Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/pooled/per_donor.csv

@@ -0,0 +1,145 @@
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paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/pooled/summary.json

@@ -0,0 +1,2010 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "giroux2022",
+ "written_utc": "2026-10-01T01:52:48.003600+00:00",
+ "command": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
+  "--dataset",
+  "giroux2022",
+  "--threads",
+  "8",
+  "--arm",
+  "pseudo_full__cal=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/pooled/pseudo_full__cal.npz",
+  "--arm",
+  "pseudo_full__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/pooled/pseudo_full__raw.npz",
+  "--arm",
+  "real_lora__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/pooled/real_lora__raw.npz",
+  "--arm",
+  "zs__cal=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/pooled/zs__cal.npz",
+  "--arm",
+  "zs__pseudo=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/pooled/zs__pseudo.npz",
+  "--arm",
+  "zs__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/giroux2022/pooled/zs__raw.npz",
+  "--case-groups",
+  "severe",
+  "--control-groups",
+  "mild",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/pooled",
+  "--exclude-genes",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/chrY_nonPAR_genes.csv",
+  "--reuse-labels",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/pooled"
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+ },
+ "seconds": 27.7,
+ "gene_exclusion": {
+  "file": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/chrY_nonPAR_genes.csv",
+  "sha256": "1962e57ca198323fb47c42bc53926b247086073d9293581b994cb2a2f651bb28",
+  "n_listed": 43,
+  "n_scoring_genes": 5563,
+  "n_dropped_from_scoring_genes": 7,
+  "n_scoring_genes_kept": 5556,
+  "dropped": [
+   {
+    "gene_id": "ENSG00000067646",
+    "symbol": "ZFY"
+   },
+   {
+    "gene_id": "ENSG00000099715",
+    "symbol": "PCDH11Y"
+   },
+   {
+    "gene_id": "ENSG00000114374",
+    "symbol": "USP9Y"
+   },
+   {
+    "gene_id": "ENSG00000067048",
+    "symbol": "DDX3Y"
+   },
+   {
+    "gene_id": "ENSG00000183878",
+    "symbol": "UTY"
+   },
+   {
+    "gene_id": "ENSG00000165246",
+    "symbol": "NLGN4Y"
+   },
+   {
+    "gene_id": "ENSG00000012817",
+    "symbol": "KDM5D"
+   }
+  ],
+  "applies_to": "group contrast (logFC r generated / broadcast / partial: dropped from each type's detected-gene set) and top-50 DEG (observed, generated and broadcast tests: columns dropped before log1p CPM, Welch and BH); all other metrics use every scoring gene"
+ },
+ "labels_reused_from": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/pooled"
+}

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nosex/README.md

@@ -0,0 +1,65 @@
+# Scores without sex-linked genes (2026-10-01)
+
+**Why.** Giroux 2022's scored contrast (moderate = GEO "severe" vs mild) is fully confounded with sex: the scored
+moderate subjects are 4 F / 0 M, the mild ones 0 F / 5 M (all paired subjects 4 F / 1 M vs 0 F / 5 M; the one moderate
+male failed the identity check). Sex from `data/giroux2022/features.npz` `sex_bulk_expr` (bulk chrY expression),
+identical to `sex_sc_expr` and GEO sex. User decision 2026-10-01: Giroux's disease-signal metrics are reported without
+sex-linked genes. **The deck (section 9) reads Giroux's disease metrics from here**; every other cohort keeps `scores/`.
+
+**Gene list.** `data/sex_linked_genes/sex_linked_genes_exclude.csv`: 196 genes = 43 chrY non-PAR + 153 X genes that
+escape X inactivation (Tukiainen et al., Nature 2017, Suppl. Table 13: Escape, Variable, new escape calls); PAR genes
+kept. See `data/sex_linked_genes/README.md`. 65 of them are Giroux scoring genes (5,563 → 5,498); 67 for the other
+cohorts.
+
+**How.** `tools/rescore_excluding_genes.py` replays each original `score_ft.py` call (command from its `summary.json`)
+into this tree with `--exclude-genes` and `--reuse-labels <original dir>`. Nothing in `scores/` is written.
+- `--exclude-genes` drops the genes from the disease-signal scores only: group-contrast logFC r, broadcast r and
+  partial r (removed from each cell type's detected-gene set; per-cell CP10K libraries unchanged, as in the observed
+  reference cache) and the top-50 DEG tests of the observed, generated and broadcast sides (columns removed before
+  log1p CPM, Welch and BH).
+- `--reuse-labels` takes each arm's frozen scANVI labels from the original call (same reference sha, NPZ sha256, donors
+  and NPZ rows checked); predict_scanvi.py was not run.
+- Composition, precision/coverage, pseudobulk r and depth are therefore byte-identical to `scores/` (asserted by the
+  deck script). With `--reuse-labels` and no exclusion, `per_donor.csv`, `contrast.csv` and the DEG lists of the Giroux
+  pooled call reproduce the originals byte for byte.
+
+| dir | content |
+|---|---|
+| `giroux2022/pooled` | 5-fold pooled call, 6 arms (used by the deck) |
+| `giroux2022/fewshot_n5/draw<j>`, `draw<j>_scsemi5` | 5-donor draws: zs_raw, zs_pseudo, zs_cal, lora5; scSemiProfiler (used by the deck) |
+| `ye2026_sepsis/pooled_deg`, `imperial_eskd/pooled`, `ji2025_chili/pooled` | sensitivity only (5 arms: zs__raw, zs__cal, pseudo_full__cal, pseudo_full__raw, real_lora__raw); Sepsis and Imperial NPZs were read from a temporary copy of the server_2 files (`--npz-map`, condition sha checked) |
+
+`../scores_nochrY/giroux2022/` is the same Giroux rescoring with only the 43 chrY non-PAR genes excluded
+(`data/sex_linked_genes/chrY_nonPAR_genes.csv`), a sensitivity check.
+
+## Giroux, before and after (logFC r = mean of T/B/Mono/NK; partial r = beyond the raw-bulk broadcast)
+
+5-fold pooled (24 samples; arm broadcast = its own input bulk; the deck's broadcast line is the raw-bulk one):
+
+| arm | logFC r | partial r | top-50 DEG | broadcast r | broadcast DEG |
+|---|---|---|---|---|---|
+| zero-shot, real bulk | 0.416 → 0.334 | 0.192 → 0.190 | 20 → 1 | 0.638 → 0.575 | 39 → 5 |
+| zero-shot + calibration | 0.305 → 0.269 | 0.138 → 0.133 | 12 → 0 | 0.626 → 0.564 | 38 → 2 |
+| sc-only FT (+ calibration) | 0.374 → 0.344 | 0.174 → 0.175 | 7 → 0 | 0.626 → 0.564 | 38 → 2 |
+| sc-only FT, real bulk | 0.433 → 0.398 | 0.326 → 0.319 | 13 → 0 | 0.638 → 0.575 | 39 → 5 |
+| LoRA, all training pairs | 0.504 → 0.461 | 0.332 → 0.323 | 25 → 5 | 0.638 → 0.575 | 39 → 5 |
+| observed DEG list size (max) | | | 123 → 101 | | |
+
+5 donors (mean over 5 draws; 2 vs 2 test subjects per draw):
+
+| arm | logFC r | partial r | top-50 DEG | broadcast r (raw bulk) | broadcast DEG |
+|---|---|---|---|---|---|
+| zero-shot, real bulk | 0.297 → 0.255 | 0.108 → 0.103 | 4.4 → 0.0 | 0.539 → 0.492 | 25.4 → 0.0 |
+| zero-shot + calibration | 0.242 → 0.228 | 0.112 → 0.111 | 1.8 → 0.0 | | |
+| scSemiProfiler, 5 donors | 0.398 → 0.397 | 0.239 → 0.242 | 1.2 → 0.0 | | |
+| LoRA, 5 donors | 0.331 → 0.297 | 0.194 → 0.189 | 11.6 → 0.2 | | |
+| observed DEG list size (max, mean over draws) | | | 71 → 28.8 | | |
+
+- The real-bulk broadcast still beats every arm on Giroux logFC r (pooled 0.575 vs best arm 0.461; 5 donors 0.492 vs
+  0.397). On top-50 DEG nothing is left to beat: after the exclusion the Giroux DEG overlaps are 0–5 for every arm and
+  for the broadcast (pooled LoRA 5 = broadcast 5).
+- chrY-only exclusion gives almost the same numbers (pooled LoRA r 0.466, broadcast r 0.579, DEG 7 vs 11), so the chrY
+  genes carry most of the confound.
+- Sensitivity, full exclusion on the sex-balanced cohorts (pooled): every logFC r and partial r moves by ≤ 0.007 and
+  top-50 DEG by ≤ 1 (Sepsis LoRA r 0.527 → 0.526, Imperial 0.819 → 0.820, Ji 0.479 → 0.482; DEG 23 → 23, 143 → 143,
+  15 → 14).

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/fewshot_n5/draw0/README.md

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+# score_ft.py output: giroux2022, 11 scored donors
+
+Arms: zs_raw, zs_pseudo, zs_cal, lora5.  Every arm is scored on the same donors against the same observed reference.
+
+## Inputs and fixed contracts
+- Observed reference: `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/reference.npz` (sha dd0152f7bde4), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022;
+  observed cells sum exactly to the features pseudobulk of every donor. Observed labels: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/observed_labels/labels/labels.parquet.
+- Generated cells: `generated_counts` read out once with Poisson(seed 20260916) over the **whole NPZ in row order**
+  (`poisson_readout.poisson_integerise`), then the scored donors' rows. These integer counts are used for labels, embedding,
+  depth and the contrast. Pseudobulk uses the summed `generated_counts` (rates), as `score_generated.py`.
+- Labels: frozen f01common scANVI (`predict_scanvi.py`, CPU, zero query epochs), six-type gate ['T cell', 'B cell', 'Monocyte', 'NK cell', 'DC', 'Other']; query genes =
+  the 1,006-gene annotation list minus genes the dataset's sc assay did not measure in every sample (1006 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5563 genes).
+
+## Definitions
+1. **Composition**: fraction of each donor's cells per six-type label (all generated cells; all observed QC cells).
+   `comp_l1` = sum over the six labels of |generated - observed| (immicro_common.l1). `comp_l1_obs_loo_null` = L1 of the
+   donor's observed composition to the mean of the *other scored donors'* observed compositions. `composition_top1` =
+   fraction of donors whose generated composition is closest (L1) to their own observed composition.
+2. **Pseudobulk**: `pb_r` = Pearson r of CPM-log1p(summed generated_counts) vs CPM-log1p(observed pseudobulk) on the scoring
+   genes (evaluate_realbulk.cpm_log1p / paired_r). `pb_top1` = the donor's generated pseudobulk is closest (Pearson) to its own
+   observed pseudobulk among the 11 scored donors (immicro_common.retrieval = evaluate_realbulk.top1 arithmetic).
+3. **Precision / coverage / density** (stage_b_embed.py functions): per-cell CP10K over the scoring genes + log1p; PCA-50
+   (randomized, random_state 20260925) and k = 10 real-real k-NN balls fitted on **R1 of the scored donors only**.
+   Reference rule (independent of fold and arm): per donor, cells in canonical order (library_id+barcode), one
+   permutation from `default_rng([20260925, uint64(sha256(donor)[:8])])`; R1 = first n, R2 = next n,
+   n = min(150, n_cells // 2) (1650 R1 cells here). Generated query: per donor n cells (the donor's R1 size),
+   without replacement, `default_rng([20260926, same key])`. Precision = fraction of generated cells inside any R1 ball
+   (Kynkaanniemi 2019); coverage = fraction of R1 cells whose ball holds a generated cell; density = Naeem 2020.
+   Per-donor precision = that donor's generated cells; per-donor coverage = that donor's R1 cells (the pooled values are
+   their n-weighted means; plain means when every donor has 150 reference cells). R2 (held-out real cells of the same donors) is the
+   perfect-generator control: precision 0.9988, coverage 1.0000.
+   **Depth matched**: stage_b_embed depth matching run pairwise (R1 vs this arm): per donor and within-donor rank of detected
+   genes, both cells are thinned by UMI sampling without replacement to the smaller detected-gene count; PCA kept from
+   un-thinned R1, radii recomputed on thinned R1; fresh rng(20260929) per arm. R2 control: precision
+   0.9982, coverage 1.0000.
+4. **Monocyte %** = 100 x Monocyte fraction (all cells) per donor; **median UMI per cell** = median over cells of the
+   Poisson-read (generated) / observed counts summed over the scoring genes (per donor, and pooled).
+5. **Group contrast** (T, B, Monocyte, NK): case groups ['severe'] (n = 6) vs control groups ['mild'] (n = 5); rule: explicit --case-groups / --control-groups; groups from features groups.
+   Genes per type = scoring genes detected (count > 0) in >= 1 % of the scored donors' observed
+   cells of that type. Primary statistic `mean_cp10k`: per-cell CP10K over the scoring genes; per donor the mean over its
+   cells of the type (donors with >= 1 such cell); group mean = mean over the group's donors (donor-balanced);
+   logFC = log2(case mean + 1) - log2(control mean + 1). Secondary `pseudobulk_log1p_cpm`: the SLE benchmark evaluator's
+   statistic (donor x type summed counts, log1p CPM over the scoring genes, mean case - mean control;
+   evaluate_generated_vs_truth.group_log_fold_change). Both are computed on the generated cells (their frozen labels) and on
+   the observed cells; `r_generated` = Pearson r of the two logFC vectors. Broadcast baseline `r_broadcast`: the same
+   observed vector against the logFC of the input bulk (condition NPZ `bulk_counts` of the arm, scoring genes, per-donor
+   CP10K resp. log1p CPM, group means). `partial_r_generated_given_broadcast`: r of the OLS residuals of observed and
+   generated logFC on the bulk logFC (residual_specificity.py).
+6. **Top-50 DEG overlap** (contrast.csv rows `statistic = welch_top50_deg`; summary `deg_top50`): the SLE benchmark's score (`total_top50_overlap` of
+   `bulk2sc-paper/scripts/evaluate_generated_vs_truth.py`, Welch backend: the "top-50 DEG 81 / 84" of the final model), run
+   with the evaluator's own functions on the same donor x type pseudobulks as the contrast. Per cell type, observed and
+   generated separately: donor x type summed counts on all 5563 scoring genes (donors of the two groups with
+   >= 1 cell of the type) -> `welch_bh_deg` (log1p CPM, two-sided Welch t-test case vs control, Benjamini-Hochberg over the
+   genes with a defined p-value; needs >= 2 donors per group) -> `top_deg_genes` (FDR < 0.05, descending |logFC|,
+   first 50; shorter when fewer genes pass). `top50_overlap` = |observed list & generated list|; `top50_jaccard`
+   the evaluator's Jaccard. Summary `sum` = total over the scored types (the quantity reported as 81), `max` = the sum of
+   the observed list lengths (the "of 262"), `sum_4type` = T + B + Monocyte + NK. A type is scored when its observed test
+   is defined; an arm whose generated test is undefined for a scored type gets overlap 0. Types: T cell, B cell, Monocyte, NK cell, DC
+   (aggregates: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/giroux2022/deg_reference.npz).
+   **Broadcast analogue** (`top50_overlap_broadcast`, as `sle_broadcast.py` `broadcast_input_bulk`; not part of the
+   published benchmark): the same test on the arm's condition bulk_counts (scoring genes, the 6 + 5
+   donors), one whole-PBMC top-50 list, compared with every type's observed list.
+
+## Files
+- `per_donor.csv`: one row per arm x donor (all per-donor quantities above).
+- `summary.json`: per arm the donor means / pooled values, provenance (NPZ sha, receipt check, generation metadata,
+  Poisson nnz of the whole NPZ, annotation contract), and the reference block (PCA, R2 control).
+- `contrast.csv`: per arm x cell type x statistic (`mean_cp10k`, `pseudobulk_log1p_cpm`: logFC r; `welch_top50_deg`:
+  top-50 DEG overlap, T / B / Monocyte / NK / DC; columns of the other kind are empty).
+- `arms/<arm>/labels/` (predict_scanvi.py labels + manifest), `arms/<arm>/generated_cells.parquet` (NPZ row, donor, label,
+  UMI on the scoring genes, P/C subsample flag), `arms/<arm>/deg_top50_lists.csv` (observed, generated and broadcast
+  top-50 gene lists, Ensembl ids, rank order).
+
+## Gene exclusion (--exclude-genes)
+`/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/sex_linked_genes_exclude.csv` (sha e3a0f3f225d9): 196 genes listed, 65 of them scoring genes (XG, PRKX, NLGN4X, PUDP, STS, ANOS1, FAM9B, TBL1X, SHROOM2, AP1S2, CTPS2, TXLNG, NHS, BEND2, PPEF1, CNKSR2, PHEX, PTCHD1, IL1RAPL1, LANCL3, XK, MED14, EFHC2, KDM6A, ZNF157, TIMP1, RIBC1, VSIG4, CXorf65, NHSL2, P2RY10, GPR174, HMGN5, ZNF711, POF1B, DACH2, PCDH11X, SYTL4, XKRX, ZMAT1, IL1RAPL2, NUP62CL, FRMPD3, MID2, COL4A6, TRPC5, RTL4, GRIA3, SH2D1A, ZNF280C, HS6ST2, PLAC1, FHL1, CD40LG, FGF13, AFF2, GABRA3, PNMA3, ZFY, PCDH11Y, USP9Y, DDX3Y, UTY, NLGN4Y, KDM5D). Applies to: group contrast (logFC r generated / broadcast / partial: dropped from each type's detected-gene set) and top-50 DEG (observed, generated and broadcast tests: columns dropped before log1p CPM, Welch and BH); all other metrics use every scoring gene. Definitions 5 and 6 above therefore run on 5498 of the 5563 scoring genes.
+
+## Labels
+Frozen scANVI labels reused from `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/fewshot_n5/draw0` (same reference, NPZ sha256 and scored donors; checked per arm); predict_scanvi.py was not run.