divide by the type's mean UMI per reference cell, renormalise
|
| 172 |
+
(utility_studies_final6k_20260925/deconv: the 'cellsize' unit, primary there); absent types stay 0."""
|
| 173 |
+
c = np.where(theta > 0, theta / np.where(np.isnan(size), 1.0, size), 0.0)
|
| 174 |
+
return c / c.sum(1, keepdims=True)
|
| 175 |
+
|
| 176 |
+
|
| 177 |
+
def bp_metrics(v: dict, bp: dict, bulk: np.ndarray, contrast: dict, obs_deg: dict, genes_extra: np.ndarray | None) -> tuple:
|
| 178 |
+
"""Contrast and DEG rows of BayesPrism and of the broadcast; genes_extra restricts the gene set (covered variant):
|
| 179 |
+
one mask for every type (G,) or one per type (len(F.GATE), G; CIBERSORTx HiRes imputes a gene subset per type).
|
| 180 |
+
A type whose mask holds < 3 genes gets NaN rows and no DEG score."""
|
| 181 |
+
case, ctrl = contrast["case"], contrast["ctrl"]
|
| 182 |
+
S = v["donors"].size
|
| 183 |
+
ex = lambda gi: None if genes_extra is None else (genes_extra[gi] if genes_extra.ndim == 2 else genes_extra) # noqa: E731
|
| 184 |
+
G = len(v["score_ids"])
|
| 185 |
+
rows = []
|
| 186 |
+
for ti, t in enumerate(F.CONTRAST_TYPES):
|
| 187 |
+
gi = F.GATE.index(t)
|
| 188 |
+
ge = ex(gi)
|
| 189 |
+
if ge is not None and ge.sum() < 3:
|
| 190 |
+
for variant in ("mean_cp10k", "pseudobulk_log1p_cpm"):
|
| 191 |
+
rows.append({"celltype": t, "statistic": variant,
|
| 192 |
+
**F.contrast_scores(None, None, None, np.zeros(G, bool))})
|
| 193 |
+
continue
|
| 194 |
+
blk = F.bulk_lfc(bulk if ge is None else bulk * ge, case, ctrl)
|
| 195 |
+
z = bp["Z"][:, gi, :]
|
| 196 |
+
n_g = (z.sum(1) > 0).astype(np.int64)
|
| 197 |
+
lib = z.sum(1, keepdims=True)
|
| 198 |
+
cp = np.divide(z * F.CP10K, lib, out=np.zeros_like(z), where=lib > 0)
|
| 199 |
+
n_o = v["agg_n"][:, ti]
|
| 200 |
+
det = v["agg_det"][:, ti].sum(0) / max(int(n_o.sum()), 1)
|
| 201 |
+
genes = det >= F.MIN_DETECT_FRAC
|
| 202 |
+
if "keep" in v:
|
| 203 |
+
genes &= v["keep"]
|
| 204 |
+
if ge is not None:
|
| 205 |
+
genes &= ge
|
| 206 |
+
for variant, fn, key, mat in (("mean_cp10k", F.lfc_mean_cp10k, "cp10k", cp),
|
| 207 |
+
("pseudobulk_log1p_cpm", F.lfc_pseudobulk, "sum", z)):
|
| 208 |
+
o = fn(n_o, v[f"agg_{key}"][:, ti], case, ctrl)
|
| 209 |
+
g = fn(n_g, mat, case, ctrl)
|
| 210 |
+
rows.append({"celltype": t, "statistic": variant, **F.contrast_scores(o, g, blk[variant], genes)})
|
| 211 |
+
deg, deg_b = {}, {}
|
| 212 |
+
kg = SF.keep_genes(v)
|
| 213 |
+
ksel = np.ones(G, bool) if "keep" not in v else v["keep"]
|
| 214 |
+
blk_deg = {}
|
| 215 |
+
for t in v["deg_types"]:
|
| 216 |
+
gi = F.GATE.index(t)
|
| 217 |
+
ge = ex(gi)
|
| 218 |
+
if ge is not None and ge[ksel].sum() < 3:
|
| 219 |
+
deg[t] = deg_b[t] = None
|
| 220 |
+
continue
|
| 221 |
+
z = bp["Z"][:, gi, :]
|
| 222 |
+
if ge is not None:
|
| 223 |
+
z = z * ge
|
| 224 |
+
gd = F.deg_top((z.sum(1) > 0).astype(np.int64), z[:, ksel], case, ctrl, kg)
|
| 225 |
+
deg[t] = F.deg_overlap(obs_deg[t]["top"], gd["top"])["overlap"] if obs_deg[t]["defined"] else None
|
| 226 |
+
k_ = None if ge is None else ge.tobytes()
|
| 227 |
+
if k_ not in blk_deg:
|
| 228 |
+
blk_deg[k_] = F.deg_top(np.ones(S, np.int64), (bulk if ge is None else bulk * ge)[:, ksel], case, ctrl, kg)
|
| 229 |
+
deg_b[t] = F.deg_overlap(obs_deg[t]["top"], blk_deg[k_]["top"])["overlap"] if obs_deg[t]["defined"] else None
|
| 230 |
+
return pd.DataFrame(rows), deg, deg_b
|
| 231 |
+
|
| 232 |
+
|
| 233 |
+
def main(argv=None) -> int:
|
| 234 |
+
args = argv or sys.argv[1:]
|
| 235 |
+
ds, kind = args[0], args[1]
|
| 236 |
+
j = int(args[2]) if kind == "fewshot" else None
|
| 237 |
+
mode = (args[3] if kind == "fewshot" else args[2]) if len(args) > (3 if kind == "fewshot" else 2) else "score"
|
| 238 |
+
t0 = time.monotonic()
|
| 239 |
+
sdir = stored_dir(ds, kind, j)
|
| 240 |
+
summ = json.loads((sdir / "summary.json").read_text())
|
| 241 |
+
lora_arm = "lora5" if kind == "fewshot" else "real_lora__raw"
|
| 242 |
+
spec = F.DATASETS[ds]
|
| 243 |
+
cache = F.CACHE_ROOT / spec.name
|
| 244 |
+
ref, man = F.load_reference(cache)
|
| 245 |
+
ref["deg"] = F.load_deg_reference(cache, man, ref)
|
| 246 |
+
if man["reference_sha256"] != summ["reference"]["reference_sha256"]:
|
| 247 |
+
raise SystemExit("reference cache differs from the stored scoring")
|
| 248 |
+
donors = [str(d) for d in summ["donors"]]
|
| 249 |
+
v = light_view(ref, donors)
|
| 250 |
+
cmd = summ["command"]
|
| 251 |
+
if "--exclude-genes" in cmd:
|
| 252 |
+
v["keep"], _ = SF.gene_exclusion(Path(cmd[cmd.index("--exclude-genes") + 1]), v["score_ids"])
|
| 253 |
+
cd = summ.get("contrast_definition") or {}
|
| 254 |
+
has_contrast = bool(cd.get("enabled")) and "case" in cd # ImMicro (healthy cohort): composition only
|
| 255 |
+
# gate 1: observed composition equals the stored per_donor obs_* of the LoRA arm
|
| 256 |
+
per_st = pd.read_csv(sdir / "per_donor.csv")
|
| 257 |
+
pl = per_st[per_st.arm == lora_arm].set_index("donor").loc[donors]
|
| 258 |
+
oc_st = pl[[f"obs_{g}" for g in F.GATE]].to_numpy()
|
| 259 |
+
if not np.allclose(oc_st, v["comp"], atol=1e-12):
|
| 260 |
+
raise SystemExit("observed composition differs from the stored scoring")
|
| 261 |
+
if mode == "cibersortx":
|
| 262 |
+
splits = [f"fewshot{j}"] if kind == "fewshot" else [f"cv{k}" for k in range(5)]
|
| 263 |
+
bp = load_cx(ds, splits, np.asarray(donors), v["score_ids"])
|
| 264 |
+
else:
|
| 265 |
+
splits = [f"fewshot{j}{C.suffix(mode)}"] if kind == "fewshot" else [f"cv{k}{C.suffix(mode)}" for k in range(5)]
|
| 266 |
+
bp = load_bp(ds, splits, np.asarray(donors), v["score_ids"])
|
| 267 |
+
bp["has_z"] = True
|
| 268 |
+
# composition
|
| 269 |
+
comps = {"final_cells": to_cells(bp["theta"]["final"], bp["size"]), "first_cells": to_cells(bp["theta"]["first"], bp["size"]),
|
| 270 |
+
"final_mrna": bp["theta"]["final"], "first_mrna": bp["theta"]["first"]}
|
| 271 |
+
l1s = {k: F.IC.l1(c, v["comp"]) for k, c in comps.items()}
|
| 272 |
+
l1 = l1s["final_cells"]
|
| 273 |
+
per = pd.DataFrame({"donor": donors, "group": v["groups"], **{f"bp_comp_l1_{k}": x for k, x in l1s.items()},
|
| 274 |
+
"lora_comp_l1": pl.comp_l1.to_numpy()})
|
| 275 |
+
for gi, g in enumerate(F.GATE):
|
| 276 |
+
per[f"bp_first_{g}"] = comps["first_cells"][:, gi]
|
| 277 |
+
per[f"bp_final_{g}"] = comps["final_cells"][:, gi]
|
| 278 |
+
per[f"obs_{g}"] = v["comp"][:, gi]
|
| 279 |
+
h = summ["headline"]
|
| 280 |
+
res = {"dataset": ds, "comparison": kind, "draw": j, "gene_mode": mode, "stored_scores": str(sdir),
|
| 281 |
+
"lora_arm": lora_arm, "n_donors": len(donors), "contrast": has_contrast,
|
| 282 |
+
"genes_scoring": len(v["score_ids"]), "genes_covered_by_Z_all_donors": int(bp["covered"].all(0).sum()),
|
| 283 |
+
"bayesprism": {"comp_l1": float(l1.mean()), "comp_l1_by_variant": {k: float(x.mean()) for k, x in l1s.items()},
|
| 284 |
+
"mean_fraction": {k: dict(zip(F.GATE, c.mean(0).round(4).tolist())) for k, c in comps.items()}},
|
| 285 |
+
"observed_mean_fraction": dict(zip(F.GATE, v["comp"].mean(0).round(4).tolist())),
|
| 286 |
+
"lora": {"comp_l1": h[lora_arm]["comp_l1"], "coverage": h[lora_arm].get("coverage")},
|
| 287 |
+
"broadcast": {},
|
| 288 |
+
"other_arms": {a: {"comp_l1": h[a]["comp_l1"], "logfc_r_mean_cp10k": h[a].get("contrast_mean_cp10k_r_gen"),
|
| 289 |
+
"deg_top50_sum": h[a].get("deg_top50_sum")} for a in h if a != lora_arm},
|
| 290 |
+
"gates": {"observed_composition_equal": True}}
|
| 291 |
+
con = pd.DataFrame()
|
| 292 |
+
res["method"] = "cibersortx" if mode == "cibersortx" else "bayesprism"
|
| 293 |
+
res["has_cell_type_expression"] = bool(bp["has_z"])
|
| 294 |
+
if has_contrast and bp["has_z"]:
|
| 295 |
+
contrast = {"enabled": True, "case": np.asarray(cd["case"], bool), "ctrl": np.asarray(cd["ctrl"], bool)}
|
| 296 |
+
if list(cd["groups"]) != list(v["groups"]):
|
| 297 |
+
raise SystemExit("stored contrast groups differ from the reference view")
|
| 298 |
+
obs_deg = SF.observed_deg(v, contrast)
|
| 299 |
+
cb = summ["per_arm"][lora_arm]["condition_for_broadcast"]
|
| 300 |
+
cpath = C.local_copy(cb["path"], cb["sha256"]) # pooled conditions may live on spark-0110 only
|
| 301 |
+
bulk = SF.load_condition_bulk(str(cpath), cb["sha256"], np.asarray(donors), np.asarray(v["score_ids"]))["bulk"]
|
| 302 |
+
# covered genes: every donor's Z holds them (CIBERSORTx: per type, its HiRes-imputed genes)
|
| 303 |
+
covered_all = bp["covered_type"].all(0) if "covered_type" in bp else bp["covered"].all(0)
|
| 304 |
+
out_rows, deg_out = [], {}
|
| 305 |
+
for variant_name, extra in (("zero_filled", None), ("covered", covered_all)):
|
| 306 |
+
crow, deg, deg_b = bp_metrics(v, bp, bulk, contrast, obs_deg, extra)
|
| 307 |
+
crow.insert(0, "genes", variant_name)
|
| 308 |
+
out_rows.append(crow)
|
| 309 |
+
deg_out[variant_name] = {"bp": deg, "broadcast": deg_b}
|
| 310 |
+
con = pd.concat(out_rows, ignore_index=True)
|
| 311 |
+
# gate 2: the zero-filled variant's broadcast equals the stored broadcast (same genes, same bulk, same truth)
|
| 312 |
+
st_con = pd.read_csv(sdir / "contrast.csv")
|
| 313 |
+
st_l = st_con[st_con.arm == lora_arm]
|
| 314 |
+
zf = con[con.genes == "zero_filled"]
|
| 315 |
+
for _, r in zf.iterrows():
|
| 316 |
+
s = st_l[(st_l.celltype == r.celltype) & (st_l.statistic == r.statistic)]
|
| 317 |
+
if len(s) != 1 or not np.isclose(float(s.r_broadcast.iloc[0]), float(r.r_broadcast), atol=1e-9, equal_nan=True):
|
| 318 |
+
raise SystemExit(f"broadcast r differs from the stored scoring ({r.celltype}, {r.statistic})")
|
| 319 |
+
sd = st_l[st_l.statistic == F.DEG_STATISTIC].set_index("celltype")
|
| 320 |
+
for t_, b in deg_out["zero_filled"]["broadcast"].items(): # older pooled scorings have no DEG rows
|
| 321 |
+
if "top50_overlap_broadcast" in sd.columns and t_ in sd.index and b is not None and \
|
| 322 |
+
int(sd.loc[t_, "top50_overlap_broadcast"]) != b:
|
| 323 |
+
raise SystemExit(f"broadcast top-50 overlap differs from the stored scoring ({t_})")
|
| 324 |
+
zmc = zf[zf.statistic == "mean_cp10k"]
|
| 325 |
+
cv_ = con[(con.genes == "covered") & (con.statistic == "mean_cp10k")]
|
| 326 |
+
res["n_case"], res["n_control"] = int(contrast["case"].sum()), int(contrast["ctrl"].sum())
|
| 327 |
+
res["bayesprism"].update({
|
| 328 |
+
"logfc_r_mean_cp10k": float(zmc.r_generated.mean()),
|
| 329 |
+
"partial_r_mean_cp10k": float(zmc.partial_r_generated_given_broadcast.mean()),
|
| 330 |
+
"logfc_r_mean_cp10k_covered_genes": float(cv_.r_generated.mean()),
|
| 331 |
+
"deg_top50_sum": int(sum(x for x in deg_out["zero_filled"]["bp"].values() if x is not None)),
|
| 332 |
+
"deg_top50_sum_covered_genes": int(sum(x for x in deg_out["covered"]["bp"].values() if x is not None)),
|
| 333 |
+
"per_type_logfc_r": dict(zip(zmc.celltype, zmc.r_generated.round(4)))})
|
| 334 |
+
res["broadcast"] = {"logfc_r_mean_cp10k": float(zmc.r_broadcast.mean()),
|
| 335 |
+
"logfc_r_mean_cp10k_covered_genes": float(cv_.r_broadcast.mean()),
|
| 336 |
+
"deg_top50_sum": h[lora_arm].get("deg_top50_sum_broadcast")}
|
| 337 |
+
res["lora"].update({"logfc_r_mean_cp10k": h[lora_arm]["contrast_mean_cp10k_r_gen"],
|
| 338 |
+
"deg_top50_sum": h[lora_arm].get("deg_top50_sum"),
|
| 339 |
+
"partial_r_mean_cp10k": summ["per_arm"][lora_arm]["contrast"]["mean_cp10k"]["mean_partial_r"]})
|
| 340 |
+
if kind == "cv" and res["lora"].get("deg_top50_sum") is None: # older pooled scorings: DEG in pooled_deg/
|
| 341 |
+
for root in ("scores_nosex", "scores") if ds in NOSEX else ("scores",):
|
| 342 |
+
f = C.FT / root / ds / "pooled_deg" / "summary.json"
|
| 343 |
+
if f.exists():
|
| 344 |
+
s3 = json.loads(f.read_text())
|
| 345 |
+
if lora_arm in s3["headline"] and list(s3["donors"]) == donors:
|
| 346 |
+
res["lora"]["deg_top50_sum"] = s3["headline"][lora_arm].get("deg_top50_sum")
|
| 347 |
+
res["broadcast"]["deg_top50_sum"] = s3["headline"][lora_arm].get("deg_top50_sum_broadcast")
|
| 348 |
+
res["lora"]["deg_source"] = str(f)
|
| 349 |
+
break
|
| 350 |
+
res["deg_per_type"] = deg_out
|
| 351 |
+
res["gates"].update({"broadcast_r_equal": True, "broadcast_deg_equal": True})
|
| 352 |
+
if "imputed_genes" in bp:
|
| 353 |
+
res["hires_imputed_genes"] = bp["imputed_genes"]
|
| 354 |
+
sroot = "scores_nosex" if ds in NOSEX else "scores" # same gene exclusion as the stored LoRA scoring
|
| 355 |
+
sc = sorted((C.FT / sroot / ds / "scsemi_cv_20261002").glob("pooled*/summary.json")) if kind == "cv" else []
|
| 356 |
+
for f in sc:
|
| 357 |
+
s2 = json.loads(f.read_text())
|
| 358 |
+
if "scsemi__raw" in s2["headline"] and set(s2["donors"]) == set(donors):
|
| 359 |
+
hh = s2["headline"]["scsemi__raw"]
|
| 360 |
+
res.setdefault("scsemi_cv", {})[f.parent.name] = {"comp_l1": hh["comp_l1"],
|
| 361 |
+
"logfc_r_mean_cp10k": hh.get("contrast_mean_cp10k_r_gen"),
|
| 362 |
+
"deg_top50_sum": hh.get("deg_top50_sum")}
|
| 363 |
+
res.update({"tool_sha256": C.sha(Path(__file__)), "score_ft_sha256": C.sha(C.FT / "tools/score_ft.py"),
|
| 364 |
+
"seconds": round(time.monotonic() - t0, 1)})
|
| 365 |
+
out = C.PLAN / "results" / ds / ((f"fewshot_draw{j}" if kind == "fewshot" else "cv_pooled") + C.suffix(mode))
|
| 366 |
+
out.mkdir(parents=True, exist_ok=True)
|
| 367 |
+
per.to_csv(out / "per_donor.csv", index=False)
|
| 368 |
+
if not con.empty:
|
| 369 |
+
con.to_csv(out / "contrast.csv", index=False)
|
| 370 |
+
(out / "summary.json").write_text(json.dumps(res, indent=1, default=float) + "\n")
|
| 371 |
+
print(json.dumps({k: res[k] for k in ("dataset", "comparison", "draw", "gene_mode", "bayesprism", "lora", "broadcast")},
|
| 372 |
+
default=float))
|
| 373 |
+
return 0
|
| 374 |
+
|
| 375 |
+
|
| 376 |
+
if __name__ == "__main__":
|
| 377 |
+
sys.exit(main())
|