Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/ye2026_sepsis/xcalL_K1000/README.md

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+# score_ft.py output: ye2026_sepsis, 82 scored donors
+
+Arms: xcalL_K1000.  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 82 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) (12300 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 0.9805, 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
+   0.9796, 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 = 61) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 21); 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 61 + 21
+   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/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/ye2026_sepsis/xcalL_K1000/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
+xcalL_K1000,T cell,mean_cp10k,3258,0.05085794894649846,0.2417280392439751,-0.0261168783284682,0.3100646508849977,61,21,61,21,298117,57613,,,,,,,,,,,
+xcalL_K1000,T cell,pseudobulk_log1p_cpm,3258,0.14032467657623549,0.21842986671424938,0.05425037777576061,0.42278318586799984,61,21,61,21,298117,57613,,,,,,,,,,,
+xcalL_K1000,B cell,mean_cp10k,3310,0.14744352944624264,0.27652248568893345,0.0832041710220926,0.2534886846618699,61,21,61,21,99729,17472,,,,,,,,,,,
+xcalL_K1000,B cell,pseudobulk_log1p_cpm,3310,0.22777933892768737,0.2766567545456621,0.16163286016844935,0.28521568371419437,61,21,61,21,99729,17472,,,,,,,,,,,
+xcalL_K1000,Monocyte,mean_cp10k,3289,0.21575861947763836,0.5887447215431866,0.09051526926538025,0.24601856099946168,61,21,60,21,111269,32496,,,,,,,,,,,
+xcalL_K1000,Monocyte,pseudobulk_log1p_cpm,3289,0.13910745284724751,0.6208124073714443,-0.0826511378364418,0.3228554659686702,61,21,60,21,111269,32496,,,,,,,,,,,
+xcalL_K1000,NK cell,mean_cp10k,3240,-0.027309308056751334,0.16207395037992536,-0.08172372983887331,0.3053134694234651,61,21,61,21,47832,11794,,,,,,,,,,,
+xcalL_K1000,NK cell,pseudobulk_log1p_cpm,3240,-0.03669939141435601,0.05311099002810362,-0.0549475478455553,0.29587040005067533,61,21,61,21,47832,11794,,,,,,,,,,,
+xcalL_K1000,T cell,welch_top50_deg,5975,,,,,61,21,61,21,298117,57613,True,5,0.05263157894736842,50,50,1578,162,4,0.041666666666666664,50,1714
+xcalL_K1000,B cell,welch_top50_deg,5975,,,,,61,21,61,21,99729,17472,True,1,0.010101010101010102,50,50,1102,331,3,0.030927835051546393,50,1714
+xcalL_K1000,Monocyte,welch_top50_deg,5975,,,,,61,21,60,21,111269,32496,True,7,0.07526881720430108,50,50,2459,501,14,0.16279069767441862,50,1714
+xcalL_K1000,NK cell,welch_top50_deg,5975,,,,,61,21,61,21,47832,11794,True,0,0.0,50,47,1235,47,4,0.041666666666666664,50,1714
+xcalL_K1000,DC,welch_top50_deg,5975,,,,,57,21,59,21,3300,834,True,0,0.0,50,2,1572,2,2,0.02040816326530612,50,1714

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/ye2026_sepsis/xcalL_K1000/per_donor.csv

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+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
+xcalL_K1000,Abd-PS04,ped_sepsis,1500,5449,150,0.13466666666666666,0.14094329234721967,0.020666666666666667,0.17030647825289044,0.798,0.5608368507983116,0.02266666666666667,0.02147182969352175,0.012,0.003853918150119288,0.012,0.10258763075793724,0.4930081360494281,0.8240358281801887,0.8687285651471347,5,False,0.9133333333333333,0.44,0.94,0.6133333333333333,79.80000000000001,56.083685079831156,2812.0,2716.0
+xcalL_K1000,Abd-PS05,ped_sepsis,1500,6924,150,0.41733333333333333,0.30387059503177355,0.26066666666666666,0.5060658578856152,0.21933333333333332,0.07019064124783363,0.03866666666666667,0.0891103408434431,0.0033333333333333335,0.008954361640670132,0.06066666666666667,0.021808203350664357,0.6029277874061235,0.6987393915367811,0.8488594569575455,63,False,0.84,0.47333333333333333,0.78,0.49333333333333335,21.933333333333334,7.0190641247833625,1154.0,2313.0
+xcalL_K1000,Abd-PS07,ped_sepsis,1500,3472,150,0.7646666666666667,0.8626152073732719,0.024666666666666667,0.023329493087557603,0.08266666666666667,0.002304147465437788,0.11733333333333333,0.09331797235023041,0.005333333333333333,0.0002880184331797235,0.005333333333333333,0.018145161290322582,0.2215207373271889,0.7909717817075781,0.8235078743128642,61,False,0.8333333333333334,0.5066666666666667,0.8866666666666667,0.62,8.266666666666666,0.2304147465437788,1306.0,1614.0
+xcalL_K1000,Abd-PS19,ped_sepsis,1500,7984,150,0.45866666666666667,0.4622995991983968,0.10333333333333333,0.11209919839679358,0.28,0.2937124248496994,0.12666666666666668,0.05586172344689379,0.0033333333333333335,0.0156563126252505,0.028,0.06037074148296593,0.14160988643954575,0.218193607618059,0.8659890643920749,25,False,0.9,0.38,0.8733333333333333,0.5533333333333333,28.000000000000004,29.37124248496994,1160.0,2111.0
+xcalL_K1000,Abd-PS20,ped_sepsis,1500,7286,150,0.108,0.30496843261048584,0.8806666666666667,0.5780949766675817,0.0,0.08770244304144935,0.01,0.007823222618720834,0.0006666666666666666,0.0005489980785067252,0.0006666666666666666,0.02086192698325556,0.6097322719370483,0.8206851316278935,0.8406862836710743,4,False,0.9466666666666667,0.48,0.98,0.66,0.0,8.770244304144935,2390.5,1820.0
+xcalL_K1000,Abd-PS23,ped_sepsis,1500,4309,150,0.48333333333333334,0.2529589231840334,0.20333333333333334,0.3272220932931075,0.20333333333333334,0.32142028312833604,0.03333333333333333,0.013460199582269667,0.004666666666666667,0.002088651659317707,0.072,0.08284984915293572,0.505651117815425,0.6120732295054894,0.8570448223461099,43,False,0.8133333333333334,0.4666666666666667,0.7666666666666667,0.5533333333333333,20.333333333333332,32.1420283128336,1009.5,1832.0
+xcalL_K1000,Abd-PS27,ped_sepsis,1500,9427,150,0.8286666666666667,0.6126021003500584,0.052,0.06078285774901877,0.05466666666666667,0.13196138750397793,0.05533333333333333,0.010289593720165482,0.0006666666666666666,0.0029701920016972526,0.008666666666666666,0.1813938686750822,0.5222166118595524,0.5288253850706728,0.8615406153272472,3,False,0.8733333333333333,0.15333333333333332,0.88,0.24,5.466666666666667,13.196138750397793,2815.5,2356.0
+xcalL_K1000,Abd-S102,adult_sepsis,1500,9512,150,0.256,0.47981497056349875,0.07866666666666666,0.39318755256518084,0.542,0.0053616484440706475,0.062,0.095878889823381,0.005333333333333333,0.0007359125315391085,0.056,0.02502102607232969,1.1444294925707879,0.47699080006140543,0.7957714091972375,74,False,0.9,0.29333333333333333,0.8933333333333333,0.44,54.2,0.5361648444070648,1598.5,1646.0
+xcalL_K1000,Abd-S103,adult_sepsis,1500,9423,150,0.49,0.4813753581661891,0.13066666666666665,0.14782977820227103,0.254,0.0024408362517245037,0.05466666666666667,0.04393505253104107,0.005333333333333333,0.00021224663058473945,0.06533333333333333,0.3242067282181895,0.5520730128409211,0.5587060076493682,0.8187158707459276,73,False,0.8,0.2866666666666667,0.7533333333333333,0.36,25.4,0.24408362517245036,973.0,971.0
+xcalL_K1000,Abd-S120,adult_sepsis,1500,5783,150,0.3586666666666667,0.3140238630468615,0.028,0.11049628220646723,0.47533333333333333,0.46446481065191075,0.10266666666666667,0.07297250561992046,0.016,0.0008646031471554556,0.019333333333333334,0.03717793532768459,0.20068176840163704,0.5189164550921991,0.8758938454677504,6,False,0.92,0.44666666666666666,0.9533333333333334,0.8133333333333334,47.53333333333333,46.44648106519107,1790.0,2845.0
+xcalL_K1000,Abd-S123,adult_sepsis,1500,6299,150,0.354,0.16986823305286552,0.029333333333333333,0.08779171297031275,0.5253333333333333,0.5027782187648833,0.064,0.10938244165740593,0.008666666666666666,0.0007937767899666614,0.018666666666666668,0.1293856167645658,0.429119542784569,0.8187693250726966,0.8728387416615117,6,False,0.9,0.54,0.9066666666666666,0.7066666666666667,52.53333333333333,50.277821876488325,1550.0,2261.0
+xcalL_K1000,Abd-S156,adult_sepsis,1500,7004,150,0.224,0.3343803540833809,0.016666666666666666,0.08209594517418618,0.638,0.39406053683609366,0.076,0.0910908052541405,0.008666666666666666,0.0012849800114220445,0.03666666666666667,0.0970873786407767,0.502642299638302,0.4962206922485184,0.8769073311474255,6,False,0.9466666666666667,0.6266666666666667,0.9533333333333334,0.7133333333333334,63.800000000000004,39.406053683609365,2315.5,1821.0
+xcalL_K1000,Abd-S39,adult_sepsis,1500,4291,150,0.30533333333333335,0.13819622465625728,0.048,0.06152412025168958,0.554,0.6933115823817292,0.023333333333333334,0.049638778839431366,0.009333333333333334,0.0013982754602656724,0.06,0.05593101841062689,0.35828229627903363,0.9977910325594677,0.8771412273054763,13,False,0.8866666666666667,0.36,0.8933333333333333,0.6733333333333333,55.400000000000006,69.33115823817292,1754.5,3523.0
+xcalL_K1000,Abd-S42,adult_sepsis,1500,6567,150,0.654,0.5748439165524593,0.028666666666666667,0.09760925841327851,0.14933333333333335,0.17039744175422566,0.14733333333333334,0.05862646566164154,0.009333333333333334,0.0016750418760469012,0.011333333333333334,0.0968478757423481,0.35104248515303793,0.2811971753944427,0.8716543684083823,13,False,0.9533333333333334,0.4533333333333333,0.9533333333333334,0.66,14.933333333333335,17.039744175422566,1821.0,1982.0
+xcalL_K1000,Abd-S47,adult_sepsis,1500,7971,150,0.6406666666666667,0.7027976414502571,0.3373333333333333,0.20474219044034625,0.0026666666666666666,0.03224187680341237,0.013333333333333334,0.019069125580228326,0.0006666666666666666,0.0006272738677706687,0.005333333333333333,0.040521891857985194,0.26526107138376603,0.5066504444101315,0.8544927686627204,4,False,0.88,0.36666666666666664,0.92,0.5666666666666667,0.26666666666666666,3.224187680341237,2345.5,2030.0
+xcalL_K1000,Abd-S51,adult_sepsis,1500,6865,150,0.304,0.25273124544792425,0.174,0.269482884195193,0.38666666666666666,0.3551347414420976,0.09533333333333334,0.03670793882010197,0.017333333333333333,0.0014566642388929353,0.02266666666666667,0.08448652585579024,0.31460548676863315,0.566744289390268,0.8658000859403784,27,False,0.9066666666666666,0.42,0.9,0.5666666666666667,38.666666666666664,35.51347414420976,1924.0,1849.0
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paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/ye2026_sepsis/xcalL_K1000/summary.json

@@ -0,0 +1,767 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-02T15:49:54.225064+00:00",
+ "command": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
+  "--dataset",
+  "ye2026_sepsis",
+  "--threads",
+  "8",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/data/ye2026_sepsis/usable_samples.txt",
+  "--arm",
+  "xcalL_K1000=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/generated/ye2026_sepsis/hoL_ye2026_sepsis/xcal_K1000.npz",
+  "--case-groups",
+  "adult_sepsis,ped_sepsis",
+  "--control-groups",
+  "adult_HC,adult_ICU_ctrl,ped_HC,ped_ICU_ctrl",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_crosscohort_20261002/scores/ye2026_sepsis/xcalL_K1000"
+ ],
+ "arms": [
+  "xcalL_K1000"
+ ],
+ "donors": [
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+  "Abd-PS05",
+  "Abd-PS07",
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+  "Abd-PS23",
+  "Abd-PS27",
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+ "n_donors": 82,
+ "reference": {
+  "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/ye2026_sepsis",
+  "reference_sha256": "bac12da8a241652cb75100a1b082b2be56806c9bf2fd20ce6f927f19683ea5b8",
+  "features_sha256": "0cfda4b0cf1caf5e5319d071697c228d894e992fd090e2b3ed450bd6c9887d46",
+  "n_r1_cells": 12300,
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+   "explained_variance_ratio_sum": 0.33549821376800537,
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