Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw3/README.md

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+# score_ft.py output: ye2026_sepsis, 77 scored donors
+
+Arms: trunk4x, panel_matched_1x, current_f01, scsemi.  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/trunk4x_lora_sepsis_20261013/reference/reference.npz` (sha 8c5f2d794d13), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/evaluation_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/trunk4x_lora_sepsis_20261013/reference/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 (4243 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 77 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) (11550 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 0.9806, coverage 0.9997.
+   **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
+   0.9801, coverage 0.9999.
+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 = 57) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 20); 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 4243 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/trunk4x_lora_sepsis_20261013/reference/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 57 + 20
+   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/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw3/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
+trunk4x,T cell,mean_cp10k,3226,0.15078934938906322,0.26809840085604497,0.06973189251962002,0.3255107871595414,57,20,57,20,274345,62777,,,,,,,,,,,
+trunk4x,T cell,pseudobulk_log1p_cpm,3226,0.2545116283919379,0.22243884599954644,0.2004237735225078,0.30861092863836725,57,20,57,20,274345,62777,,,,,,,,,,,
+trunk4x,B cell,mean_cp10k,3203,0.20374939504385547,0.29096817002062314,0.14661450186593356,0.23122766061588548,57,20,57,20,97853,13086,,,,,,,,,,,
+trunk4x,B cell,pseudobulk_log1p_cpm,3203,0.21730449048147094,0.24917650163982139,0.18318003318385148,0.17056999581025,57,20,57,20,97853,13086,,,,,,,,,,,
+trunk4x,Monocyte,mean_cp10k,3222,0.34840404358506477,0.539403945751839,0.18005820442303358,0.3866874722116814,57,20,57,20,103223,27168,,,,,,,,,,,
+trunk4x,Monocyte,pseudobulk_log1p_cpm,3222,0.4102681120369614,0.574496719723945,0.18587000132738302,0.4821296239155691,57,20,57,20,103223,27168,,,,,,,,,,,
+trunk4x,NK cell,mean_cp10k,3178,0.08864209128005825,0.2005187864256568,0.05677193381291009,0.16866253993568123,57,20,57,20,43846,6597,,,,,,,,,,,
+trunk4x,NK cell,pseudobulk_log1p_cpm,3178,0.10963353662340947,0.029852921773951005,0.1070960222295748,0.10730280711741892,57,20,57,20,43846,6597,,,,,,,,,,,
+trunk4x,T cell,welch_top50_deg,4243,,,,,57,20,57,20,274345,62777,True,1,0.010101010101010102,50,50,1519,225,5,0.05263157894736842,50,831
+trunk4x,B cell,welch_top50_deg,4243,,,,,57,20,57,20,97853,13086,True,0,0.0,50,23,993,23,4,0.041666666666666664,50,831
+trunk4x,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,103223,27168,True,9,0.0989010989010989,50,50,2376,1076,10,0.1111111111111111,50,831
+trunk4x,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,43846,6597,True,0,0.0,50,15,1162,15,5,0.05263157894736842,50,831
+trunk4x,DC,welch_top50_deg,4243,,,,,53,20,56,20,2961,925,True,3,0.04918032786885246,50,14,1583,14,3,0.030927835051546393,50,831
+panel_matched_1x,T cell,mean_cp10k,3226,0.30943574054583123,0.26809840085604497,0.23094446906170923,0.3900305775684669,57,20,57,20,274345,64289,,,,,,,,,,,
+panel_matched_1x,T cell,pseudobulk_log1p_cpm,3226,0.41881987285098643,0.22243884599954644,0.3764422788366945,0.31925639791740523,57,20,57,20,274345,64289,,,,,,,,,,,
+panel_matched_1x,B cell,mean_cp10k,3203,0.251918414413503,0.29096817002062314,0.1809043016683712,0.29798496916795486,57,20,57,20,97853,13786,,,,,,,,,,,
+panel_matched_1x,B cell,pseudobulk_log1p_cpm,3203,0.3299481505802535,0.24917650163982139,0.28997111261504194,0.2264081736347889,57,20,57,20,97853,13786,,,,,,,,,,,
+panel_matched_1x,Monocyte,mean_cp10k,3222,0.4162524787979721,0.539403945751839,0.2577109679911189,0.40360824503918596,57,20,57,20,103223,24036,,,,,,,,,,,
+panel_matched_1x,Monocyte,pseudobulk_log1p_cpm,3222,0.3815405280149422,0.574496719723945,0.1757020883268092,0.4392419982456369,57,20,57,20,103223,24036,,,,,,,,,,,
+panel_matched_1x,NK cell,mean_cp10k,3178,0.19615134234834744,0.2005187864256568,0.15568436236079494,0.23976805278099397,57,20,57,20,43846,7850,,,,,,,,,,,
+panel_matched_1x,NK cell,pseudobulk_log1p_cpm,3178,0.13923348257098841,0.029852921773951005,0.13654200471812122,0.13251514126176794,57,20,57,20,43846,7850,,,,,,,,,,,
+panel_matched_1x,T cell,welch_top50_deg,4243,,,,,57,20,57,20,274345,64289,True,5,0.05263157894736842,50,50,1519,131,5,0.05263157894736842,50,831
+panel_matched_1x,B cell,welch_top50_deg,4243,,,,,57,20,57,20,97853,13786,True,0,0.0,50,45,993,45,4,0.041666666666666664,50,831
+panel_matched_1x,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,103223,24036,True,10,0.1111111111111111,50,50,2376,468,10,0.1111111111111111,50,831
+panel_matched_1x,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,43846,7850,True,0,0.0,50,30,1162,30,5,0.05263157894736842,50,831
+panel_matched_1x,DC,welch_top50_deg,4243,,,,,53,20,56,20,2961,973,True,1,0.011235955056179775,50,40,1583,40,3,0.030927835051546393,50,831
+current_f01,T cell,mean_cp10k,3226,0.5100196274762202,0.26809840085604497,0.47026361615175055,0.2801913630192521,57,20,57,20,274345,76292,,,,,,,,,,,
+current_f01,T cell,pseudobulk_log1p_cpm,3226,0.5469093213461613,0.22243884599954644,0.5177993334812313,0.279904162648953,57,20,57,20,274345,76292,,,,,,,,,,,
+current_f01,B cell,mean_cp10k,3203,0.473879471916893,0.29096817002062314,0.4347353033225754,0.2414798227722394,57,20,57,20,97853,11596,,,,,,,,,,,
+current_f01,B cell,pseudobulk_log1p_cpm,3203,0.44279871577782254,0.24917650163982139,0.41905984276085556,0.17282375889717702,57,20,57,20,97853,11596,,,,,,,,,,,
+current_f01,Monocyte,mean_cp10k,3222,0.37158763923831034,0.539403945751839,0.2707021808496601,0.2836581712630723,57,20,57,20,103223,16882,,,,,,,,,,,
+current_f01,Monocyte,pseudobulk_log1p_cpm,3222,0.3592459391334746,0.574496719723945,0.25342767562024576,0.27854493026800475,57,20,57,20,103223,16882,,,,,,,,,,,
+current_f01,NK cell,mean_cp10k,3178,0.3344699752043396,0.2005187864256568,0.303315431231119,0.2236195971017804,57,20,57,20,43846,5864,,,,,,,,,,,
+current_f01,NK cell,pseudobulk_log1p_cpm,3178,0.23611377347036167,0.029852921773951005,0.23442440444249937,0.1552120528164474,57,20,57,20,43846,5864,,,,,,,,,,,
+current_f01,T cell,welch_top50_deg,4243,,,,,57,20,57,20,274345,76292,True,11,0.12359550561797752,50,50,1519,303,5,0.05263157894736842,50,831
+current_f01,B cell,welch_top50_deg,4243,,,,,57,20,57,20,97853,11596,True,3,0.030927835051546393,50,50,993,115,4,0.041666666666666664,50,831
+current_f01,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,103223,16882,True,3,0.030927835051546393,50,50,2376,887,10,0.1111111111111111,50,831
+current_f01,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,43846,5864,True,1,0.010101010101010102,50,50,1162,142,5,0.05263157894736842,50,831
+current_f01,DC,welch_top50_deg,4243,,,,,53,20,50,20,2961,657,True,1,0.010101010101010102,50,50,1583,197,3,0.030927835051546393,50,831
+scsemi,T cell,mean_cp10k,3226,0.4626659524696519,0.26809840085604497,0.3930954658161709,0.5143427485792619,57,20,57,20,274345,69437,,,,,,,,,,,
+scsemi,T cell,pseudobulk_log1p_cpm,3226,0.3173192597036992,0.22243884599954644,0.28277908270524604,0.2165339991657517,57,20,57,20,274345,69437,,,,,,,,,,,
+scsemi,B cell,mean_cp10k,3203,0.5563349083147909,0.29096817002062314,0.5060861362950518,0.35866988059441973,57,20,57,19,97853,5722,,,,,,,,,,,
+scsemi,B cell,pseudobulk_log1p_cpm,3203,0.23553459855036532,0.24917650163982139,0.2319079408495956,0.0448161723058942,57,20,57,19,97853,5722,,,,,,,,,,,
+scsemi,Monocyte,mean_cp10k,3222,0.501836497429718,0.539403945751839,0.3975774402935045,0.34865178824032733,57,20,56,19,103223,21920,,,,,,,,,,,
+scsemi,Monocyte,pseudobulk_log1p_cpm,3222,0.06610244593317491,0.574496719723945,-0.03199022253036384,0.1600515899344449,57,20,56,19,103223,21920,,,,,,,,,,,
+scsemi,NK cell,mean_cp10k,3178,0.5847926251582312,0.2005187864256568,0.5609979853904774,0.3699415538995843,57,20,56,19,43846,6190,,,,,,,,,,,
+scsemi,NK cell,pseudobulk_log1p_cpm,3178,0.24210290946468555,0.029852921773951005,0.2407683984518882,0.18006295857964286,57,20,56,19,43846,6190,,,,,,,,,,,
+scsemi,T cell,welch_top50_deg,4243,,,,,57,20,57,20,274345,69437,True,7,0.07526881720430108,50,50,1519,68,5,0.05263157894736842,50,831
+scsemi,B cell,welch_top50_deg,4243,,,,,57,20,57,19,97853,5722,True,2,0.02040816326530612,50,50,993,97,4,0.041666666666666664,50,831
+scsemi,Monocyte,welch_top50_deg,4243,,,,,57,20,56,19,103223,21920,True,3,0.030927835051546393,50,50,2376,189,10,0.1111111111111111,50,831
+scsemi,NK cell,welch_top50_deg,4243,,,,,57,20,56,19,43846,6190,True,0,0.0,50,50,1162,135,5,0.05263157894736842,50,831
+scsemi,DC,welch_top50_deg,4243,,,,,53,20,56,19,2961,2853,True,4,0.041666666666666664,50,50,1583,126,3,0.030927835051546393,50,831

paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw3/summary.json

@@ -0,0 +1,1612 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-13T14:04:54.327801+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",
+  "--reference",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/reference",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/scores/draw3",
+  "--threads",
+  "4",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/scores/draw3/test_samples.txt",
+  "--case-groups",
+  "adult_sepsis,ped_sepsis",
+  "--control-groups",
+  "adult_HC,adult_ICU_ctrl,ped_HC,ped_ICU_ctrl",
+  "--arm",
+  "trunk4x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw3/trunk4x.npz",
+  "--condition",
+  "trunk4x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw3/ye2026_sepsis/test_raw.npz",
+  "--arm",
+  "panel_matched_1x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw3/panel_matched_1x.npz",
+  "--condition",
+  "panel_matched_1x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw3/ye2026_sepsis/test_raw.npz",
+  "--arm",
+  "current_f01=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw3/current_f01.npz",
+  "--condition",
+  "current_f01=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw3/ye2026_sepsis/test_raw.npz",
+  "--arm",
+  "scsemi=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw3/scsemi.npz",
+  "--condition",
+  "scsemi=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw3/ye2026_sepsis/test_raw.npz"
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