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/fold2/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: ye2026_sepsis, 16 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 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/lora_rank_20261001/fold2/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,3229,0.13475329366001013,0.31034725182586503,0.025907374099183002,0.36017082580171944,12,4,12,4,62381,15805,,,,,,,,,,,
+lora_r4_n5__raw,T cell,pseudobulk_log1p_cpm,3229,-0.05758044203273228,0.34175880321619384,-0.19747634736776126,0.341836432107539,12,4,12,4,62381,15805,,,,,,,,,,,
+lora_r4_n5__raw,B cell,mean_cp10k,3383,0.10941122417891512,0.24396958429685228,0.026334351696918504,0.3504205701754056,12,4,12,4,16254,3501,,,,,,,,,,,
+lora_r4_n5__raw,B cell,pseudobulk_log1p_cpm,3383,-0.08034367133765542,0.19804716577966308,-0.1256173391641496,0.20308150050331422,12,4,12,4,16254,3501,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,mean_cp10k,3329,0.16552607326265395,0.5044336153831825,0.04766846461251735,0.2491197144414617,12,4,12,4,19357,3289,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,pseudobulk_log1p_cpm,3329,0.05772413208102816,0.5334718119101565,-0.08442670879592222,0.23820965140267072,12,4,12,4,19357,3289,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,mean_cp10k,3243,0.07906821244864642,0.27184580545716674,0.014999204097291309,0.23930203119201798,12,4,12,4,8525,609,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,pseudobulk_log1p_cpm,3243,-0.054941700946456576,0.1916717245397386,-0.07094880602370421,0.07561011655215259,12,4,12,4,8525,609,,,,,,,,,,,
+lora_r4_n5__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,15805,True,0,,0,0,0,0,0,,0,0
+lora_r4_n5__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3501,True,0,,0,0,0,0,0,,0,0
+lora_r4_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,3289,True,0,0.0,1,0,1,0,0,0.0,0,0
+lora_r4_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,609,True,0,0.0,5,5,5,5,0,0.0,0,0
+lora_r4_n5__raw,DC,welch_top50_deg,5975,,,,,11,4,12,4,697,138,True,0,,0,0,0,0,0,,0,0
+lora_r64_n5__raw,T cell,mean_cp10k,3229,0.14433719567850964,0.31034725182586503,0.04111870203684815,0.34695625700327654,12,4,12,4,62381,15723,,,,,,,,,,,
+lora_r64_n5__raw,T cell,pseudobulk_log1p_cpm,3229,-0.07162055949531346,0.34175880321619384,-0.21087064345613868,0.33648652481134517,12,4,12,4,62381,15723,,,,,,,,,,,
+lora_r64_n5__raw,B cell,mean_cp10k,3383,0.14121833339811465,0.24396958429685228,0.045304434922499345,0.4149889637131695,12,4,12,4,16254,3879,,,,,,,,,,,
+lora_r64_n5__raw,B cell,pseudobulk_log1p_cpm,3383,-0.05981936790357662,0.19804716577966308,-0.11404733989602235,0.24517877497914456,12,4,12,4,16254,3879,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,mean_cp10k,3329,0.10802326114421452,0.5044336153831825,-0.016430169896410517,0.24143950003625925,12,4,12,4,19357,3100,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,pseudobulk_log1p_cpm,3329,0.0750139665695869,0.5334718119101565,-0.06414882104450095,0.23936572926261654,12,4,12,4,19357,3100,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,mean_cp10k,3243,0.06033679316059374,0.27184580545716674,-0.0030891804853414064,0.23258813298529302,12,4,12,4,8525,606,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,pseudobulk_log1p_cpm,3243,-0.09050357351538446,0.1916717245397386,-0.11144514014875333,0.09584967627856708,12,4,12,4,8525,606,,,,,,,,,,,
+lora_r64_n5__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,15723,True,0,0.0,0,1,0,1,0,,0,0
+lora_r64_n5__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3879,True,0,,0,0,0,0,0,,0,0
+lora_r64_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,3100,True,0,0.0,1,2,1,2,0,0.0,0,0
+lora_r64_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,606,True,0,0.0,5,6,5,6,0,0.0,0,0
+lora_r64_n5__raw,DC,welch_top50_deg,5975,,,,,11,4,10,3,697,122,True,0,0.0,0,2,0,2,0,,0,0
+lora_r4_n20__raw,T cell,mean_cp10k,3229,0.17386607130147827,0.31034725182586503,0.06765082997237795,0.3675113103754241,12,4,12,4,62381,13864,,,,,,,,,,,
+lora_r4_n20__raw,T cell,pseudobulk_log1p_cpm,3229,0.12956335719396864,0.34175880321619384,0.013887790395076385,0.3432382306386364,12,4,12,4,62381,13864,,,,,,,,,,,
+lora_r4_n20__raw,B cell,mean_cp10k,3383,0.11717293424385829,0.24396958429685228,0.05820660907555693,0.25665488515754104,12,4,12,4,16254,3266,,,,,,,,,,,
+lora_r4_n20__raw,B cell,pseudobulk_log1p_cpm,3383,0.035182791329485624,0.19804716577966308,0.0011732908321742345,0.1719280614563093,12,4,12,4,16254,3266,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,mean_cp10k,3329,0.19628353916778932,0.5044336153831825,0.010057489094964659,0.3731444875991732,12,4,12,4,19357,4644,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,pseudobulk_log1p_cpm,3329,0.16402796226860775,0.5334718119101565,-0.01751578422965523,0.3336524171404647,12,4,12,4,19357,4644,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,mean_cp10k,3243,0.04354515834215848,0.27184580545716674,-0.0390198175233128,0.292282505734349,12,4,12,4,8525,1221,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,pseudobulk_log1p_cpm,3243,-0.042514807560991845,0.1916717245397386,-0.06886485303001835,0.1279165635173095,12,4,12,4,8525,1221,,,,,,,,,,,
+lora_r4_n20__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,13864,True,0,0.0,0,1,0,1,0,,0,0
+lora_r4_n20__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3266,True,0,,0,0,0,0,0,,0,0
+lora_r4_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,4644,True,0,0.0,1,0,1,0,0,0.0,0,0
+lora_r4_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,1221,True,0,0.0,5,3,5,3,0,0.0,0,0
+lora_r4_n20__raw,DC,welch_top50_deg,5975,,,,,11,4,12,4,697,187,True,0,,0,0,0,0,0,,0,0
+lora_r64_n20__raw,T cell,mean_cp10k,3229,0.11753097974138646,0.31034725182586503,-0.0019879936800621567,0.38432969894605346,12,4,12,4,62381,13461,,,,,,,,,,,
+lora_r64_n20__raw,T cell,pseudobulk_log1p_cpm,3229,0.14903120592463145,0.34175880321619384,0.020767803286148377,0.38332513887391845,12,4,12,4,62381,13461,,,,,,,,,,,
+lora_r64_n20__raw,B cell,mean_cp10k,3383,0.035565438197877594,0.24396958429685228,-0.040619622290890896,0.2998140869117075,12,4,12,4,16254,3162,,,,,,,,,,,
+lora_r64_n20__raw,B cell,pseudobulk_log1p_cpm,3383,0.013418727119047815,0.19804716577966308,-0.017762726723402737,0.15461094793563493,12,4,12,4,16254,3162,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,mean_cp10k,3329,0.21328239418239345,0.5044336153831825,0.009405559227600098,0.4081176881823348,12,4,12,4,19357,5147,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,pseudobulk_log1p_cpm,3329,0.21516855313218528,0.5334718119101565,-0.004737428635996734,0.4101865185946517,12,4,12,4,19357,5147,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,mean_cp10k,3243,0.011862808119529165,0.27184580545716674,-0.06381051352638846,0.26165884814669543,12,4,12,4,8525,1210,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,pseudobulk_log1p_cpm,3243,-0.022044417186242903,0.1916717245397386,-0.04226422370460002,0.10031194130710863,12,4,12,4,8525,1210,,,,,,,,,,,
+lora_r64_n20__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,13461,True,0,0.0,0,1,0,1,0,,0,0
+lora_r64_n20__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3162,True,0,,0,0,0,0,0,,0,0
+lora_r64_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,5147,True,0,0.0,1,6,1,6,0,0.0,0,0
+lora_r64_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,8525,1210,True,0,0.0,5,7,5,7,0,0.0,0,0
+lora_r64_n20__raw,DC,welch_top50_deg,5975,,,,,11,4,11,4,697,178,True,0,,0,0,0,0,0,,0,0
+lora_r4__raw,T cell,mean_cp10k,3229,0.06542622640317938,0.31034725182586503,-0.055632616737748566,0.3691858367540351,12,4,12,4,62381,13596,,,,,,,,,,,
+lora_r4__raw,T cell,pseudobulk_log1p_cpm,3229,0.11184483129607867,0.34175880321619384,-0.012447460363440981,0.35920671138378524,12,4,12,4,62381,13596,,,,,,,,,,,
+lora_r4__raw,B cell,mean_cp10k,3383,0.0029658100602187676,0.24396958429685228,-0.02360435159315415,0.10546090387162245,12,4,12,4,16254,3131,,,,,,,,,,,
+lora_r4__raw,B cell,pseudobulk_log1p_cpm,3383,0.03026622441629274,0.19804716577966308,0.011062644261778859,0.09833647263816424,12,4,12,4,16254,3131,,,,,,,,,,,
+lora_r4__raw,Monocyte,mean_cp10k,3329,0.17734939257034757,0.5044336153831825,-0.005581124149574456,0.36049222500909284,12,4,12,4,19357,4821,,,,,,,,,,,
+lora_r4__raw,Monocyte,pseudobulk_log1p_cpm,3329,0.1899317718548915,0.5334718119101565,0.016462811211021448,0.33140292245522057,12,4,12,4,19357,4821,,,,,,,,,,,
+lora_r4__raw,NK cell,mean_cp10k,3243,0.06811549095458248,0.27184580545716674,-0.021618438695815607,0.3229945133228076,12,4,12,4,8525,1528,,,,,,,,,,,
+lora_r4__raw,NK cell,pseudobulk_log1p_cpm,3243,-0.010115338404560685,0.1916717245397386,-0.036114144784889175,0.1305659393767817,12,4,12,4,8525,1528,,,,,,,,,,,
+lora_r4__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,62381,13596,True,0,,0,0,0,0,0,,0,0
+lora_r4__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,16254,3131,True,0,,0,0,0,0,0,,0,0
+lora_r4__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,19357,4821,True,0,0.0,1,0,1,0,0,0.0,0,0
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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/fold2/per_donor.csv

@@ -0,0 +1,97 @@
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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/fold2/summary.json

@@ -0,0 +1,1926 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-01T14:15:50.730200+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/fold2",
+  "--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/fold2/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/fold2/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/fold2/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/fold2/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/fold2/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/fold2/lora_r64__raw.npz",
+  "--case-groups",
+  "adult_sepsis,ped_sepsis",
+  "--control-groups",
+  "adult_HC,adult_ICU_ctrl,ped_HC,ped_ICU_ctrl"
+ ],
+ "arms": [
+  "lora_r4_n5__raw",
+  "lora_r64_n5__raw",
+  "lora_r4_n20__raw",
+  "lora_r64_n20__raw",
+  "lora_r4__raw",
+  "lora_r64__raw"
+ ],
+ "donors": [
+  "Abd-PS04",
+  "Abd-PS05",
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