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/fewshot_n5/draw2/per_donor.csv

@@ -0,0 +1,49 @@
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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/fewshot_n5/draw2/summary.json

@@ -0,0 +1,1390 @@
+{
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+  "--case-groups",
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+  "--arm",
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+  "--arm",
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+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw2",
+  "--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",
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+  "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/fewshot_n5/draw2"
+}

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw2_scsemi5/README.md

@@ -0,0 +1,79 @@
+# score_ft.py output: giroux2022, 12 scored donors
+
+Arms: scsemi5.  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 12 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) (1800 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 0.9989, 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
+   0.9994, 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 = 6); 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 + 6
+   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/chrY_nonPAR_genes.csv` (sha 1962e57ca198): 43 genes listed, 7 of them scoring genes (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 5556 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/draw2_scsemi5` (same reference, NPZ sha256 and scored donors; checked per arm); predict_scanvi.py was not run.

paper-releases/pbisc-diffusion/updates/20261001T023742Z-deck-calibration-giroux-sexgenes/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores_nochrY/giroux2022/fewshot_n5/draw2_scsemi5/contrast.csv

@@ -0,0 +1,14 @@
+arm,celltype,statistic,n_genes,r_generated,r_broadcast,partial_r_generated_given_broadcast,r_generated_vs_broadcast,n_case_donors_obs,n_control_donors_obs,n_case_donors_gen,n_control_donors_gen,n_cells_obs,n_cells_gen,deg_scored,top50_overlap,top50_jaccard,obs_top_list_size,gen_top_list_size,n_sig_obs,n_sig_gen,top50_overlap_broadcast,top50_jaccard_broadcast,broadcast_top_list_size,n_sig_broadcast
+scsemi5,T cell,mean_cp10k,2888,0.4309727369514002,0.3655194961458824,0.3326671508962687,0.404217490770922,6,6,6,6,67243,10280,,,,,,,,,,,
+scsemi5,T cell,pseudobulk_log1p_cpm,2888,0.32120308861552305,0.48277319445489014,0.18043953587431388,0.35995366979866167,6,6,6,6,67243,10280,,,,,,,,,,,
+scsemi5,B cell,mean_cp10k,3018,0.19967100737535526,0.34416431629040306,0.11481619305025643,0.2794087198836022,6,6,6,6,10555,817,,,,,,,,,,,
+scsemi5,B cell,pseudobulk_log1p_cpm,3018,0.09576524586736682,0.4452233155685807,0.011058428249972226,0.19327382959285652,6,6,6,6,10555,817,,,,,,,,,,,
+scsemi5,Monocyte,mean_cp10k,3047,0.47964713244401386,0.5795391640255549,0.30527201023564887,0.442726040314773,6,6,6,6,25096,4366,,,,,,,,,,,
+scsemi5,Monocyte,pseudobulk_log1p_cpm,3047,0.27122122061624654,0.5895661684053563,0.12189420472968962,0.3007697537072179,6,6,6,6,25096,4366,,,,,,,,,,,
+scsemi5,NK cell,mean_cp10k,2920,0.36747532802275057,0.30833037919796075,0.2848274709461476,0.3783995604602179,6,6,6,6,16282,1995,,,,,,,,,,,
+scsemi5,NK cell,pseudobulk_log1p_cpm,2920,0.2088575868696494,0.39209933870428326,0.11525401935260736,0.27249484463062645,6,6,6,6,16282,1995,,,,,,,,,,,
+scsemi5,T cell,welch_top50_deg,5556,,,,,6,6,6,6,67243,10280,True,0,0.0,27,0,27,0,0,0.0,0,0
+scsemi5,B cell,welch_top50_deg,5556,,,,,6,6,6,6,10555,817,True,0,0.0,27,1,27,1,0,0.0,0,0
+scsemi5,Monocyte,welch_top50_deg,5556,,,,,6,6,6,6,25096,4366,True,0,,0,0,0,0,0,,0,0
+scsemi5,NK cell,welch_top50_deg,5556,,,,,6,6,6,6,16282,1995,True,0,0.0,10,1,10,1,0,0.0,0,0
+scsemi5,DC,welch_top50_deg,5556,,,,,6,6,6,6,1911,80,True,0,,0,0,0,0,0,,0,0