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/fold1/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 0.9980, 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
+   0.9965, 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/fold1/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,3244,0.26325641150384266,0.11447476231901513,0.2515706659000684,0.13716202036896477,12,5,12,5,67325,13278,,,,,,,,,,,
+lora_r4_n5__raw,T cell,pseudobulk_log1p_cpm,3244,0.2035773075754312,0.15597694662484468,0.18585039225287067,0.13979058096050948,12,5,12,5,67325,13278,,,,,,,,,,,
+lora_r4_n5__raw,B cell,mean_cp10k,3302,0.2003388521595182,0.20148362032609382,0.17164163391916146,0.1723928452961794,12,5,12,5,21250,5126,,,,,,,,,,,
+lora_r4_n5__raw,B cell,pseudobulk_log1p_cpm,3302,0.1953352630288154,0.3280028171270088,0.15705016794611945,0.14820621647618995,12,5,12,5,21250,5126,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,mean_cp10k,3234,0.15116822812994657,0.4917801908254279,0.11826141017925175,0.09903186985429,12,5,12,5,21940,5356,,,,,,,,,,,
+lora_r4_n5__raw,Monocyte,pseudobulk_log1p_cpm,3234,-0.015436519739464593,0.47511664678816756,-0.07192339179566971,0.10004490165833536,12,5,12,5,21940,5356,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,mean_cp10k,3190,0.12978049866614952,0.07549722484921216,0.12602922094400867,0.057174759415894054,12,5,12,5,11270,981,,,,,,,,,,,
+lora_r4_n5__raw,NK cell,pseudobulk_log1p_cpm,3190,0.0317123988009356,0.0598276856464892,0.029641738305357088,0.035815196912317875,12,5,12,5,11270,981,,,,,,,,,,,
+lora_r4_n5__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,67325,13278,True,0,0.0,0,1,0,1,0,0.0,5,5
+lora_r4_n5__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21250,5126,True,0,0.0,3,0,3,0,0,0.0,5,5
+lora_r4_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,21940,5356,True,0,0.0,1,0,1,0,0,0.0,5,5
+lora_r4_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,11270,981,True,0,0.0,36,0,36,0,0,0.0,5,5
+lora_r4_n5__raw,DC,welch_top50_deg,5975,,,,,11,5,7,2,654,70,True,0,0.0,5,0,5,0,0,0.0,5,5
+lora_r64_n5__raw,T cell,mean_cp10k,3244,0.2932781616078673,0.11447476231901513,0.2810107924214087,0.15141737280831222,12,5,12,5,67325,14166,,,,,,,,,,,
+lora_r64_n5__raw,T cell,pseudobulk_log1p_cpm,3244,0.21739270617061954,0.15597694662484468,0.20082121493755792,0.13336128144064818,12,5,12,5,67325,14166,,,,,,,,,,,
+lora_r64_n5__raw,B cell,mean_cp10k,3302,0.19250939173960513,0.20148362032609382,0.16358604793340759,0.17206303408036683,12,5,12,5,21250,5158,,,,,,,,,,,
+lora_r64_n5__raw,B cell,pseudobulk_log1p_cpm,3302,0.23571915817329506,0.3280028171270088,0.19090922051654996,0.1775505334485131,12,5,12,5,21250,5158,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,mean_cp10k,3234,0.15869093101359746,0.4917801908254279,0.11819357967817022,0.1148031751233844,12,5,11,5,21940,4427,,,,,,,,,,,
+lora_r64_n5__raw,Monocyte,pseudobulk_log1p_cpm,3234,0.0377642771157495,0.47511664678816756,-0.007038408734752875,0.09246361366589785,12,5,11,5,21940,4427,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,mean_cp10k,3190,0.11938244538851113,0.07549722484921216,0.11510076812970055,0.06420030627015634,12,5,12,5,11270,1053,,,,,,,,,,,
+lora_r64_n5__raw,NK cell,pseudobulk_log1p_cpm,3190,0.028580517375016032,0.0598276856464892,0.027038480128812322,0.02674557424296962,12,5,12,5,11270,1053,,,,,,,,,,,
+lora_r64_n5__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,67325,14166,True,0,,0,0,0,0,0,0.0,5,5
+lora_r64_n5__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21250,5158,True,0,0.0,3,1,3,1,0,0.0,5,5
+lora_r64_n5__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,11,5,21940,4427,True,0,0.0,1,0,1,0,0,0.0,5,5
+lora_r64_n5__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,11270,1053,True,0,0.0,36,4,36,4,0,0.0,5,5
+lora_r64_n5__raw,DC,welch_top50_deg,5975,,,,,11,5,9,3,654,76,True,0,0.0,5,0,5,0,0,0.0,5,5
+lora_r4_n20__raw,T cell,mean_cp10k,3244,0.12761754972351613,0.11447476231901513,0.10323330774384164,0.24660639297820758,12,5,12,5,67325,13291,,,,,,,,,,,
+lora_r4_n20__raw,T cell,pseudobulk_log1p_cpm,3244,0.20852425558059634,0.15597694662484468,0.18352313852810792,0.19760463427036118,12,5,12,5,67325,13291,,,,,,,,,,,
+lora_r4_n20__raw,B cell,mean_cp10k,3302,0.07996508836254157,0.20148362032609382,0.03146613573226776,0.24871890962338386,12,5,12,5,21250,3895,,,,,,,,,,,
+lora_r4_n20__raw,B cell,pseudobulk_log1p_cpm,3302,0.2654348263737978,0.3280028171270088,0.2040373152326803,0.23856809470296836,12,5,12,5,21250,3895,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,mean_cp10k,3234,0.31904809308334586,0.4917801908254279,0.20280909493388288,0.3070215543913732,12,5,12,5,21940,5377,,,,,,,,,,,
+lora_r4_n20__raw,Monocyte,pseudobulk_log1p_cpm,3234,0.24198824165788613,0.47511664678816756,0.13182660835606885,0.27456170010426156,12,5,12,5,21940,5377,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,mean_cp10k,3190,0.03683097015841813,0.07549722484921216,0.02904932678156092,0.10634579481897251,12,5,12,5,11270,1720,,,,,,,,,,,
+lora_r4_n20__raw,NK cell,pseudobulk_log1p_cpm,3190,0.05714606949471153,0.0598276856464892,0.053541579761286866,0.06366308471656684,12,5,12,5,11270,1720,,,,,,,,,,,
+lora_r4_n20__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,67325,13291,True,0,,0,0,0,0,0,0.0,5,5
+lora_r4_n20__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21250,3895,True,0,0.0,3,0,3,0,0,0.0,5,5
+lora_r4_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,21940,5377,True,0,0.0,1,0,1,0,0,0.0,5,5
+lora_r4_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,11270,1720,True,0,0.0,36,1,36,1,0,0.0,5,5
+lora_r4_n20__raw,DC,welch_top50_deg,5975,,,,,11,5,10,5,654,102,True,0,0.0,5,0,5,0,0,0.0,5,5
+lora_r64_n20__raw,T cell,mean_cp10k,3244,0.1714398021677271,0.11447476231901513,0.1471761655804747,0.2666522687862158,12,5,12,5,67325,13647,,,,,,,,,,,
+lora_r64_n20__raw,T cell,pseudobulk_log1p_cpm,3244,0.2090005509997477,0.15597694662484468,0.18145115599701123,0.21867283726873687,12,5,12,5,67325,13647,,,,,,,,,,,
+lora_r64_n20__raw,B cell,mean_cp10k,3302,0.13851073670122602,0.20148362032609382,0.08695071887025997,0.2818946304591375,12,5,12,5,21250,4197,,,,,,,,,,,
+lora_r64_n20__raw,B cell,pseudobulk_log1p_cpm,3302,0.33612007926922993,0.3280028171270088,0.26922104391648227,0.28049459692913836,12,5,12,5,21250,4197,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,mean_cp10k,3234,0.45074535539695115,0.4917801908254279,0.33182781129950356,0.37096286893531805,12,5,12,5,21940,5129,,,,,,,,,,,
+lora_r64_n20__raw,Monocyte,pseudobulk_log1p_cpm,3234,0.35071814094237597,0.47511664678816756,0.22994226689979297,0.3372677982364816,12,5,12,5,21940,5129,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,mean_cp10k,3190,0.0945471139580771,0.07549722484921216,0.08437373041332479,0.15066154679184457,12,5,12,5,11270,1534,,,,,,,,,,,
+lora_r64_n20__raw,NK cell,pseudobulk_log1p_cpm,3190,0.03169150277612871,0.0598276856464892,0.028551664110453498,0.05403211357523926,12,5,12,5,11270,1534,,,,,,,,,,,
+lora_r64_n20__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,67325,13647,True,0,,0,0,0,0,0,0.0,5,5
+lora_r64_n20__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21250,4197,True,0,0.0,3,2,3,2,0,0.0,5,5
+lora_r64_n20__raw,Monocyte,welch_top50_deg,5975,,,,,12,5,12,5,21940,5129,True,0,0.0,1,0,1,0,0,0.0,5,5
+lora_r64_n20__raw,NK cell,welch_top50_deg,5975,,,,,12,5,12,5,11270,1534,True,0,0.0,36,0,36,0,0,0.0,5,5
+lora_r64_n20__raw,DC,welch_top50_deg,5975,,,,,11,5,10,5,654,114,True,0,0.0,5,0,5,0,0,0.0,5,5
+lora_r4__raw,T cell,mean_cp10k,3244,0.42678417946923913,0.11447476231901513,0.41614540199628425,0.1684208193479739,12,5,12,5,67325,13822,,,,,,,,,,,
+lora_r4__raw,T cell,pseudobulk_log1p_cpm,3244,0.42424116662178285,0.15597694662484468,0.4201107641474795,0.06508775476907834,12,5,12,5,67325,13822,,,,,,,,,,,
+lora_r4__raw,B cell,mean_cp10k,3302,0.355413259826774,0.20148362032609382,0.3329324869012867,0.16864657913131822,12,5,12,5,21250,3739,,,,,,,,,,,
+lora_r4__raw,B cell,pseudobulk_log1p_cpm,3302,0.34079052033146934,0.3280028171270088,0.3162548610592795,0.13669484390484762,12,5,12,5,21250,3739,,,,,,,,,,,
+lora_r4__raw,Monocyte,mean_cp10k,3234,0.4221639276951814,0.4917801908254279,0.34755766103975066,0.2650888803153907,12,5,12,5,21940,5308,,,,,,,,,,,
+lora_r4__raw,Monocyte,pseudobulk_log1p_cpm,3234,0.3257011258742,0.47511664678816756,0.264668854071874,0.20584492040914928,12,5,12,5,21940,5308,,,,,,,,,,,
+lora_r4__raw,NK cell,mean_cp10k,3190,0.09462709929906321,0.07549722484921216,0.08692881532753309,0.11254891909927726,12,5,12,5,11270,1512,,,,,,,,,,,
+lora_r4__raw,NK cell,pseudobulk_log1p_cpm,3190,0.15410850665151565,0.0598276856464892,0.15498936720455458,-0.009954677759598969,12,5,12,5,11270,1512,,,,,,,,,,,
+lora_r4__raw,T cell,welch_top50_deg,5975,,,,,12,5,12,5,67325,13822,True,0,,0,0,0,0,0,0.0,5,5
+lora_r4__raw,B cell,welch_top50_deg,5975,,,,,12,5,12,5,21250,3739,True,0,0.0,3,1,3,1,0,0.0,5,5
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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/lora_rank_20261001/fold1/per_donor.csv

@@ -0,0 +1,103 @@
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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/fold1/summary.json

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+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-01T14:14:59.294601+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/fold1",
+  "--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/fold1/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/fold1/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/fold1/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/fold1/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/fold1/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/fold1/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-PS07",
+  "Abd-S120",
+  "Abd-S156",
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+  "Res-PS12",
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+  "Uri-S157"
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+  "group_source": "features groups",
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+   "ped_sepsis"
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+   "ped_HC",
+   "ped_ICU_ctrl"
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+   "adult_sepsis",
+   "adult_HC",
+   "adult_HC",
+   "adult_ICU_ctrl",
+   "ped_HC",
+   "ped_ICU_ctrl",
+   "ped_sepsis",
+   "ped_sepsis",
+   "adult_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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