Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/cal_budget_20261001/fold4/README.md

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+# score_ft.py output: ye2026_sepsis, 16 scored donors
+
+Arms: zs__cal_n5, zs__cal_n10, zs__cal_n20, zs__cal_n40.  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 16 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) (2400 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 0.9975, 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
+   0.9958, 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 ['adult_sepsis', 'ped_sepsis'] (n = 12) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 4); 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 12 + 4
+   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/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/cal_budget_20261001/fold4/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__cal_n5,T cell,mean_cp10k,3262,0.285891661892175,0.3845952081966029,0.11271750111696696,0.5107706249917372,12,4,12,4,58503,11145,,,,,,,,,,,
+zs__cal_n5,T cell,pseudobulk_log1p_cpm,3262,0.19340217400382673,0.3531374096379015,0.07292711683702163,0.36802134320143376,12,4,12,4,58503,11145,,,,,,,,,,,
+zs__cal_n5,B cell,mean_cp10k,3296,0.23546405219568656,0.4051732466331055,0.13187427341914087,0.29700831306981307,12,4,12,4,21119,4661,,,,,,,,,,,
+zs__cal_n5,B cell,pseudobulk_log1p_cpm,3296,0.015712655630918084,0.24630008833384856,-0.012211038261326973,0.11154546760451928,12,4,12,4,21119,4661,,,,,,,,,,,
+zs__cal_n5,Monocyte,mean_cp10k,3254,0.3178537618363284,0.5886767736991653,0.14924328644317233,0.34780061641887455,12,4,12,4,18965,6022,,,,,,,,,,,
+zs__cal_n5,Monocyte,pseudobulk_log1p_cpm,3254,0.11022448173729725,0.5568467446861869,-0.04704913777603082,0.2656037060039932,12,4,12,4,18965,6022,,,,,,,,,,,
+zs__cal_n5,NK cell,mean_cp10k,3218,0.11605078833641864,0.30308305665277596,0.028066452846476374,0.2986816919245631,12,4,12,4,9793,1233,,,,,,,,,,,
+zs__cal_n5,NK cell,pseudobulk_log1p_cpm,3218,-0.013848158907398293,0.07283854583322694,-0.023738217971487317,0.1320677947623601,12,4,12,4,9793,1233,,,,,,,,,,,
+zs__cal_n5,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,11145,True,0,0.0,35,0,35,0,0,0.0,0,0
+zs__cal_n5,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4661,True,0,0.0,3,0,3,0,0,0.0,0,0
+zs__cal_n5,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,6022,True,0,0.0,1,6,1,6,0,0.0,0,0
+zs__cal_n5,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1233,True,0,,0,0,0,0,0,,0,0
+zs__cal_n5,DC,welch_top50_deg,5975,,,,,11,4,11,4,630,356,True,0,0.0,0,6,0,6,0,,0,0
+zs__cal_n10,T cell,mean_cp10k,3262,0.30251890447538154,0.38658937834714585,0.1375301611657477,0.4980218753314251,12,4,12,4,58503,11980,,,,,,,,,,,
+zs__cal_n10,T cell,pseudobulk_log1p_cpm,3262,0.19681053100304513,0.3532283853754512,0.0753333026139003,0.37197052070889147,12,4,12,4,58503,11980,,,,,,,,,,,
+zs__cal_n10,B cell,mean_cp10k,3296,0.22580379779438983,0.4065559347974283,0.12372048477165552,0.28926359583419603,12,4,12,4,21119,4731,,,,,,,,,,,
+zs__cal_n10,B cell,pseudobulk_log1p_cpm,3296,0.037894104751331606,0.24608927094478453,0.004391188510579021,0.13685278515962482,12,4,12,4,21119,4731,,,,,,,,,,,
+zs__cal_n10,Monocyte,mean_cp10k,3254,0.311971139280368,0.5868787582998451,0.16075989961534043,0.3215678047664187,12,4,12,4,18965,5099,,,,,,,,,,,
+zs__cal_n10,Monocyte,pseudobulk_log1p_cpm,3254,0.08449028978306897,0.5552567767864671,-0.05935180413517334,0.23849778291324747,12,4,12,4,18965,5099,,,,,,,,,,,
+zs__cal_n10,NK cell,mean_cp10k,3218,0.17302819468774433,0.3043976096778886,0.08210081848184199,0.3255032921619397,12,4,12,4,9793,1329,,,,,,,,,,,
+zs__cal_n10,NK cell,pseudobulk_log1p_cpm,3218,0.04153715025578399,0.07186591413706665,0.03280982241184178,0.12626392701742012,12,4,12,4,9793,1329,,,,,,,,,,,
+zs__cal_n10,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,11980,True,0,0.0,35,0,35,0,0,0.0,0,0
+zs__cal_n10,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4731,True,0,0.0,3,0,3,0,0,0.0,0,0
+zs__cal_n10,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,5099,True,0,0.0,1,0,1,0,0,0.0,0,0
+zs__cal_n10,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1329,True,0,,0,0,0,0,0,,0,0
+zs__cal_n10,DC,welch_top50_deg,5975,,,,,11,4,12,4,630,314,True,0,,0,0,0,0,0,,0,0
+zs__cal_n20,T cell,mean_cp10k,3262,0.2973272549302694,0.3817284222495409,0.1272448613009018,0.5147546690772796,12,4,12,4,58503,11717,,,,,,,,,,,
+zs__cal_n20,T cell,pseudobulk_log1p_cpm,3262,0.21420002230439128,0.3539518841033177,0.10083641546596075,0.3561982915627783,12,4,12,4,58503,11717,,,,,,,,,,,
+zs__cal_n20,B cell,mean_cp10k,3296,0.2450234017855247,0.4009106482043577,0.1486848050611067,0.2855556679038178,12,4,12,4,21119,4717,,,,,,,,,,,
+zs__cal_n20,B cell,pseudobulk_log1p_cpm,3296,0.02832624863470423,0.2457583908279347,-0.0019996917182302726,0.12308785142014765,12,4,12,4,21119,4717,,,,,,,,,,,
+zs__cal_n20,Monocyte,mean_cp10k,3254,0.29101595588858337,0.5808785574660575,0.14695150270963958,0.3048716883487403,12,4,12,4,18965,5348,,,,,,,,,,,
+zs__cal_n20,Monocyte,pseudobulk_log1p_cpm,3254,0.09167841229116008,0.5563566930326533,-0.05645930944309236,0.24650587294728496,12,4,12,4,18965,5348,,,,,,,,,,,
+zs__cal_n20,NK cell,mean_cp10k,3218,0.15460208707524098,0.29886968512387413,0.05394850116415008,0.35633874589120146,12,4,12,4,9793,1325,,,,,,,,,,,
+zs__cal_n20,NK cell,pseudobulk_log1p_cpm,3218,0.008944091399918313,0.07150300642731264,-0.0038640579414623046,0.17812707011301093,12,4,12,4,9793,1325,,,,,,,,,,,
+zs__cal_n20,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,11717,True,0,0.0,35,0,35,0,0,0.0,0,0
+zs__cal_n20,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4717,True,0,0.0,3,0,3,0,0,0.0,0,0
+zs__cal_n20,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,5348,True,0,0.0,1,29,1,29,0,0.0,0,0
+zs__cal_n20,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1325,True,0,,0,0,0,0,0,,0,0
+zs__cal_n20,DC,welch_top50_deg,5975,,,,,11,4,11,4,630,308,True,0,,0,0,0,0,0,,0,0
+zs__cal_n40,T cell,mean_cp10k,3262,0.30959656974924726,0.385602733286599,0.1498234481249038,0.4904777591021505,12,4,12,4,58503,12323,,,,,,,,,,,
+zs__cal_n40,T cell,pseudobulk_log1p_cpm,3262,0.1970752282543297,0.3538048838494179,0.07063555033514864,0.3846512359442403,12,4,12,4,58503,12323,,,,,,,,,,,
+zs__cal_n40,B cell,mean_cp10k,3296,0.19668937262685873,0.4065468156826579,0.09526812032597111,0.27815881919624813,12,4,12,4,21119,4775,,,,,,,,,,,
+zs__cal_n40,B cell,pseudobulk_log1p_cpm,3296,0.011595432676269167,0.24657048781603008,-0.019898100150583714,0.12462496569464922,12,4,12,4,21119,4775,,,,,,,,,,,
+zs__cal_n40,Monocyte,mean_cp10k,3254,0.2893995365426704,0.5823379767902359,0.15325628109616174,0.2923627815598567,12,4,12,4,18965,4640,,,,,,,,,,,
+zs__cal_n40,Monocyte,pseudobulk_log1p_cpm,3254,0.08042941547890026,0.5553247778175561,-0.057307436378647554,0.22838636781149907,12,4,12,4,18965,4640,,,,,,,,,,,
+zs__cal_n40,NK cell,mean_cp10k,3218,0.17700511815002326,0.30353627939689865,0.09094464906395827,0.3119036960684911,12,4,12,4,9793,1419,,,,,,,,,,,
+zs__cal_n40,NK cell,pseudobulk_log1p_cpm,3218,-0.010215758934740802,0.07150314650708933,-0.021927564976901518,0.15911254232752645,12,4,12,4,9793,1419,,,,,,,,,,,
+zs__cal_n40,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,12323,True,0,0.0,35,0,35,0,0,0.0,0,0
+zs__cal_n40,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4775,True,0,0.0,3,0,3,0,0,0.0,0,0
+zs__cal_n40,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,4640,True,0,0.0,1,0,1,0,0,0.0,0,0
+zs__cal_n40,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1419,True,0,0.0,0,1,0,1,0,,0,0
+zs__cal_n40,DC,welch_top50_deg,5975,,,,,11,4,11,4,630,285,True,0,0.0,0,12,0,12,0,,0,0

paper-releases/pbisc-diffusion/updates/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/cal_budget_20261001/fold4/per_donor.csv

@@ -0,0 +1,65 @@
+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__cal_n5,Abd-PS20,ped_sepsis,1500,7286,150,0.088,0.30496843261048584,0.9006666666666666,0.5780949766675817,0.004,0.08770244304144935,0.006666666666666667,0.007823222618720834,0.0,0.0005489980785067252,0.0006666666666666666,0.02086192698325556,0.64514337999817,0.8183075443274621,0.835218968373668,2,False,0.9866666666666667,0.44,1.0,0.54,0.4,8.770244304144935,2473.0,1820.0
+zs__cal_n5,Abd-PS27,ped_sepsis,1500,9427,150,0.8393333333333334,0.6126021003500584,0.054,0.06078285774901877,0.06733333333333333,0.13196138750397793,0.019333333333333334,0.010289593720165482,0.006,0.0029701920016972526,0.014,0.1813938686750822,0.47760956118949116,0.5444559358148133,0.8576390132175086,1,True,0.9933333333333333,0.24,0.9933333333333333,0.2733333333333333,6.7333333333333325,13.196138750397793,3505.0,2356.0
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+zs__cal_n5,Abd-S54,adult_sepsis,1500,7818,150,0.48133333333333334,0.4854182655410591,0.17333333333333334,0.1364799181376311,0.19133333333333333,0.012918905090816066,0.11666666666666667,0.340752110514198,0.019333333333333334,0.0011511895625479663,0.018,0.023279611153747763,0.46689997441800973,0.5525001038535718,0.8507515798157848,7,False,0.9866666666666667,0.8066666666666666,0.98,0.9066666666666666,19.133333333333333,1.2918905090816066,1717.0,1933.5
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paper-releases/pbisc-diffusion/updates/20261001T145641Z-lora-rank-sweep-calibration-budget/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/cal_budget_20261001/fold4/summary.json

@@ -0,0 +1,1356 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-01T14:23:15.070825+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",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/cal_budget_20261001/fold4",
+  "--threads",
+  "8",
+  "--arm",
+  "zs__cal_n5=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold4/zs__cal_n5.npz",
+  "--arm",
+  "zs__cal_n10=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold4/zs__cal_n10.npz",
+  "--arm",
+  "zs__cal_n20=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold4/zs__cal_n20.npz",
+  "--arm",
+  "zs__cal_n40=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold4/zs__cal_n40.npz",
+  "--case-groups",
+  "adult_sepsis,ped_sepsis",
+  "--control-groups",
+  "adult_HC,adult_ICU_ctrl,ped_HC,ped_ICU_ctrl"
+ ],
+ "arms": [
+  "zs__cal_n5",
+  "zs__cal_n10",
+  "zs__cal_n20",
+  "zs__cal_n40"
+ ],
+ "donors": [
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+  "Abd-S42",
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+  "HC16",
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+ "contrast_definition": {
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+  "rule": "explicit --case-groups / --control-groups",
+  "group_source": "features groups",
+  "case_groups": [
+   "adult_sepsis",
+   "ped_sepsis"
+  ],
+  "control_groups": [
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+   "adult_ICU_ctrl",
+   "ped_HC",
+   "ped_ICU_ctrl"
+  ],
+  "n_case_donors": 12,
+  "n_control_donors": 4,
+  "n_donors_in_neither": 0,
+  "case": [
+   true,
+   true,
+   true,
+   true,
+   true,
+   false,
+   false,
+   false,
+   false,
+   true,
+   true,
+   true,
+   true,
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+  "ctrl": [
+   false,
+   false,
+   false,
+   false,
+   false,
+   true,
+   true,
+   true,
+   true,
+   false,
+   false,
+   false,
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+  ],
+  "groups": [
+   "ped_sepsis",
+   "ped_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_HC",
+   "adult_ICU_ctrl",
+   "ped_HC",
+   "ped_ICU_ctrl",
+   "ped_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis"
+  ]
+ },
+ "deg_definition": {
+  "enabled": true,
+  "score": "top-50 DEG overlap per cell type (benchmark total_top50_overlap, Welch backend)",
+  "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py",
+  "functions": [
+   "welch_bh_deg",
+   "top_deg_genes",
+   "jaccard"
+  ],
+  "reproduces": "utility_studies_final6k_20260925/sle top-50 DEG 81 (1,006 genes) / 84 (2,279 genes) = sum over the six gated types of top50_overlap_count",
+  "top_n": 50,
+  "fdr": 0.05,
+  "n_genes": 5975,
+  "types": [
+   "T cell",
+   "B cell",
+   "Monocyte",
+   "NK cell",
+   "DC"
+  ],
+  "aggregates_source": "/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",
+  "aggregates_sha256": "9886ea64da90b1db6a07984d5d61d7dab04ee0beee7d4094ee3672316c2691d0",
+  "aggregates_note": null,
+  "types_not_scored_observed": [],
+  "observed": {
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+    "n_case_donors": 12,
+    "n_control_donors": 4,
+    "n_cells": 58503,
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