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/fold2/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 0.9971, 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
+   0.9962, 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/fold2/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,3229,0.2088883221522293,0.2670266590255714,0.10837529272797652,0.42896633671652673,12,4,12,4,62381,12555,,,,,,,,,,,
+zs__cal_n5,T cell,pseudobulk_log1p_cpm,3229,0.15663409836338046,0.33597948811036094,0.03569592193353656,0.37336975908091175,12,4,12,4,62381,12555,,,,,,,,,,,
+zs__cal_n5,B cell,mean_cp10k,3383,0.2702313884130483,0.24028472730217654,0.20026593319276012,0.37445743489242284,12,4,12,4,16254,3288,,,,,,,,,,,
+zs__cal_n5,B cell,pseudobulk_log1p_cpm,3383,0.026687859211635506,0.18722660823477769,-0.003221788573042468,0.15923109190358772,12,4,12,4,16254,3288,,,,,,,,,,,
+zs__cal_n5,Monocyte,mean_cp10k,3329,0.22740942943286058,0.5343727567098001,0.03663596903926544,0.37176748035838725,12,4,12,4,19357,5834,,,,,,,,,,,
+zs__cal_n5,Monocyte,pseudobulk_log1p_cpm,3329,0.06340991105050406,0.5344693182671868,-0.09219603547295842,0.2594436368694753,12,4,12,4,19357,5834,,,,,,,,,,,
+zs__cal_n5,NK cell,mean_cp10k,3243,0.16714455391913854,0.240851788504321,0.0973620895512351,0.32260968231175235,12,4,12,4,8525,1515,,,,,,,,,,,
+zs__cal_n5,NK cell,pseudobulk_log1p_cpm,3243,-0.01397799004306591,0.18282958777065642,-0.04001789568718974,0.13671725200159235,12,4,12,4,8525,1515,,,,,,,,,,,
+zs__cal_n5,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,12555,True,0,,0,0,0,0,0,,0,0
+zs__cal_n5,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3288,True,0,,0,0,0,0,0,,0,0
+zs__cal_n5,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,5834,True,0,0.0,1,0,1,0,0,0.0,0,0
+zs__cal_n5,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,1515,True,0,0.0,5,1,5,1,0,0.0,0,0
+zs__cal_n5,DC,welch_top50_deg,5975,,,,,11,4,12,4,697,312,True,0,0.0,0,2,0,2,0,,0,0
+zs__cal_n10,T cell,mean_cp10k,3229,0.20551950755366613,0.26668514101174456,0.09991425449720111,0.44778461403644554,12,4,12,4,62381,12472,,,,,,,,,,,
+zs__cal_n10,T cell,pseudobulk_log1p_cpm,3229,0.14190537133082673,0.33735760912090085,0.01657164716131419,0.377823195085391,12,4,12,4,62381,12472,,,,,,,,,,,
+zs__cal_n10,B cell,mean_cp10k,3383,0.28121679263068583,0.23664830284089314,0.21297149513858152,0.37927684988172694,12,4,12,4,16254,3217,,,,,,,,,,,
+zs__cal_n10,B cell,pseudobulk_log1p_cpm,3383,0.046185142224715856,0.188961000667589,0.01643775710890464,0.16009504050511722,12,4,12,4,16254,3217,,,,,,,,,,,
+zs__cal_n10,Monocyte,mean_cp10k,3329,0.23838297142060794,0.5357377047331497,0.042794826403594596,0.382645750419606,12,4,12,4,19357,5975,,,,,,,,,,,
+zs__cal_n10,Monocyte,pseudobulk_log1p_cpm,3329,0.08346391502504587,0.5350772320971197,-0.08186881908479346,0.28006988627927826,12,4,12,4,19357,5975,,,,,,,,,,,
+zs__cal_n10,NK cell,mean_cp10k,3243,0.17757893519243592,0.23384873349403695,0.11343335740413535,0.3111668300465316,12,4,12,4,8525,1468,,,,,,,,,,,
+zs__cal_n10,NK cell,pseudobulk_log1p_cpm,3243,-0.03325142396093698,0.18310128969450787,-0.05172519808905861,0.094865412231117,12,4,12,4,8525,1468,,,,,,,,,,,
+zs__cal_n10,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,12472,True,0,,0,0,0,0,0,,0,0
+zs__cal_n10,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3217,True,0,,0,0,0,0,0,,0,0
+zs__cal_n10,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,5975,True,0,0.0,1,0,1,0,0,0.0,0,0
+zs__cal_n10,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,1468,True,0,0.0,5,0,5,0,0,0.0,0,0
+zs__cal_n10,DC,welch_top50_deg,5975,,,,,11,4,12,4,697,316,True,0,,0,0,0,0,0,,0,0
+zs__cal_n20,T cell,mean_cp10k,3229,0.21451643687577746,0.26491548207253857,0.1095865154487764,0.45443413197202465,12,4,12,4,62381,12580,,,,,,,,,,,
+zs__cal_n20,T cell,pseudobulk_log1p_cpm,3229,0.1465100801070094,0.3376185631628485,0.02854953331569887,0.35968218053881573,12,4,12,4,62381,12580,,,,,,,,,,,
+zs__cal_n20,B cell,mean_cp10k,3383,0.2571111667218497,0.23445105127832738,0.1865768959090733,0.38155767048686506,12,4,12,4,16254,3241,,,,,,,,,,,
+zs__cal_n20,B cell,pseudobulk_log1p_cpm,3383,0.027008122980687506,0.18988520555047808,-0.007027491296857379,0.17798957691420078,12,4,12,4,16254,3241,,,,,,,,,,,
+zs__cal_n20,Monocyte,mean_cp10k,3329,0.2561829272651841,0.5379330972864618,0.05206658799720698,0.4015084375269136,12,4,12,4,19357,5867,,,,,,,,,,,
+zs__cal_n20,Monocyte,pseudobulk_log1p_cpm,3329,0.11939656254220188,0.5340231295050889,-0.05102374326605398,0.3006238849104068,12,4,12,4,19357,5867,,,,,,,,,,,
+zs__cal_n20,NK cell,mean_cp10k,3243,0.15587650915545692,0.22636875941492973,0.09509950741937191,0.29798145855467245,12,4,12,4,8525,1455,,,,,,,,,,,
+zs__cal_n20,NK cell,pseudobulk_log1p_cpm,3243,-0.012022464349847247,0.18443129184401663,-0.036020978819198066,0.12525941497519152,12,4,12,4,8525,1455,,,,,,,,,,,
+zs__cal_n20,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,12580,True,0,,0,0,0,0,0,,0,0
+zs__cal_n20,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3241,True,0,,0,0,0,0,0,,0,0
+zs__cal_n20,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,5867,True,0,0.0,1,1,1,1,0,0.0,0,0
+zs__cal_n20,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,1455,True,0,0.0,5,3,5,3,0,0.0,0,0
+zs__cal_n20,DC,welch_top50_deg,5975,,,,,11,4,12,4,697,349,True,0,,0,0,0,0,0,,0,0
+zs__cal_n40,T cell,mean_cp10k,3229,0.2074825401051409,0.260687435792389,0.10331528057185882,0.4552360065072991,12,4,12,4,62381,12238,,,,,,,,,,,
+zs__cal_n40,T cell,pseudobulk_log1p_cpm,3229,0.14112451496923573,0.3383516126518913,0.0260507976957348,0.3492031479147515,12,4,12,4,62381,12238,,,,,,,,,,,
+zs__cal_n40,B cell,mean_cp10k,3383,0.25093618677991897,0.22994888879651115,0.1781460423793553,0.40037847093223433,12,4,12,4,16254,3414,,,,,,,,,,,
+zs__cal_n40,B cell,pseudobulk_log1p_cpm,3383,0.015132635418702505,0.19310455698338982,-0.016323331311169822,0.16023335285845314,12,4,12,4,16254,3414,,,,,,,,,,,
+zs__cal_n40,Monocyte,mean_cp10k,3329,0.2731762225002254,0.533754745626807,0.05700445908467454,0.43027530793701574,12,4,12,4,19357,6015,,,,,,,,,,,
+zs__cal_n40,Monocyte,pseudobulk_log1p_cpm,3329,0.1648997534099773,0.5347040714920677,-0.01556483388086191,0.33160109571896434,12,4,12,4,19357,6015,,,,,,,,,,,
+zs__cal_n40,NK cell,mean_cp10k,3243,0.13277095328326513,0.22310797023149737,0.06916755101610317,0.30753965671031414,12,4,12,4,8525,1471,,,,,,,,,,,
+zs__cal_n40,NK cell,pseudobulk_log1p_cpm,3243,-0.050895453981521066,0.18713788290380956,-0.0732165832006513,0.11003092610625244,12,4,12,4,8525,1471,,,,,,,,,,,
+zs__cal_n40,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,12238,True,0,,0,0,0,0,0,,0,0
+zs__cal_n40,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3414,True,0,0.0,0,3,0,3,0,,0,0
+zs__cal_n40,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,6015,True,0,0.0,1,0,1,0,0,0.0,0,0
+zs__cal_n40,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,1471,True,0,0.0,5,0,5,0,0,0.0,0,0
+zs__cal_n40,DC,welch_top50_deg,5975,,,,,11,4,12,4,697,340,True,0,,0,0,0,0,0,,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/fold2/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-PS04,ped_sepsis,1500,5449,150,0.15733333333333333,0.14094329234721967,0.03333333333333333,0.17030647825289044,0.7633333333333333,0.5608368507983116,0.017333333333333333,0.02147182969352175,0.014,0.003853918150119288,0.014666666666666666,0.10258763075793724,0.4580652107420322,0.9482179868266278,0.8660671692325168,1,True,1.0,0.58,0.9933333333333333,0.8,76.33333333333333,56.083685079831156,2367.0,2716.0
+zs__cal_n5,Abd-PS05,ped_sepsis,1500,6924,150,0.3953333333333333,0.30387059503177355,0.30533333333333335,0.5060658578856152,0.18333333333333332,0.07019064124783363,0.016,0.0891103408434431,0.006,0.008954361640670132,0.094,0.021808203350664357,0.5535944540727902,0.7766893607384627,0.850988225155097,9,False,0.9933333333333333,0.6,0.94,0.7133333333333334,18.333333333333332,7.0190641247833625,1615.0,2313.0
+zs__cal_n5,Abd-S57,adult_sepsis,1500,14291,150,0.39466666666666667,0.8422783570079071,0.023333333333333334,0.009026660135749773,0.3933333333333333,0.016164019312854244,0.14666666666666667,0.10671051710866979,0.014666666666666666,0.0012595339724302008,0.027333333333333334,0.024560912462388916,0.8952233806824808,0.7345720589161002,0.8218927074231898,12,False,1.0,1.0,0.98,0.9666666666666667,39.33333333333333,1.6164019312854245,1708.0,852.0
+zs__cal_n5,Abd-S79,adult_sepsis,1500,8044,150,0.394,0.5500994530084535,0.246,0.30892590750870214,0.2613333333333333,0.024365987071108902,0.062,0.09373446046742914,0.010666666666666666,0.00012431626056688214,0.026,0.022749875683739432,0.5015196419691695,0.39858821404713074,0.8600334438891645,7,False,0.9866666666666667,0.52,0.9933333333333333,0.7733333333333333,26.13333333333333,2.4365987071108903,1963.0,2389.5
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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/fold2/summary.json

@@ -0,0 +1,1356 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-01T14:22:08.880298+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/fold2",
+  "--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/fold2/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/fold2/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/fold2/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/fold2/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"
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+ "donors": [
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+   "ped_sepsis"
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+   "adult_ICU_ctrl",
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+   "ped_ICU_ctrl"
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+   false,
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+   "ped_sepsis",
+   "ped_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_HC",
+   "adult_ICU_ctrl",
+   "adult_ICU_ctrl",
+   "ped_HC",
+   "ped_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis"
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+ "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"
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+  "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,
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+   "B cell",
+   "Monocyte",
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+   "DC"
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