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/lora_rank_20261001/fold0/README.md

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+# score_ft.py output: ye2026_sepsis, 17 scored donors
+
+Arms: lora_r4_n5__raw, lora_r64_n5__raw, lora_r4_n20__raw, lora_r64_n20__raw, lora_r4__raw, lora_r64__raw.  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 17 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) (2550 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 0.9969, 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
+   0.9961, 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 = 5); 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 + 5
+   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/lora_rank_20261001/fold0/contrast.csv

@@ -0,0 +1,79 @@
+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
+lora_r4_n5__raw,T cell,mean_cp10k,3276,0.15692940823081752,0.3932774345878696,0.06505793086557916,0.251836791431376,12,5,12,5,61162,12508,,,,,,,,,,,
+lora_r4_n5__raw,T cell,pseudobulk_log1p_cpm,3276,0.13714650905629402,0.34430236190429087,0.03410950690012926,0.30989929547460565,12,5,12,5,61162,12508,,,,,,,,,,,
+lora_r4_n5__raw,B cell,mean_cp10k,3261,0.24138789974646807,0.47022569734381875,0.14498934732614638,0.24985117545253238,12,5,12,5,21022,3807,,,,,,,,,,,
+lora_r4_n5__raw,B cell,pseudobulk_log1p_cpm,3261,0.20440255845978023,0.4467015184665126,0.12435887988227518,0.21429405045891126,12,5,12,5,21022,3807,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,mean_cp10k,3260,0.36103063739025765,0.7018160115879556,0.19318409551090263,0.32927222426789104,12,5,12,5,31471,7275,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,pseudobulk_log1p_cpm,3260,0.3330695463690264,0.7631886901658,0.0870100163868638,0.367916064309476,12,5,12,5,31471,7275,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,mean_cp10k,3269,0.05588509569564679,0.3607355096498457,0.009926757179724537,0.1294706216294934,12,5,12,5,10457,958,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,pseudobulk_log1p_cpm,3269,-0.05950565556671146,0.2468852852573835,-0.0958514366824234,0.13191147739555273,12,5,12,5,10457,958,,,,,,,,,,,
+lora_r4_n5__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,61162,12508,True,0,0.0,50,0,113,0,0,0.0,0,0
+lora_r4_n5__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21022,3807,True,0,0.0,50,1,164,1,0,0.0,0,0
+lora_r4_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,31471,7275,True,0,0.0,50,2,932,2,0,0.0,0,0
+lora_r4_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,10457,958,True,0,0.0,50,0,122,0,0,0.0,0,0
+lora_r4_n5__raw,DC,welch_top50_deg,5975,,,,,12,5,12,5,1035,130,True,0,0.0,50,0,152,0,0,0.0,0,0
+lora_r64_n5__raw,T cell,mean_cp10k,3276,0.23321333452238857,0.3932774345878696,0.15384445643137712,0.24422706871517272,12,5,12,5,61162,12382,,,,,,,,,,,
+lora_r64_n5__raw,T cell,pseudobulk_log1p_cpm,3276,0.1676454561040724,0.34430236190429087,0.06890550360248529,0.30816306922164377,12,5,12,5,61162,12382,,,,,,,,,,,
+lora_r64_n5__raw,B cell,mean_cp10k,3261,0.29389578981465686,0.47022569734381875,0.20429727898988975,0.2541650988809243,12,5,12,5,21022,3831,,,,,,,,,,,
+lora_r64_n5__raw,B cell,pseudobulk_log1p_cpm,3261,0.24626026017672903,0.4467015184665126,0.16537079387718706,0.2288612444500953,12,5,12,5,21022,3831,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,mean_cp10k,3260,0.34690752042039213,0.7018160115879556,0.1496980856346204,0.3520823502212437,12,5,12,5,31471,7368,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,pseudobulk_log1p_cpm,3260,0.3673846367395435,0.7631886901658,0.08216745509508463,0.41818696644900905,12,5,12,5,31471,7368,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,mean_cp10k,3269,0.11737069956839447,0.3607355096498457,0.06423633896356296,0.16146352077086343,12,5,12,5,10457,1048,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,pseudobulk_log1p_cpm,3269,-0.014027472512825967,0.2468852852573835,-0.06174737847710236,0.18151938483895438,12,5,12,5,10457,1048,,,,,,,,,,,
+lora_r64_n5__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,61162,12382,True,0,0.0,50,0,113,0,0,0.0,0,0
+lora_r64_n5__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21022,3831,True,0,0.0,50,5,164,5,0,0.0,0,0
+lora_r64_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,31471,7368,True,0,0.0,50,0,932,0,0,0.0,0,0
+lora_r64_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,10457,1048,True,0,0.0,50,1,122,1,0,0.0,0,0
+lora_r64_n5__raw,DC,welch_top50_deg,5975,,,,,12,5,12,5,1035,83,True,0,0.0,50,5,152,5,0,0.0,0,0
+lora_r4_n20__raw,T cell,mean_cp10k,3276,0.47674906037369746,0.3932774345878696,0.4316436612262179,0.23024417684018192,12,5,12,5,61162,12836,,,,,,,,,,,
+lora_r4_n20__raw,T cell,pseudobulk_log1p_cpm,3276,0.39333782437305437,0.34430236190429087,0.33584905147079375,0.2574926046026266,12,5,12,5,61162,12836,,,,,,,,,,,
+lora_r4_n20__raw,B cell,mean_cp10k,3261,0.384013302734918,0.47022569734381875,0.31442815286964787,0.2444200285355327,12,5,12,5,21022,4045,,,,,,,,,,,
+lora_r4_n20__raw,B cell,pseudobulk_log1p_cpm,3261,0.3362783065038579,0.4467015184665126,0.276196155263446,0.2122198474706068,12,5,12,5,21022,4045,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,mean_cp10k,3260,0.48774921786662706,0.7018160115879556,0.30610420539047134,0.4118541972587604,12,5,12,5,31471,6207,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,pseudobulk_log1p_cpm,3260,0.44182967176176774,0.7631886901658,0.25480417101009495,0.37931076299712296,12,5,12,5,31471,6207,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,mean_cp10k,3269,0.24759441908482166,0.3607355096498457,0.20488618142161988,0.1637880865169867,12,5,12,5,10457,1113,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,pseudobulk_log1p_cpm,3269,0.04777829055790498,0.2468852852573835,0.022560772486100528,0.10546525718364401,12,5,12,5,10457,1113,,,,,,,,,,,
+lora_r4_n20__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,61162,12836,True,0,0.0,50,0,113,0,0,0.0,0,0
+lora_r4_n20__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21022,4045,True,0,0.0,50,21,164,21,0,0.0,0,0
+lora_r4_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,31471,6207,True,0,0.0,50,0,932,0,0,0.0,0,0
+lora_r4_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,10457,1113,True,0,0.0,50,0,122,0,0,0.0,0,0
+lora_r4_n20__raw,DC,welch_top50_deg,5975,,,,,12,5,11,5,1035,185,True,0,0.0,50,0,152,0,0,0.0,0,0
+lora_r64_n20__raw,T cell,mean_cp10k,3276,0.5171011195440144,0.3932774345878696,0.4762042726149594,0.23191341114952707,12,5,12,5,61162,12610,,,,,,,,,,,
+lora_r64_n20__raw,T cell,pseudobulk_log1p_cpm,3276,0.4059757484358993,0.34430236190429087,0.36564065862012707,0.20279879939453668,12,5,12,5,61162,12610,,,,,,,,,,,
+lora_r64_n20__raw,B cell,mean_cp10k,3261,0.44237646655624546,0.47022569734381875,0.3838824894406615,0.241632045134575,12,5,12,5,21022,4270,,,,,,,,,,,
+lora_r64_n20__raw,B cell,pseudobulk_log1p_cpm,3261,0.4118007554325622,0.4467015184665126,0.3533174415653548,0.23384266645780166,12,5,12,5,21022,4270,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,mean_cp10k,3260,0.4976129295960048,0.7018160115879556,0.31970504184047027,0.41358302432493826,12,5,12,5,31471,6335,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,pseudobulk_log1p_cpm,3260,0.4557730024496201,0.7631886901658,0.25369025465823414,0.4003681245382531,12,5,12,5,31471,6335,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,mean_cp10k,3269,0.29113682964452364,0.3607355096498457,0.2608589115744438,0.13918865179020715,12,5,12,5,10457,1152,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,pseudobulk_log1p_cpm,3269,0.14186126432753865,0.2468852852573835,0.1296664626602437,0.06678904130661366,12,5,12,5,10457,1152,,,,,,,,,,,
+lora_r64_n20__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,61162,12610,True,0,0.0,50,0,113,0,0,0.0,0,0
+lora_r64_n20__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21022,4270,True,0,0.0,50,41,164,41,0,0.0,0,0
+lora_r64_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,31471,6335,True,0,0.0,50,0,932,0,0,0.0,0,0
+lora_r64_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,10457,1152,True,0,0.0,50,1,122,1,0,0.0,0,0
+lora_r64_n20__raw,DC,welch_top50_deg,5975,,,,,12,5,12,5,1035,176,True,0,0.0,50,33,152,33,0,0.0,0,0
+lora_r4__raw,T cell,mean_cp10k,3276,0.4301796196638663,0.3932774345878696,0.35859741292432834,0.292034101902441,12,5,12,5,61162,13757,,,,,,,,,,,
+lora_r4__raw,T cell,pseudobulk_log1p_cpm,3276,0.3944801727245482,0.34430236190429087,0.34699317029568105,0.22346991769805752,12,5,12,5,61162,13757,,,,,,,,,,,
+lora_r4__raw,B cell,mean_cp10k,3261,0.47221827660033977,0.47022569734381875,0.41546281507716915,0.249041557403281,12,5,12,5,21022,5256,,,,,,,,,,,
+lora_r4__raw,B cell,pseudobulk_log1p_cpm,3261,0.3684517587323292,0.4467015184665126,0.31275110622440583,0.21277268989103856,12,5,12,5,21022,5256,,,,,,,,,,,
+lora_r4__raw,Monocyte,mean_cp10k,3260,0.4536506041603273,0.7018160115879556,0.2652962391113456,0.39954100199425663,12,5,12,5,31471,3970,,,,,,,,,,,
+lora_r4__raw,Monocyte,pseudobulk_log1p_cpm,3260,0.4288744568112384,0.7631886901658,0.23962180641630468,0.37377293452910576,12,5,12,5,31471,3970,,,,,,,,,,,
+lora_r4__raw,NK cell,mean_cp10k,3269,0.2649117314009065,0.3607355096498457,0.2190938551192757,0.17683354570113965,12,5,12,5,10457,1324,,,,,,,,,,,
+lora_r4__raw,NK cell,pseudobulk_log1p_cpm,3269,0.10283235164759039,0.2468852852573835,0.07965732297853668,0.10560569610364173,12,5,12,5,10457,1324,,,,,,,,,,,
+lora_r4__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,61162,13757,True,0,0.0,50,0,113,0,0,0.0,0,0
+lora_r4__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21022,5256,True,0,0.0,50,0,164,0,0,0.0,0,0
+lora_r4__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,31471,3970,True,0,0.0,50,2,932,2,0,0.0,0,0
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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/lora_rank_20261001/fold0/per_donor.csv

@@ -0,0 +1,103 @@
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+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-01T14:14:06.661967+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/lora_rank_20261001/fold0",
+  "--threads",
+  "8",
+  "--arm",
+  "lora_r4_n5__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold0/lora_r4_n5__raw.npz",
+  "--arm",
+  "lora_r64_n5__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold0/lora_r64_n5__raw.npz",
+  "--arm",
+  "lora_r4_n20__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold0/lora_r4_n20__raw.npz",
+  "--arm",
+  "lora_r64_n20__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold0/lora_r64_n20__raw.npz",
+  "--arm",
+  "lora_r4__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold0/lora_r4__raw.npz",
+  "--arm",
+  "lora_r64__raw=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ye2026_sepsis/fold0/lora_r64__raw.npz",
+  "--case-groups",
+  "adult_sepsis,ped_sepsis",
+  "--control-groups",
+  "adult_HC,adult_ICU_ctrl,ped_HC,ped_ICU_ctrl"
+ ],
+ "arms": [
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+  "lora_r64_n5__raw",
+  "lora_r4_n20__raw",
+  "lora_r64_n20__raw",
+  "lora_r4__raw",
+  "lora_r64__raw"
+ ],
+ "donors": [
+  "Abd-PS19",
+  "Abd-PS23",
+  "Abd-S123",
+  "Abd-S39",
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+  "Abd-S66",
+  "HC01",
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+  "IC23",
+  "PHC03",
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+  "Skin-S140",
+  "Uri-S147"
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+  "group_source": "features groups",
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+   "ped_sepsis"
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+  "control_groups": [
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+   "ped_HC",
+   "ped_ICU_ctrl"
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+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_HC",
+   "adult_HC",
+   "adult_ICU_ctrl",
+   "ped_HC",
+   "ped_ICU_ctrl",
+   "ped_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
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+  "functions": [
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+   "top_deg_genes",
+   "jaccard"
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