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/fold4/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 0.9975, 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/lora_rank_20261001/fold4/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,3262,0.5244375518742862,0.42886210696669597,0.46455719716765786,0.2848338363964104,12,4,12,4,58503,13930,,,,,,,,,,,
+lora_r4_n5__raw,T cell,pseudobulk_log1p_cpm,3262,0.43193323332298933,0.363753414539451,0.39307832586405145,0.20148824187846065,12,4,12,4,58503,13930,,,,,,,,,,,
+lora_r4_n5__raw,B cell,mean_cp10k,3296,0.37829354440008406,0.4365447930339029,0.36070999420128846,0.12942186072464099,12,4,12,4,21119,4109,,,,,,,,,,,
+lora_r4_n5__raw,B cell,pseudobulk_log1p_cpm,3296,0.16442334635722766,0.2531553185437933,0.16588224240401517,0.015659509065093627,12,4,12,4,21119,4109,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,mean_cp10k,3254,0.18295704076825162,0.5930541522716729,0.14805209650573667,0.1086868805047991,12,4,12,4,18965,4034,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,pseudobulk_log1p_cpm,3254,0.19597684013435518,0.5641311182618092,0.1511187531548439,0.12803271176792144,12,4,12,4,18965,4034,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,mean_cp10k,3218,0.2098210483624047,0.35224831574513416,0.16769541299951488,0.15552539583503194,12,4,12,4,9793,1099,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,pseudobulk_log1p_cpm,3218,0.09348633217393171,0.08433113194594508,0.0906279176459272,0.03851798631821806,12,4,12,4,9793,1099,,,,,,,,,,,
+lora_r4_n5__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,13930,True,0,0.0,35,3,35,3,0,0.0,0,0
+lora_r4_n5__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4109,True,0,0.0,3,1,3,1,0,0.0,0,0
+lora_r4_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,4034,True,0,0.0,1,0,1,0,0,0.0,0,0
+lora_r4_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1099,True,0,0.0,0,2,0,2,0,,0,0
+lora_r4_n5__raw,DC,welch_top50_deg,5975,,,,,11,4,9,3,630,100,True,0,,0,0,0,0,0,,0,0
+lora_r64_n5__raw,T cell,mean_cp10k,3262,0.5061919894543161,0.42886210696669597,0.4443074381189804,0.28254344652867064,12,4,12,4,58503,13522,,,,,,,,,,,
+lora_r64_n5__raw,T cell,pseudobulk_log1p_cpm,3262,0.4316642540811813,0.363753414539451,0.3857727249965585,0.2238907946780021,12,4,12,4,58503,13522,,,,,,,,,,,
+lora_r64_n5__raw,B cell,mean_cp10k,3296,0.4202885625745153,0.4365447930339029,0.42324755384551715,0.09437696266912862,12,4,12,4,21119,4298,,,,,,,,,,,
+lora_r64_n5__raw,B cell,pseudobulk_log1p_cpm,3296,0.2725657415461128,0.2531553185437933,0.27272184708822844,0.03511827024594539,12,4,12,4,21119,4298,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,mean_cp10k,3254,0.2442069300157903,0.5930541522716729,0.2052271935532107,0.13572929755966243,12,4,12,4,18965,4233,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,pseudobulk_log1p_cpm,3254,0.18286682380906638,0.5641311182618092,0.11608533809388927,0.15633864325935742,12,4,12,4,18965,4233,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,mean_cp10k,3218,0.19772391498310501,0.35224831574513416,0.14478620013184093,0.18313584580132394,12,4,12,4,9793,1090,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,pseudobulk_log1p_cpm,3218,0.12667176944499225,0.08433113194594508,0.12248905564468461,0.057137129141743445,12,4,12,4,9793,1090,,,,,,,,,,,
+lora_r64_n5__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,13522,True,0,0.0,35,0,35,0,0,0.0,0,0
+lora_r64_n5__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4298,True,0,0.0,3,0,3,0,0,0.0,0,0
+lora_r64_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,4233,True,0,0.0,1,0,1,0,0,0.0,0,0
+lora_r64_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1090,True,0,,0,0,0,0,0,,0,0
+lora_r64_n5__raw,DC,welch_top50_deg,5975,,,,,11,4,9,4,630,102,True,0,,0,0,0,0,0,,0,0
+lora_r4_n20__raw,T cell,mean_cp10k,3262,0.5041521229346402,0.42886210696669597,0.41567111387460676,0.3580089570424592,12,4,12,4,58503,12472,,,,,,,,,,,
+lora_r4_n20__raw,T cell,pseudobulk_log1p_cpm,3262,0.3910536692626687,0.363753414539451,0.3383999775108741,0.2321572400969406,12,4,12,4,58503,12472,,,,,,,,,,,
+lora_r4_n20__raw,B cell,mean_cp10k,3296,0.379724244798429,0.4365447930339029,0.3121578456862138,0.24633300763750016,12,4,12,4,21119,4421,,,,,,,,,,,
+lora_r4_n20__raw,B cell,pseudobulk_log1p_cpm,3296,0.15444143480627864,0.2531553185437933,0.13730335803151977,0.08737173394071263,12,4,12,4,21119,4421,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,mean_cp10k,3254,0.31500466568406044,0.5930541522716729,0.173400350464327,0.307116179300156,12,4,12,4,18965,4635,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,pseudobulk_log1p_cpm,3254,0.23335793098608087,0.5641311182618092,0.11101331040755881,0.25661647641757557,12,4,12,4,18965,4635,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,mean_cp10k,3218,0.3586080449460064,0.35224831574513416,0.316558678487183,0.19274491717347517,12,4,12,4,9793,1624,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,pseudobulk_log1p_cpm,3218,0.08776082761155904,0.08433113194594508,0.08508405484229647,0.03598641150202491,12,4,12,4,9793,1624,,,,,,,,,,,
+lora_r4_n20__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,12472,True,0,0.0,35,0,35,0,0,0.0,0,0
+lora_r4_n20__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4421,True,0,0.0,3,0,3,0,0,0.0,0,0
+lora_r4_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,4635,True,0,0.0,1,0,1,0,0,0.0,0,0
+lora_r4_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1624,True,0,,0,0,0,0,0,,0,0
+lora_r4_n20__raw,DC,welch_top50_deg,5975,,,,,11,4,10,3,630,124,True,0,,0,0,0,0,0,,0,0
+lora_r64_n20__raw,T cell,mean_cp10k,3262,0.524137166608226,0.42886210696669597,0.432525049431676,0.37906717577538995,12,4,12,4,58503,12694,,,,,,,,,,,
+lora_r64_n20__raw,T cell,pseudobulk_log1p_cpm,3262,0.4165169027041989,0.363753414539451,0.36500902539995894,0.23696438228997313,12,4,12,4,58503,12694,,,,,,,,,,,
+lora_r64_n20__raw,B cell,mean_cp10k,3296,0.4705264351593062,0.4365447930339029,0.3803749114411036,0.3408698584487189,12,4,12,4,21119,4214,,,,,,,,,,,
+lora_r64_n20__raw,B cell,pseudobulk_log1p_cpm,3296,0.1925930390083075,0.2531553185437933,0.16568151676163292,0.13327110656391689,12,4,12,4,21119,4214,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,mean_cp10k,3254,0.447609607491644,0.5930541522716729,0.2767449860850088,0.41248153123953896,12,4,12,4,18965,4351,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,pseudobulk_log1p_cpm,3254,0.2931384899036231,0.5641311182618092,0.1380511525039289,0.3288059038186617,12,4,12,4,18965,4351,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,mean_cp10k,3218,0.4254132233173276,0.35224831574513416,0.3699047905455858,0.258220730499428,12,4,12,4,9793,1728,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,pseudobulk_log1p_cpm,3218,0.15373619474412206,0.08433113194594508,0.14988890392729678,0.05459394928300484,12,4,12,4,9793,1728,,,,,,,,,,,
+lora_r64_n20__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,12694,True,0,0.0,35,1,35,1,0,0.0,0,0
+lora_r64_n20__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4214,True,0,0.0,3,0,3,0,0,0.0,0,0
+lora_r64_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,4351,True,0,0.0,1,0,1,0,0,0.0,0,0
+lora_r64_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,4,12,4,9793,1728,True,0,,0,0,0,0,0,,0,0
+lora_r64_n20__raw,DC,welch_top50_deg,5975,,,,,11,4,11,4,630,92,True,0,,0,0,0,0,0,,0,0
+lora_r4__raw,T cell,mean_cp10k,3262,0.5663999857934275,0.42886210696669597,0.4728161250183088,0.4141979723028943,12,4,12,4,58503,13216,,,,,,,,,,,
+lora_r4__raw,T cell,pseudobulk_log1p_cpm,3262,0.4739518329777934,0.363753414539451,0.42416831047750203,0.2517183561923914,12,4,12,4,58503,13216,,,,,,,,,,,
+lora_r4__raw,B cell,mean_cp10k,3296,0.5212266237848271,0.4365447930339029,0.4722439812730194,0.2521822780087688,12,4,12,4,21119,4856,,,,,,,,,,,
+lora_r4__raw,B cell,pseudobulk_log1p_cpm,3296,0.20719577708835007,0.2531553185437933,0.19207958544380024,0.08722426089947785,12,4,12,4,21119,4856,,,,,,,,,,,
+lora_r4__raw,Monocyte,mean_cp10k,3254,0.45652031379455277,0.5930541522716729,0.3091636291760724,0.38184635987273285,12,4,12,4,18965,3441,,,,,,,,,,,
+lora_r4__raw,Monocyte,pseudobulk_log1p_cpm,3254,0.26635762057617707,0.5641311182618092,0.1395932288795015,0.27576337337093426,12,4,12,4,18965,3441,,,,,,,,,,,
+lora_r4__raw,NK cell,mean_cp10k,3218,0.3963877929909515,0.35224831574513416,0.35086443976271814,0.2148480652113048,12,4,12,4,9793,1612,,,,,,,,,,,
+lora_r4__raw,NK cell,pseudobulk_log1p_cpm,3218,0.13108711156352185,0.08433113194594508,0.12813040604227457,0.04179594268272428,12,4,12,4,9793,1612,,,,,,,,,,,
+lora_r4__raw,T cell,welch_top50_deg,5975,,,,,12,4,12,4,58503,13216,True,0,0.0,35,0,35,0,0,0.0,0,0
+lora_r4__raw,B cell,welch_top50_deg,5975,,,,,12,4,12,4,21119,4856,True,0,0.0,3,0,3,0,0,0.0,0,0
+lora_r4__raw,Monocyte,welch_top50_deg,5975,,,,,12,4,12,4,18965,3441,True,0,0.0,1,2,1,2,0,0.0,0,0
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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/fold4/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/fold4/summary.json

@@ -0,0 +1,1926 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-01T14:17:17.030594+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/fold4",
+  "--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/fold4/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/fold4/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/fold4/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/fold4/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/fold4/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/fold4/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-PS20",
+  "Abd-PS27",
+  "Abd-S42",
+  "Abd-S54",
+  "Abd-S98",
+  "HC16",
+  "IC15",
+  "PHC02",
+  "PIC02",
+  "Res-PS03",
+  "Res-S127",
+  "Res-S164",
+  "Res-S166",
+  "Res-S87",
+  "Skin-S43",
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