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/draw2_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 0.9816, coverage 0.9999.
+   **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
+   0.9798, coverage 0.9996.
+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/draw2_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,3250,0.3668495215515692,0.28418337062604826,0.3083494188813424,0.29778477553352783,57,20,57,20,279996,70329,,,,,,,,,,,
+scsemi5,T cell,pseudobulk_log1p_cpm,3250,0.23813613549063514,0.2620470604793019,0.20662696966489605,0.15726595692691142,57,20,57,20,279996,70329,,,,,,,,,,,
+scsemi5,B cell,mean_cp10k,3308,0.2713363351999342,0.301568070894857,0.23669299452484927,0.1612049395950547,57,20,56,20,95354,16804,,,,,,,,,,,
+scsemi5,B cell,pseudobulk_log1p_cpm,3308,0.21593287361502483,0.3165050349105062,0.20441870770976045,0.0711343668805164,57,20,56,20,95354,16804,,,,,,,,,,,
+scsemi5,Monocyte,mean_cp10k,3287,0.24390924211624132,0.5587432606078608,0.26404890567254813,0.04500206196094533,57,20,55,20,107968,17080,,,,,,,,,,,
+scsemi5,Monocyte,pseudobulk_log1p_cpm,3287,-0.013741313444878925,0.6315053831846796,-0.009245290421821964,-0.010408727027405399,57,20,55,20,107968,17080,,,,,,,,,,,
+scsemi5,NK cell,mean_cp10k,3233,0.35869566930561325,0.21023399085209113,0.3181326941904189,0.29061034660972535,57,20,47,17,45869,4231,,,,,,,,,,,
+scsemi5,NK cell,pseudobulk_log1p_cpm,3233,0.0888458400940008,0.08941555025367794,0.07717015084093977,0.14285063885467764,57,20,47,17,45869,4231,,,,,,,,,,,
+scsemi5,T cell,welch_top50_deg,5975,,,,,57,20,57,20,279996,70329,True,3,0.030927835051546393,50,50,1479,413,2,0.02040816326530612,50,1469
+scsemi5,B cell,welch_top50_deg,5975,,,,,57,20,56,20,95354,16804,True,0,0.0,50,2,1019,2,2,0.02040816326530612,50,1469
+scsemi5,Monocyte,welch_top50_deg,5975,,,,,57,20,55,20,107968,17080,True,0,0.0,50,2,2388,2,11,0.12359550561797752,50,1469
+scsemi5,NK cell,welch_top50_deg,5975,,,,,57,20,47,17,45869,4231,True,0,0.0,50,50,1124,169,2,0.02040816326530612,50,1469
+scsemi5,DC,welch_top50_deg,5975,,,,,53,20,55,18,3221,1561,True,3,0.030927835051546393,50,50,1428,61,1,0.010101010101010102,50,1469

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/draw2_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,1500,5449,150,0.152,0.14094329234721967,0.025333333333333333,0.17030647825289044,0.504,0.5608368507983116,0.012666666666666666,0.02147182969352175,0.006,0.003853918150119288,0.3,0.10258763075793724,0.4212303174894475,0.8147806942228808,0.6413718371031905,3,False,0.9533333333333334,0.18,0.5266666666666666,0.24,50.4,56.083685079831156,2180.0,2716.0
+scsemi5,Abd-PS05,ped_sepsis,1500,6924,150,0.9146666666666666,0.30387059503177355,0.03133333333333333,0.5060658578856152,0.017333333333333333,0.07019064124783363,0.0,0.0891103408434431,0.0006666666666666666,0.008954361640670132,0.036,0.021808203350664357,1.2499757365684572,0.6930472386554026,0.46974110142888054,18,False,1.0,0.05333333333333334,0.9,0.11333333333333333,1.7333333333333332,7.0190641247833625,2181.0,2313.0
+scsemi5,Abd-PS07,ped_sepsis,1500,3472,150,0.9106666666666666,0.8626152073732719,0.03133333333333333,0.023329493087557603,0.0,0.002304147465437788,0.0,0.09331797235023041,0.05466666666666667,0.0002880184331797235,0.0033333333333333335,0.018145161290322582,0.22086789554531483,0.805531392820092,0.49190022375645537,31,False,0.9333333333333333,0.1,0.7933333333333333,0.12666666666666668,0.0,0.2304147465437788,2183.0,1614.0
+scsemi5,Abd-PS19,ped_sepsis,1500,7984,150,0.8126666666666666,0.4622995991983968,0.124,0.11209919839679358,0.028,0.2937124248496994,0.0013333333333333333,0.05586172344689379,0.007333333333333333,0.0156563126252505,0.02666666666666667,0.06037074148296593,0.7245357381429525,0.20809386747845918,0.5027116574338087,28,False,0.9866666666666667,0.05333333333333334,0.94,0.08,2.8000000000000003,29.37124248496994,2184.0,2111.0
+scsemi5,Abd-PS20,ped_sepsis,1500,7286,150,0.568,0.30496843261048584,0.304,0.5780949766675817,0.006,0.08770244304144935,0.012,0.007823222618720834,0.08866666666666667,0.0005489980785067252,0.021333333333333333,0.02086192698325556,0.7115948394180621,0.8176933692244222,0.5260117507013453,20,False,0.8866666666666667,0.04,0.6333333333333333,0.16666666666666666,0.6,8.770244304144935,2182.0,1820.0
+scsemi5,Abd-PS23,ped_sepsis,1500,4309,150,0.7073333333333334,0.2529589231840334,0.21,0.3272220932931075,0.03666666666666667,0.32142028312833604,0.006666666666666667,0.013460199582269667,0.0026666666666666666,0.002088651659317707,0.03666666666666667,0.08284984915293572,0.9099048503132979,0.5989978288681517,0.5357954012138822,21,False,0.88,0.07333333333333333,0.84,0.18,3.6666666666666665,32.1420283128336,2183.0,1832.0
+scsemi5,Abd-PS27,ped_sepsis,1500,9427,150,0.6293333333333333,0.6126021003500584,0.06466666666666666,0.06078285774901877,0.11266666666666666,0.13196138750397793,0.072,0.010289593720165482,0.004,0.0029701920016972526,0.11733333333333333,0.1813938686750822,0.16671051235812023,0.5429659016547174,0.6829016053922496,2,False,0.8933333333333333,0.04,0.68,0.4066666666666667,11.266666666666666,13.196138750397793,2182.0,2356.0
+scsemi5,Abd-S102,adult_sepsis,1500,9512,150,0.7226666666666667,0.47981497056349875,0.186,0.39318755256518084,0.04933333333333333,0.0053616484440706475,0.004,0.095878889823381,0.002,0.0007359125315391085,0.036,0.02502102607232969,0.5981328847771236,0.4780589924443877,0.5166945529184158,38,False,0.9266666666666666,0.05333333333333334,0.9333333333333333,0.07333333333333333,4.933333333333334,0.5361648444070648,2181.0,1646.0
+scsemi5,Abd-S103,adult_sepsis,1500,9423,150,0.9046666666666666,0.4813753581661891,0.021333333333333333,0.14782977820227103,0.036,0.0024408362517245037,0.0,0.04393505253104107,0.0013333333333333333,0.00021224663058473945,0.03666666666666667,0.3242067282181895,0.9159431179030031,0.5664430341984366,0.4992036802171552,18,False,0.9866666666666667,0.2866666666666667,0.8733333333333333,0.17333333333333334,3.5999999999999996,0.24408362517245036,2183.0,971.0
+scsemi5,Abd-S120,adult_sepsis,1500,5783,150,0.756,0.3140238630468615,0.16533333333333333,0.11049628220646723,0.03333333333333333,0.46446481065191075,0.009333333333333334,0.07297250561992046,0.002,0.0008646031471554556,0.034,0.03717793532768459,0.9958971698656984,0.5118693118899336,0.5445181799294042,34,False,0.78,0.04666666666666667,0.8466666666666667,0.14666666666666667,3.3333333333333335,46.44648106519107,2183.0,2845.0
+scsemi5,Abd-S123,adult_sepsis,1500,6299,150,0.6986666666666667,0.16986823305286552,0.17533333333333334,0.08779171297031275,0.05333333333333334,0.5027782187648833,0.026,0.10938244165740593,0.0013333333333333333,0.0007937767899666614,0.04533333333333334,0.1293856167645658,1.233759221040377,0.8072659461461709,0.6918865976295101,5,False,0.7333333333333333,0.18,0.5266666666666666,0.29333333333333333,5.333333333333334,50.277821876488325,2181.0,2261.0
+scsemi5,Abd-S156,adult_sepsis,1500,7004,150,0.7286666666666667,0.3343803540833809,0.142,0.08209594517418618,0.050666666666666665,0.39406053683609366,0.02266666666666667,0.0910908052541405,0.012,0.0012849800114220445,0.044,0.0970873786407767,0.9298107747953551,0.4844585291172235,0.700822057079064,2,False,0.5466666666666666,0.11333333333333333,0.5733333333333334,0.24,5.066666666666666,39.406053683609365,2184.0,1821.0
+scsemi5,Abd-S39,adult_sepsis,1500,4291,150,0.692,0.13819622465625728,0.184,0.06152412025168958,0.048666666666666664,0.6933115823817292,0.020666666666666667,0.049638778839431366,0.002,0.0013982754602656724,0.05266666666666667,0.05593101841062689,1.3537627592635748,0.9886753042539405,0.7154087151514175,29,False,0.7266666666666667,0.06666666666666667,0.49333333333333335,0.15333333333333332,4.866666666666666,69.33115823817292,2185.0,3523.0
+scsemi5,Abd-S42,adult_sepsis,1500,6567,150,0.3993333333333333,0.5748439165524593,0.04466666666666667,0.09760925841327851,0.44333333333333336,0.17039744175422566,0.018666666666666668,0.05862646566164154,0.030666666666666665,0.0016750418760469012,0.06333333333333334,0.0968478757423481,0.603855032739455,0.29513901722746333,0.6108514866382997,4,False,0.8866666666666667,0.10666666666666667,0.52,0.24,44.333333333333336,17.039744175422566,2185.5,1982.0
+scsemi5,Abd-S47,adult_sepsis,1500,7971,150,0.5773333333333334,0.7027976414502571,0.2846666666666667,0.20474219044034625,0.07333333333333333,0.03224187680341237,0.04933333333333333,0.019069125580228326,0.013333333333333334,0.0006272738677706687,0.002,0.040521891857985194,0.3279723999498181,0.5195757542533206,0.6483876588894782,6,False,0.7333333333333333,0.08,0.5666666666666667,0.26666666666666666,7.333333333333333,3.224187680341237,2182.0,2030.0
+scsemi5,Abd-S51,adult_sepsis,1500,6865,150,0.318,0.25273124544792425,0.042666666666666665,0.269482884195193,0.462,0.3551347414420976,0.007333333333333333,0.03670793882010197,0.03666666666666667,0.0014566642388929353,0.13333333333333333,0.08448652585579024,0.51238164603059,0.5536325208613101,0.5769172831314305,11,False,0.9266666666666666,0.16,0.6266666666666667,0.21333333333333335,46.2,35.51347414420976,2182.0,1849.0
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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/draw2_scsemi5/summary.json

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