Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw4_scsemi5/README.md

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+# score_ft.py output: ye2026_sepsis, 77 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/ye2026_sepsis/reference.npz` (sha bac12da8a241), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ye2026_sepsis;
+  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/ye2026_sepsis/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 (5975 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 77 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) (11550 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 0.9807, coverage 0.9997.
+   **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
+   0.9795, coverage 0.9998.
+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 ['adult_sepsis', 'ped_sepsis'] (n = 57) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 20); 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 5975 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/ye2026_sepsis/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 57 + 20
+   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).

paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw4_scsemi5/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
+scsemi5,T cell,mean_cp10k,3259,0.42462090347305576,0.2635493097811775,0.3486544124480115,0.5251419606939168,57,20,57,20,277774,53597,,,,,,,,,,,
+scsemi5,T cell,pseudobulk_log1p_cpm,3259,0.18092161814376057,0.2326120649060939,0.11895722401352994,0.3039434897568695,57,20,57,20,277774,53597,,,,,,,,,,,
+scsemi5,B cell,mean_cp10k,3311,0.4425788280300799,0.2759123555261431,0.36460885815571925,0.5161798816406824,57,20,57,20,92596,11156,,,,,,,,,,,
+scsemi5,B cell,pseudobulk_log1p_cpm,3311,0.14787985734968617,0.25121490410279235,0.08907963621791537,0.2569595435142086,57,20,57,20,92596,11156,,,,,,,,,,,
+scsemi5,Monocyte,mean_cp10k,3304,0.44937324039359416,0.5497635656505115,0.24194544043578112,0.49876761556354693,57,20,55,20,98968,37612,,,,,,,,,,,
+scsemi5,Monocyte,pseudobulk_log1p_cpm,3304,0.11744297797785887,0.6130579062755492,-0.10215337971736418,0.3164474298064529,57,20,55,20,98968,37612,,,,,,,,,,,
+scsemi5,NK cell,mean_cp10k,3235,0.4954356771906141,0.18915162420474085,0.46663868491449495,0.4127005161645233,57,20,52,20,45385,2688,,,,,,,,,,,
+scsemi5,NK cell,pseudobulk_log1p_cpm,3235,0.17685562333973642,0.05607162520288094,0.1701407750883899,0.16715050894177844,57,20,52,20,45385,2688,,,,,,,,,,,
+scsemi5,T cell,welch_top50_deg,5975,,,,,57,20,57,20,277774,53597,True,2,0.031746031746031744,50,15,1511,15,3,0.030927835051546393,50,1128
+scsemi5,B cell,welch_top50_deg,5975,,,,,57,20,57,20,92596,11156,True,0,0.0,50,23,932,23,1,0.010101010101010102,50,1128
+scsemi5,Monocyte,welch_top50_deg,5975,,,,,57,20,55,20,98968,37612,True,0,0.0,50,0,2357,0,13,0.14942528735632185,50,1128
+scsemi5,NK cell,welch_top50_deg,5975,,,,,57,20,52,20,45385,2688,True,0,0.0,50,8,1071,8,2,0.02040816326530612,50,1128
+scsemi5,DC,welch_top50_deg,5975,,,,,53,20,55,20,3031,3383,True,0,0.0,50,16,1473,16,1,0.010101010101010102,50,1128

paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw4_scsemi5/per_donor.csv

@@ -0,0 +1,78 @@
+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
+scsemi5,Abd-PS04,ped_sepsis,1500,5449,150,0.246,0.14094329234721967,0.02266666666666667,0.17030647825289044,0.61,0.5608368507983116,0.02266666666666667,0.02147182969352175,0.005333333333333333,0.003853918150119288,0.09333333333333334,0.10258763075793724,0.31378821802165535,0.8357947379641445,0.7099588835599341,2,False,0.8866666666666667,0.17333333333333334,0.38,0.30666666666666664,61.0,56.083685079831156,1798.0,2716.0
+scsemi5,Abd-PS05,ped_sepsis,1500,6924,150,0.554,0.30387059503177355,0.154,0.5060658578856152,0.03933333333333333,0.07019064124783363,0.017333333333333333,0.0891103408434431,0.01,0.008954361640670132,0.22533333333333333,0.021808203350664357,0.9094003466204506,0.6929612909185169,0.5109476232663583,30,False,0.7333333333333333,0.06,0.52,0.15333333333333332,3.933333333333333,7.0190641247833625,1795.0,2313.0
+scsemi5,Abd-PS07,ped_sepsis,1500,3472,150,0.9433333333333334,0.8626152073732719,0.0006666666666666666,0.023329493087557603,0.0,0.002304147465437788,0.0,0.09331797235023041,0.002,0.0002880184331797235,0.054,0.018145161290322582,0.23656989247311827,0.7836127509636722,0.4458837620484545,45,False,1.0,0.11333333333333333,0.8133333333333334,0.08666666666666667,0.0,0.2304147465437788,1797.0,1614.0
+scsemi5,Abd-PS19,ped_sepsis,1500,7984,150,0.43533333333333335,0.4622995991983968,0.10533333333333333,0.11209919839679358,0.382,0.2937124248496994,0.02666666666666667,0.05586172344689379,0.016666666666666666,0.0156563126252505,0.034,0.06037074148296593,0.17859585838343353,0.22953233206814283,0.6644497110827896,14,False,0.7466666666666667,0.08,0.37333333333333335,0.12666666666666668,38.2,29.37124248496994,1797.0,2111.0
+scsemi5,Abd-PS20,ped_sepsis,1500,7286,150,0.758,0.30496843261048584,0.11466666666666667,0.5780949766675817,0.0033333333333333335,0.08770244304144935,0.0,0.007823222618720834,0.088,0.0005489980785067252,0.036,0.02086192698325556,1.111241284655504,0.8181735464911584,0.49178985844470574,32,False,0.54,0.02666666666666667,0.42,0.09333333333333334,0.33333333333333337,8.770244304144935,1797.0,1820.0
+scsemi5,Abd-PS23,ped_sepsis,1500,4309,150,0.578,0.2529589231840334,0.13466666666666666,0.3272220932931075,0.058,0.32142028312833604,0.03333333333333333,0.013460199582269667,0.005333333333333333,0.002088651659317707,0.19066666666666668,0.08284984915293572,0.9119514195095536,0.6204920498761515,0.5413642382798476,22,False,0.68,0.08,0.4866666666666667,0.16,5.800000000000001,32.1420283128336,1797.0,1832.0
+scsemi5,Abd-PS27,ped_sepsis,1500,9427,150,0.5406666666666666,0.6126021003500584,0.021333333333333333,0.06078285774901877,0.31266666666666665,0.13196138750397793,0.0,0.010289593720165482,0.004666666666666667,0.0029701920016972526,0.12066666666666667,0.1813938686750822,0.3648035076553163,0.5254791951243161,0.5416735648430624,1,True,0.9666666666666667,0.04666666666666667,0.5866666666666667,0.24666666666666667,31.266666666666666,13.196138750397793,1797.0,2356.0
+scsemi5,Abd-S103,adult_sepsis,1500,9423,150,0.682,0.4813753581661891,0.12266666666666666,0.14782977820227103,0.05466666666666667,0.0024408362517245037,0.011333333333333334,0.04393505253104107,0.007333333333333333,0.00021224663058473945,0.122,0.3242067282181895,0.5199431179030033,0.5601428616270965,0.5399465492427687,24,False,0.7266666666666667,0.22666666666666666,0.5666666666666667,0.21333333333333335,5.466666666666667,0.24408362517245036,1795.0,971.0
+scsemi5,Abd-S120,adult_sepsis,1500,5783,150,0.29533333333333334,0.3140238630468615,0.028666666666666667,0.11049628220646723,0.5953333333333334,0.46446481065191075,0.024,0.07297250561992046,0.006666666666666667,0.0008646031471554556,0.05,0.03717793532768459,0.2989853017464984,0.5294419661572208,0.6893111837498442,8,False,0.9666666666666667,0.04,0.3933333333333333,0.22,59.53333333333334,46.44648106519107,1795.0,2845.0
+scsemi5,Abd-S123,adult_sepsis,1500,6299,150,0.564,0.16986823305286552,0.11,0.08779171297031275,0.14133333333333334,0.5027782187648833,0.05466666666666667,0.10938244165740593,0.008666666666666666,0.0007937767899666614,0.12133333333333333,0.1293856167645658,0.8484258877070432,0.8272894440353923,0.635833275031337,5,False,0.6133333333333333,0.18666666666666668,0.43333333333333335,0.24,14.133333333333335,50.277821876488325,1796.5,2261.0
+scsemi5,Abd-S156,adult_sepsis,1500,7004,150,0.648,0.3343803540833809,0.2813333333333333,0.08209594517418618,0.004666666666666667,0.39406053683609366,0.04533333333333334,0.0910908052541405,0.004,0.0012849800114220445,0.016666666666666666,0.0970873786407767,1.0311441081286885,0.504482027006445,0.5406933902455173,21,False,0.66,0.11333333333333333,0.7133333333333334,0.22666666666666666,0.46666666666666673,39.406053683609365,1798.5,1821.0
+scsemi5,Abd-S39,adult_sepsis,1500,4291,150,0.6346666666666667,0.13819622465625728,0.2753333333333333,0.06152412025168958,0.0033333333333333335,0.6933115823817292,0.07066666666666667,0.049638778839431366,0.0,0.0013982754602656724,0.016,0.05593101841062689,1.4626150858385771,1.009689347995204,0.588434363875681,46,False,0.6533333333333333,0.07333333333333333,0.4266666666666667,0.16,0.33333333333333337,69.33115823817292,1793.5,3523.0
+scsemi5,Abd-S42,adult_sepsis,1500,6567,150,0.328,0.5748439165524593,0.018,0.09760925841327851,0.562,0.17039744175422566,0.008,0.05862646566164154,0.03266666666666666,0.0016750418760469012,0.051333333333333335,0.0968478757423481,0.8451883660727882,0.27765231069706203,0.6112122140472781,5,False,0.9133333333333333,0.10666666666666667,0.56,0.18666666666666668,56.2,17.039744175422566,1797.0,1982.0
+scsemi5,Abd-S47,adult_sepsis,1500,7971,150,0.5646666666666667,0.7027976414502571,0.09866666666666667,0.20474219044034625,0.258,0.03224187680341237,0.0,0.019069125580228326,0.034,0.0006272738677706687,0.04466666666666667,0.040521891857985194,0.5265512482749968,0.4991278355156792,0.5504764999196831,4,False,0.30666666666666664,0.08666666666666667,0.3466666666666667,0.15333333333333332,25.8,3.224187680341237,1796.0,2030.0
+scsemi5,Abd-S51,adult_sepsis,1500,6865,150,0.284,0.25273124544792425,0.03,0.269482884195193,0.6086666666666667,0.3551347414420976,0.004,0.03670793882010197,0.02266666666666667,0.0014566642388929353,0.050666666666666665,0.08448652585579024,0.6120213644088369,0.5751267418693101,0.6049706759986028,10,False,0.9266666666666666,0.16666666666666666,0.5666666666666667,0.26,60.86666666666667,35.51347414420976,1796.0,1849.0
+scsemi5,Abd-S54,adult_sepsis,1500,7818,150,0.43866666666666665,0.4854182655410591,0.11866666666666667,0.1364799181376311,0.3566666666666667,0.012918905090816066,0.035333333333333335,0.340752110514198,0.025333333333333333,0.0011511895625479663,0.025333333333333333,0.023279611153747763,0.7399672550524431,0.5236666599143156,0.545724660354844,53,False,0.5466666666666666,0.03333333333333333,0.36666666666666664,0.11333333333333333,35.66666666666667,1.2918905090816066,1796.5,1933.5
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paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw4_scsemi5/summary.json

@@ -0,0 +1,747 @@
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