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/ji2025_chili/fewshot_n5/draw3_scsemi5/README.md

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+# score_ft.py output: ji2025_chili, 25 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/ji2025_chili/reference.npz` (sha 19c30f56db69), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili;
+  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/ji2025_chili/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 (1004 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5752 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 25 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) (3750 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 0.9965, 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
+   0.9963, 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 ['TOL', 'FU1'] (n = 15) vs control groups ['BC', 'AC'] (n = 10); 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 5752 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/ji2025_chili/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 15 + 10
+   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/ji2025_chili/fewshot_n5/draw3_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,3028,0.3048131770821959,0.2368920077117674,0.2423841800975824,0.3588769520440311,15,10,15,10,44374,19431,,,,,,,,,,,
+scsemi5,T cell,pseudobulk_log1p_cpm,3028,0.20288792612032724,0.30559676102300276,0.1521744560518492,0.1992808233144794,15,10,15,10,44374,19431,,,,,,,,,,,
+scsemi5,B cell,mean_cp10k,3238,0.24932158494709133,0.16221245678147309,0.22196836280973212,0.21975622233164435,15,10,12,9,6285,853,,,,,,,,,,,
+scsemi5,B cell,pseudobulk_log1p_cpm,3238,0.058149954638856916,0.047548729682353,0.05322262475864434,0.11191718357019693,15,10,12,9,6285,853,,,,,,,,,,,
+scsemi5,Monocyte,mean_cp10k,3132,0.2925121006231351,0.4012426962403039,0.19369880533108683,0.30838361083952903,15,10,15,9,26936,10368,,,,,,,,,,,
+scsemi5,Monocyte,pseudobulk_log1p_cpm,3132,0.1863304088293764,0.36057989165867327,0.1226738156251125,0.20624880623238476,15,10,15,9,26936,10368,,,,,,,,,,,
+scsemi5,NK cell,mean_cp10k,3064,0.16588946182979245,0.23076485505524633,0.09961399781210623,0.32109065345682525,15,10,14,9,13603,4012,,,,,,,,,,,
+scsemi5,NK cell,pseudobulk_log1p_cpm,3064,0.0854239053990332,0.11924462462412533,0.06570922435414807,0.1779984786608438,15,10,14,9,13603,4012,,,,,,,,,,,
+scsemi5,T cell,welch_top50_deg,5752,,,,,15,10,15,10,44374,19431,True,0,0.0,50,0,813,0,0,0.0,1,1
+scsemi5,B cell,welch_top50_deg,5752,,,,,15,10,12,9,6285,853,True,0,0.0,8,0,8,0,0,0.0,1,1
+scsemi5,Monocyte,welch_top50_deg,5752,,,,,15,10,15,9,26936,10368,True,0,,0,0,0,0,0,0.0,1,1
+scsemi5,NK cell,welch_top50_deg,5752,,,,,15,10,14,9,13603,4012,True,0,0.0,14,0,14,0,0,0.0,1,1
+scsemi5,DC,welch_top50_deg,5752,,,,,13,10,13,9,1144,1533,True,0,0.0,3,0,3,0,0,0.0,1,1

paper-releases/pbisc-diffusion/updates/20260930T065633Z-summary-deck-scsemi-final/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw3_scsemi5/per_donor.csv

@@ -0,0 +1,26 @@
+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
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+scsemi5,N032_AC,AC,1500,7350,150,0.5786666666666667,0.5891156462585034,0.029333333333333333,0.10979591836734694,0.10533333333333333,0.17551020408163265,0.17333333333333334,0.04707482993197279,0.10466666666666667,0.025714285714285714,0.008666666666666666,0.0527891156462585,0.410421768707483,0.4228316237077168,0.5502396149864026,11,False,0.9666666666666667,0.64,0.4666666666666667,0.2733333333333333,10.533333333333333,17.551020408163264,1065.5,437.0
+scsemi5,N055_AC,AC,1500,4308,150,0.278,0.340297121634169,0.06666666666666667,0.07799442896935933,0.47733333333333333,0.32474466109563604,0.15533333333333332,0.2051996285979573,0.0006666666666666666,0.0060352831940575676,0.022,0.0457288765088208,0.3051773444753946,0.2686738546931644,0.6672081302025609,3,False,0.7133333333333334,0.1,0.36666666666666664,0.23333333333333334,47.733333333333334,32.47446610956361,1065.0,1139.5
+scsemi5,N136_TOL,TOL,1500,759,150,0.428,0.48616600790513836,0.0013333333333333333,0.10013175230566534,0.48533333333333334,0.19104084321475626,0.020666666666666667,0.0922266139657444,0.006666666666666667,0.0013175230566534915,0.058,0.12911725955204217,0.5992832674571805,0.33033710334377353,0.466330779623278,10,False,0.4666666666666667,0.02666666666666667,0.4866666666666667,0.09333333333333334,48.53333333333333,19.104084321475625,1068.0,1837.0
+scsemi5,N136_FU1,FU1,1500,2927,150,0.37866666666666665,0.4246668944311582,0.0,0.14724974376494704,0.5013333333333333,0.19337205329689103,0.0,0.09531943969935087,0.0,0.0023915271609156134,0.12,0.13700034164673727,0.6159225600728846,0.3975871977038701,0.4434970372701727,2,False,0.48,0.08666666666666667,0.4066666666666667,0.18,50.13333333333333,19.3372053296891,1064.0,1501.0
+scsemi5,N138_TOL,TOL,1500,5129,150,0.7286666666666667,0.13803860401637746,0.005333333333333333,0.02807564827451745,0.072,0.5878338857477091,0.07466666666666667,0.17703255995320724,0.10066666666666667,0.006044063170208618,0.018666666666666668,0.06297523883798012,1.3705013322934947,0.7854106139886504,0.5912555843601679,2,False,0.8866666666666667,0.26666666666666666,0.64,0.09333333333333334,7.199999999999999,58.78338857477091,1065.0,1117.0
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+scsemi5,N128_BC,BC,1500,3902,150,0.8053333333333333,0.5158892875448488,0.004,0.06817016914402871,0.042,0.29420809841107126,0.012666666666666666,0.07842132239876987,0.11933333333333333,0.03331624807790876,0.016666666666666666,0.00999487442337263,0.7642658465744064,0.22738478927422778,0.5349946864012876,6,False,0.9533333333333334,0.18666666666666668,0.76,0.04,4.2,29.420809841107125,1067.0,1088.0
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+scsemi5,N129_BC,BC,1500,3087,150,0.47,0.5963718820861678,0.0006666666666666666,0.11305474570780694,0.2866666666666667,0.08357628765792031,0.11066666666666666,0.18140589569160998,0.04933333333333333,0.013281503077421444,0.08266666666666667,0.012309685779073534,0.6189983803045027,0.45027978930978146,0.48882338049458346,3,False,0.86,0.17333333333333334,0.6666666666666666,0.11333333333333333,28.666666666666668,8.35762876579203,1065.0,1059.0
+scsemi5,N129_AC,AC,1500,2798,150,0.7213333333333334,0.7076483202287348,0.002,0.04932094353109364,0.096,0.07969978556111508,0.07066666666666667,0.13366690493209435,0.10066666666666667,0.0228734810578985,0.009333333333333334,0.006790564689063617,0.2206423635930427,0.5443287164364481,0.5452303281885675,5,False,0.9133333333333333,0.32666666666666666,0.6666666666666666,0.11333333333333333,9.6,7.969978556111508,1066.0,759.0
+scsemi5,N158_AC,AC,1500,3749,150,0.4766666666666667,0.24619898639637236,0.0013333333333333333,0.026673779674579887,0.388,0.6295012003200854,0.05,0.04934649239797279,0.016,0.024806615097359295,0.068,0.0234729261136303,0.5512965235173825,0.781764384762707,0.5057034789348555,8,False,1.0,0.10666666666666667,0.6866666666666666,0.10666666666666667,38.800000000000004,62.95012003200854,1065.0,1047.0
+scsemi5,N159_BC,BC,1500,3779,150,0.9806666666666667,0.6996559936491136,0.0,0.018523418893887273,0.0,0.08123842286319131,0.0,0.173590897062715,0.0,0.014024874305371792,0.019333333333333334,0.01296639322572109,0.5747552262503308,0.529660106865646,0.38496113554787587,1,True,0.24666666666666667,0.12666666666666668,1.0,0.38666666666666666,0.0,8.123842286319132,1066.0,1045.0
+scsemi5,N002_TOL,TOL,1500,6318,150,0.358,0.41579613801836024,0.028,0.05777144666033555,0.20933333333333334,0.3779677113010446,0.26066666666666666,0.1179170623615068,0.12933333333333333,0.020101297879075657,0.014666666666666666,0.010446343779677113,0.5124039252928142,0.2657153405968152,0.5119303542697734,16,False,0.9933333333333333,0.1,0.6066666666666667,0.03333333333333333,20.933333333333334,37.79677113010446,1065.0,933.5
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+scsemi5,N005_BC,BC,1500,6359,150,0.306,0.7200817738638151,0.07133333333333333,0.05016512030193427,0.484,0.11448340934109137,0.128,0.09891492373014625,0.0033333333333333335,0.007705614090265765,0.007333333333333333,0.008649158672747287,0.839539759920323,0.5644381516165136,0.6163739295269746,5,False,1.0,0.4066666666666667,0.48,0.37333333333333335,48.4,11.448340934109137,1064.0,782.0
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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/ji2025_chili/fewshot_n5/draw3_scsemi5/summary.json

@@ -0,0 +1,537 @@
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