Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores_nosex/giroux2022/zs_K2000/README.md

@@ -0,0 +1,79 @@
+# score_ft.py output: giroux2022, 24 scored donors
+
+Arms: zs_K2000.  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 24 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) (3600 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 0.9981, 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
+   0.9981, 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 = 11) vs control groups ['mild'] (n = 13); 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 11 + 13
+   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_crosscohort_20261002/scores/giroux2022/zs_K2000` (same reference, NPZ sha256 and scored donors; checked per arm); predict_scanvi.py was not run.

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores_nosex/giroux2022/zs_K2000/contrast.csv

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+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
+zs_K2000,T cell,mean_cp10k,2886,0.4899275400358847,0.5864764503055301,0.24256572835016274,0.5571964423650476,11,13,11,13,145034,18065,,,,,,,,,,,
+zs_K2000,T cell,pseudobulk_log1p_cpm,2886,0.3802475268465589,0.6077451868574512,0.17583941828048757,0.4168130047440651,11,13,11,13,145034,18065,,,,,,,,,,,
+zs_K2000,B cell,mean_cp10k,3030,0.15049326389101442,0.48025603753384744,0.021675699308601432,0.27530226504963407,11,13,11,13,17383,4245,,,,,,,,,,,
+zs_K2000,B cell,pseudobulk_log1p_cpm,3030,0.06700591889323838,0.4644022343428308,0.005277623447799423,0.1343108885405301,11,13,11,13,17383,4245,,,,,,,,,,,
+zs_K2000,Monocyte,mean_cp10k,3034,0.36992208465856835,0.6794468550874515,0.25027756359295955,0.28541723120459594,11,13,11,13,47311,9725,,,,,,,,,,,
+zs_K2000,Monocyte,pseudobulk_log1p_cpm,3034,0.24109105722640795,0.6731923266540611,0.18010539664805839,0.1629382667762598,11,13,11,13,47311,9725,,,,,,,,,,,
+zs_K2000,NK cell,mean_cp10k,2931,0.3468322857769599,0.554884628817489,0.2405928205620218,0.2786209554873621,11,13,11,13,29061,2975,,,,,,,,,,,
+zs_K2000,NK cell,pseudobulk_log1p_cpm,2931,0.2090450869983934,0.5368131432639086,0.16935212573637703,0.1253499014840057,11,13,11,13,29061,2975,,,,,,,,,,,
+zs_K2000,T cell,welch_top50_deg,5498,,,,,11,13,11,13,145034,18065,True,0,0.0,50,1,274,1,3,0.05454545454545454,8,8
+zs_K2000,B cell,welch_top50_deg,5498,,,,,11,13,11,13,17383,4245,True,0,,0,0,0,0,0,0.0,8,8
+zs_K2000,Monocyte,welch_top50_deg,5498,,,,,11,13,11,13,47311,9725,True,0,0.0,1,1,1,1,1,0.125,8,8
+zs_K2000,NK cell,welch_top50_deg,5498,,,,,11,13,11,13,29061,2975,True,0,0.0,50,0,62,0,1,0.017543859649122806,8,8
+zs_K2000,DC,welch_top50_deg,5498,,,,,11,13,11,13,4328,657,True,0,,0,0,0,0,0,0.0,8,8

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores_nosex/giroux2022/zs_K2000/per_donor.csv

@@ -0,0 +1,25 @@
+arm,donor,group,n_generated_cells,n_observed_cells,n_reference_cells,gen_T cell,obs_T cell,gen_B cell,obs_B cell,gen_Monocyte,obs_Monocyte,gen_NK cell,obs_NK cell,gen_DC,obs_DC,gen_Other,obs_Other,comp_l1,comp_l1_obs_loo_null,pb_r,pb_own_rank,pb_top1,precision,coverage,precision_depth_matched,coverage_depth_matched,mono_pct_gen,mono_pct_obs,median_umi_gen,median_umi_obs
+zs_K2000,450905_d0,mild,1500,5914,150,0.216,0.30943523841731485,0.03266666666666666,0.036185322962461956,0.642,0.4293202570172472,0.051333333333333335,0.12089956036523504,0.04733333333333333,0.009976327358809604,0.010666666666666666,0.09418329387893135,0.5000734979145531,0.6266504923198378,0.8817465064034697,2,False,0.8933333333333333,0.24666666666666667,0.9866666666666667,0.32666666666666666,64.2,42.93202570172472,1211.0,574.5
+zs_K2000,450905_d14,mild,1500,7407,150,0.46266666666666667,0.4422843256379101,0.07933333333333334,0.02929661131362225,0.324,0.27420008100445525,0.11133333333333334,0.15782368030241664,0.014,0.02983664101525584,0.008666666666666666,0.06655866072633995,0.2404379640880248,0.36155260046011806,0.8902371144458188,7,False,0.9133333333333333,0.32666666666666666,0.96,0.36666666666666664,32.4,27.420008100445525,1048.5,558.0
+zs_K2000,0B943B_d0,mild,1500,11344,150,0.52,0.6220028208744711,0.09266666666666666,0.08312764456981664,0.298,0.12588152327221439,0.064,0.11865303244005641,0.019333333333333334,0.023977433004231313,0.006,0.026357545839210156,0.3633149976492713,0.1791278384873501,0.886844649737469,11,False,0.8666666666666667,0.28,0.94,0.32,29.799999999999997,12.588152327221438,949.5,539.0
+zs_K2000,0B943B_d14,mild,1500,15650,150,0.376,0.5652396166134185,0.154,0.11175718849840256,0.342,0.17086261980830672,0.10333333333333333,0.09789137380191694,0.016666666666666666,0.009968051118210863,0.008,0.04428115015974441,0.4510415335463258,0.10063602424453227,0.8868164993237605,1,True,0.92,0.2,0.9866666666666667,0.3,34.2,17.08626198083067,921.0,461.0
+zs_K2000,7085CA_d0,mild,1500,12585,150,0.18933333333333333,0.21660707191100517,0.036,0.024791418355184745,0.7053333333333334,0.5874453714739769,0.04466666666666667,0.13198251887167262,0.018666666666666668,0.017719507350019866,0.006,0.021454112038140644,0.26008740564163696,0.8829287440863147,0.8988383151665826,1,True,0.86,0.20666666666666667,0.9533333333333334,0.24,70.53333333333333,58.74453714739769,1315.5,640.0
+zs_K2000,7085CA_d7,mild,1500,9515,150,0.5633333333333334,0.521492380451918,0.11066666666666666,0.0722017866526537,0.16133333333333333,0.10110352075669994,0.13866666666666666,0.2725170782974251,0.018666666666666668,0.019863373620599055,0.007333333333333333,0.012821860220704152,0.2810712909441233,0.3632282731474772,0.8932516033210693,7,False,0.9066666666666666,0.26666666666666666,0.9933333333333333,0.30666666666666664,16.133333333333333,10.110352075669994,931.0,502.0
+zs_K2000,7085CA_d14,mild,1500,11887,150,0.5826666666666667,0.472533019264743,0.1,0.07133843694792631,0.17133333333333334,0.16210986792294102,0.12733333333333333,0.24270211155043325,0.012666666666666666,0.018591738874400604,0.006,0.032724825439555814,0.2960373517287793,0.2993283707516418,0.8950101255856584,2,False,0.9266666666666666,0.34,0.96,0.42,17.133333333333333,16.2109867922941,890.5,536.0
+zs_K2000,2AD75E_d0,mild,1500,7693,150,0.4633333333333333,0.6034056934875861,0.134,0.06408423241908229,0.268,0.14064734173924348,0.09266666666666666,0.15949564539191471,0.024666666666666667,0.025347718705316522,0.017333333333333333,0.007019368256856883,0.41516478183630146,0.20681142084856263,0.8671495507952579,23,False,0.94,0.17333333333333334,1.0,0.22,26.8,14.064734173924348,775.5,532.0
+zs_K2000,2AD75E_d7,mild,1500,10499,150,0.4246666666666667,0.5808172206876845,0.22066666666666668,0.14372797409277074,0.224,0.10277169254214687,0.10466666666666667,0.14715687208305553,0.008,0.013715591961139155,0.018,0.011810648633203162,0.4087127027970917,0.27874882058987843,0.8235212959384495,23,False,0.9733333333333334,0.12,0.9933333333333333,0.18666666666666668,22.400000000000002,10.277169254214687,619.0,443.0
+zs_K2000,2AD75E_d14,mild,1500,13692,150,0.5426666666666666,0.5811422728600643,0.15,0.12503651767455448,0.19333333333333333,0.14198071866783524,0.078,0.12496348232544552,0.025333333333333333,0.016579024247735902,0.010666666666666666,0.010297984224364592,0.17087817703768624,0.19762923258298543,0.8859144645092987,19,False,0.9333333333333333,0.06666666666666667,0.9866666666666667,0.12,19.333333333333332,14.198071866783524,941.0,428.0
+zs_K2000,B85D75_d0,severe,1500,11441,150,0.6913333333333334,0.7173324010138974,0.09266666666666666,0.031203566121842496,0.13466666666666666,0.11441307578008915,0.06733333333333333,0.06039681845992483,0.008666666666666666,0.016519535005681322,0.005333333333333333,0.06013460361856481,0.17730641260962032,0.35882358586377083,0.8892217884406611,10,False,0.94,0.36,0.9866666666666667,0.5666666666666667,13.466666666666665,11.441307578008916,955.0,347.0
+zs_K2000,B85D75_d7,severe,1500,12328,150,0.5846666666666667,0.6435756002595717,0.14,0.06448734587929916,0.18466666666666667,0.13741077222582737,0.07,0.04201817001946788,0.011333333333333334,0.027741726151849447,0.009333333333333334,0.08476638546398442,0.30150075708414453,0.28301793033829503,0.8921712312986567,4,False,0.9333333333333333,0.2,0.9933333333333333,0.2866666666666667,18.46666666666667,13.741077222582737,936.0,363.0
+zs_K2000,180E1A_d0,severe,1500,7157,150,0.532,0.7019700992035769,0.13533333333333333,0.04135811094033813,0.21333333333333335,0.13175911694844208,0.08133333333333333,0.0655302501047925,0.024,0.013273718038284197,0.014,0.04610870476456616,0.40415760793628613,0.31372800769589615,0.8875183693952524,7,False,0.9533333333333334,0.44666666666666666,0.98,0.4666666666666667,21.333333333333336,13.175911694844208,867.0,433.0
+zs_K2000,180E1A_d7,severe,1500,10591,150,0.6266666666666667,0.7348692285903126,0.12,0.04909829100179398,0.15933333333333333,0.09064299877254273,0.07733333333333334,0.08280615617033331,0.01,0.010480596733075253,0.006666666666666667,0.03210272873194221,0.27918408711799325,0.37628768415060193,0.8891025008776866,9,False,0.94,0.3933333333333333,1.0,0.5133333333333333,15.933333333333334,9.064299877254273,901.5,408.0
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paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores_nosex/giroux2022/zs_K2000/summary.json

@@ -0,0 +1,809 @@
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