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/fold3/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 0.9967, 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
+   0.9971, 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 = 13) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 3); 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 13 + 3
+   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/fold3/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,3267,0.07503850984687768,0.44101055672166223,-0.039063090510711274,0.24718181654491347,13,3,13,3,48746,12152,,,,,,,,,,,
+zs__cal_n5,T cell,pseudobulk_log1p_cpm,3267,0.03696942461801124,0.42867745714477645,-0.07571738286969226,0.24111062265283942,13,3,13,3,48746,12152,,,,,,,,,,,
+zs__cal_n5,B cell,mean_cp10k,3235,0.09370314725655356,0.37135218590354685,0.0358776537563665,0.1638368199054695,13,3,13,3,20084,3641,,,,,,,,,,,
+zs__cal_n5,B cell,pseudobulk_log1p_cpm,3235,0.09893319080085551,0.2276540469896267,0.08611308528163009,0.06707590476910526,13,3,13,3,20084,3641,,,,,,,,,,,
+zs__cal_n5,Monocyte,mean_cp10k,3323,0.08113701570502997,0.4607653089644908,0.006318810994579985,0.16408558050131242,13,3,13,3,19536,6065,,,,,,,,,,,
+zs__cal_n5,Monocyte,pseudobulk_log1p_cpm,3323,0.04076799336219742,0.4895231789694805,-0.03301525591114798,0.14149955143346032,13,3,13,3,19536,6065,,,,,,,,,,,
+zs__cal_n5,NK cell,mean_cp10k,3307,-0.05487920254830199,0.43169825561671765,-0.10535147341382617,0.09206918482199546,13,3,13,3,7787,1165,,,,,,,,,,,
+zs__cal_n5,NK cell,pseudobulk_log1p_cpm,3307,0.016110637314370502,0.2667926885252451,-0.00587081305698088,0.08152331152362764,13,3,13,3,7787,1165,,,,,,,,,,,
+zs__cal_n5,T cell,welch_top50_deg,5975,,,,,13,3,13,3,48746,12152,True,0,0.0,50,3,97,3,0,0.0,0,0
+zs__cal_n5,B cell,welch_top50_deg,5975,,,,,13,3,13,3,20084,3641,True,0,0.0,50,46,122,46,0,0.0,0,0
+zs__cal_n5,Monocyte,welch_top50_deg,5975,,,,,13,3,13,3,19536,6065,True,0,0.0,5,0,5,0,0,0.0,0,0
+zs__cal_n5,NK cell,welch_top50_deg,5975,,,,,13,3,13,3,7787,1165,True,0,0.0,30,0,30,0,0,0.0,0,0
+zs__cal_n5,DC,welch_top50_deg,5975,,,,,12,3,13,3,284,310,True,0,0.0,50,1,63,1,0,0.0,0,0
+zs__cal_n10,T cell,mean_cp10k,3267,0.04517610802392688,0.44020900457672163,-0.06063475765584601,0.22318156723257718,13,3,13,3,48746,12704,,,,,,,,,,,
+zs__cal_n10,T cell,pseudobulk_log1p_cpm,3267,0.04660814003398023,0.42972623801232585,-0.07478078681272,0.2601810067477858,13,3,13,3,48746,12704,,,,,,,,,,,
+zs__cal_n10,B cell,mean_cp10k,3235,0.12188794802849238,0.36778669756282506,0.0636710850402382,0.1728467049342273,13,3,13,3,20084,3254,,,,,,,,,,,
+zs__cal_n10,B cell,pseudobulk_log1p_cpm,3235,0.09644218858777824,0.22952179361901784,0.0838282645509198,0.06547044875444104,13,3,13,3,20084,3254,,,,,,,,,,,
+zs__cal_n10,Monocyte,mean_cp10k,3323,0.0866876430889173,0.46314881114145035,0.014910258011082277,0.1590009112080326,13,3,13,3,19536,5792,,,,,,,,,,,
+zs__cal_n10,Monocyte,pseudobulk_log1p_cpm,3323,0.019022306760655427,0.48992096860250317,-0.06042599585667251,0.14520985536620643,13,3,13,3,19536,5792,,,,,,,,,,,
+zs__cal_n10,NK cell,mean_cp10k,3307,-0.004360170481334816,0.42530923281952415,-0.07283310865217749,0.14313941101472766,13,3,13,3,7787,1326,,,,,,,,,,,
+zs__cal_n10,NK cell,pseudobulk_log1p_cpm,3307,0.06716884445503461,0.26901942344367813,0.026729690800693866,0.1551421744482923,13,3,13,3,7787,1326,,,,,,,,,,,
+zs__cal_n10,T cell,welch_top50_deg,5975,,,,,13,3,13,3,48746,12704,True,0,0.0,50,5,97,5,0,0.0,0,0
+zs__cal_n10,B cell,welch_top50_deg,5975,,,,,13,3,13,3,20084,3254,True,1,0.0136986301369863,50,24,122,24,0,0.0,0,0
+zs__cal_n10,Monocyte,welch_top50_deg,5975,,,,,13,3,13,3,19536,5792,True,0,0.0,5,4,5,4,0,0.0,0,0
+zs__cal_n10,NK cell,welch_top50_deg,5975,,,,,13,3,13,3,7787,1326,True,0,0.0,30,0,30,0,0,0.0,0,0
+zs__cal_n10,DC,welch_top50_deg,5975,,,,,12,3,13,3,284,307,True,0,0.0,50,14,63,14,0,0.0,0,0
+zs__cal_n20,T cell,mean_cp10k,3267,0.06035893308326488,0.4360322801005671,-0.04514410157809125,0.2291223653181814,13,3,13,3,48746,12045,,,,,,,,,,,
+zs__cal_n20,T cell,pseudobulk_log1p_cpm,3267,0.023454272119816218,0.4273893845599782,-0.08088673514864628,0.22172106432167046,13,3,13,3,48746,12045,,,,,,,,,,,
+zs__cal_n20,B cell,mean_cp10k,3235,0.12233109611502227,0.3696206431353157,0.05915079309621921,0.18482772241956294,13,3,13,3,20084,3256,,,,,,,,,,,
+zs__cal_n20,B cell,pseudobulk_log1p_cpm,3235,0.06798785685524647,0.2257130895592049,0.054474049575110796,0.06662208736316119,13,3,13,3,20084,3256,,,,,,,,,,,
+zs__cal_n20,Monocyte,mean_cp10k,3323,0.09375292549795713,0.45041749292110933,0.017266860448615307,0.17444513057328193,13,3,13,3,19536,6373,,,,,,,,,,,
+zs__cal_n20,Monocyte,pseudobulk_log1p_cpm,3323,0.02785883051032786,0.48983890014752834,-0.05622385068736519,0.1557197109915292,13,3,13,3,19536,6373,,,,,,,,,,,
+zs__cal_n20,NK cell,mean_cp10k,3307,-0.010984575353765432,0.4338807805185912,-0.08365644093850819,0.1465239197728093,13,3,13,3,7787,1410,,,,,,,,,,,
+zs__cal_n20,NK cell,pseudobulk_log1p_cpm,3307,0.049170043420414866,0.26527680569488865,0.008270819798475536,0.155659106530125,13,3,13,3,7787,1410,,,,,,,,,,,
+zs__cal_n20,T cell,welch_top50_deg,5975,,,,,13,3,13,3,48746,12045,True,0,0.0,50,2,97,2,0,0.0,0,0
+zs__cal_n20,B cell,welch_top50_deg,5975,,,,,13,3,13,3,20084,3256,True,0,0.0,50,33,122,33,0,0.0,0,0
+zs__cal_n20,Monocyte,welch_top50_deg,5975,,,,,13,3,13,3,19536,6373,True,0,0.0,5,4,5,4,0,0.0,0,0
+zs__cal_n20,NK cell,welch_top50_deg,5975,,,,,13,3,13,3,7787,1410,True,0,0.0,30,1,30,1,0,0.0,0,0
+zs__cal_n20,DC,welch_top50_deg,5975,,,,,12,3,13,3,284,317,True,1,0.01639344262295082,50,12,63,12,0,0.0,0,0
+zs__cal_n40,T cell,mean_cp10k,3267,0.09193644981033068,0.43768840281415844,-0.02397856644032535,0.2576452833547713,13,3,13,3,48746,11949,,,,,,,,,,,
+zs__cal_n40,T cell,pseudobulk_log1p_cpm,3267,0.07985013960339916,0.4286720169043194,-0.035186412995568406,0.2579223290907848,13,3,13,3,48746,11949,,,,,,,,,,,
+zs__cal_n40,B cell,mean_cp10k,3235,0.11301528690002181,0.3689288584704534,0.05834578704737987,0.16126457020898852,13,3,13,3,20084,3071,,,,,,,,,,,
+zs__cal_n40,B cell,pseudobulk_log1p_cpm,3235,0.04046864561207844,0.22625927605293308,0.036030221774479745,0.023790072060316813,13,3,13,3,20084,3071,,,,,,,,,,,
+zs__cal_n40,Monocyte,mean_cp10k,3323,0.11433706689032606,0.4513836128118319,0.027746799325677057,0.199554779489334,13,3,13,3,19536,6559,,,,,,,,,,,
+zs__cal_n40,Monocyte,pseudobulk_log1p_cpm,3323,0.06142884098134408,0.4902850193383437,-0.0389460777513223,0.19322048177257947,13,3,13,3,19536,6559,,,,,,,,,,,
+zs__cal_n40,NK cell,mean_cp10k,3307,-0.0046756128062820715,0.4346355607634616,-0.08673582809168189,0.16645989634913133,13,3,13,3,7787,1436,,,,,,,,,,,
+zs__cal_n40,NK cell,pseudobulk_log1p_cpm,3307,0.05280781571969329,0.2660529106416211,0.006498824202406321,0.17530434444869508,13,3,13,3,7787,1436,,,,,,,,,,,
+zs__cal_n40,T cell,welch_top50_deg,5975,,,,,13,3,13,3,48746,11949,True,0,0.0,50,0,97,0,0,0.0,0,0
+zs__cal_n40,B cell,welch_top50_deg,5975,,,,,13,3,13,3,20084,3071,True,1,0.013513513513513514,50,25,122,25,0,0.0,0,0
+zs__cal_n40,Monocyte,welch_top50_deg,5975,,,,,13,3,13,3,19536,6559,True,0,0.0,5,6,5,6,0,0.0,0,0
+zs__cal_n40,NK cell,welch_top50_deg,5975,,,,,13,3,13,3,7787,1436,True,0,0.0,30,1,30,1,0,0.0,0,0
+zs__cal_n40,DC,welch_top50_deg,5975,,,,,12,3,13,3,284,311,True,0,0.0,50,14,63,14,0,0.0,0,0

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/fold3/per_donor.csv

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+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-S102,adult_sepsis,1500,9512,150,0.33266666666666667,0.47981497056349875,0.10733333333333334,0.39318755256518084,0.452,0.0053616484440706475,0.037333333333333336,0.095878889823381,0.007333333333333333,0.0007359125315391085,0.06333333333333334,0.02502102607232969,0.9830961592374545,0.5809371755543881,0.8066741043472033,13,False,1.0,0.6933333333333334,0.9866666666666667,0.8533333333333334,45.2,0.5361648444070648,1838.0,1646.0
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+zs__cal_n5,Abd-S51,adult_sepsis,1500,6865,150,0.426,0.25273124544792425,0.2986666666666667,0.269482884195193,0.212,0.3551347414420976,0.029333333333333333,0.03670793882010197,0.020666666666666667,0.0014566642388929353,0.013333333333333334,0.08448652585579024,0.44332507890264633,0.49873446759411355,0.8484322392325029,3,False,0.9533333333333334,0.7666666666666667,0.96,0.8466666666666667,21.2,35.51347414420976,2017.0,1849.0
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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/cal_budget_20261001/fold3/summary.json

@@ -0,0 +1,1356 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-01T14:22:42.712292+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/fold3",
+  "--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/fold3/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/fold3/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/fold3/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/fold3/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"
+ ],
+ "donors": [
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+  "Abd-S83",
+  "HC12",
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+  "PIC01",
+  "Res-PS02",
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+  "Res-S125",
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+ "reference": {
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+ "contrast_definition": {
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+  "rule": "explicit --case-groups / --control-groups",
+  "group_source": "features groups",
+  "case_groups": [
+   "adult_sepsis",
+   "ped_sepsis"
+  ],
+  "control_groups": [
+   "adult_HC",
+   "adult_ICU_ctrl",
+   "ped_HC",
+   "ped_ICU_ctrl"
+  ],
+  "n_case_donors": 13,
+  "n_control_donors": 3,
+  "n_donors_in_neither": 0,
+  "case": [
+   true,
+   true,
+   true,
+   true,
+   false,
+   false,
+   false,
+   true,
+   true,
+   true,
+   true,
+   true,
+   true,
+   true,
+   true,
+   true
+  ],
+  "ctrl": [
+   false,
+   false,
+   false,
+   false,
+   true,
+   true,
+   true,
+   false,
+   false,
+   false,
+   false,
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+  ],
+  "groups": [
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_HC",
+   "adult_ICU_ctrl",
+   "ped_ICU_ctrl",
+   "ped_sepsis",
+   "ped_sepsis",
+   "ped_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis",
+   "adult_sepsis"
+  ]
+ },
+ "deg_definition": {
+  "enabled": true,
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