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/draw1/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 0.9825, 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
+   0.9793, coverage 0.9997.
+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/draw1/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,3218,0.006159042759353324,0.20765192025922913,-0.07502286147514778,0.3594551941633219,57,20,57,20,283471,52247,,,,,,,,,,,
+trunk4x,T cell,pseudobulk_log1p_cpm,3218,0.03012255633916154,0.14131653144015197,-0.02101312236446618,0.35099439990579345,57,20,57,20,283471,52247,,,,,,,,,,,
+trunk4x,B cell,mean_cp10k,3209,0.1593764664103716,0.23485845967110092,0.10569579117574338,0.2556960236733568,57,20,57,20,95857,13343,,,,,,,,,,,
+trunk4x,B cell,pseudobulk_log1p_cpm,3209,0.098694773839844,0.19476906775520855,0.060086099259202046,0.2109449075307875,57,20,57,20,95857,13343,,,,,,,,,,,
+trunk4x,Monocyte,mean_cp10k,3210,0.3244784506341268,0.5252118359841644,0.1512682208068213,0.3923676907413618,57,20,57,20,101105,37936,,,,,,,,,,,
+trunk4x,Monocyte,pseudobulk_log1p_cpm,3210,0.3643015201395356,0.5559408683182179,0.12526339769443728,0.49226310780191607,57,20,57,20,101105,37936,,,,,,,,,,,
+trunk4x,NK cell,mean_cp10k,3166,0.04917211152805471,0.1386632578207905,0.016568750369112147,0.23973121288034188,57,20,57,20,45427,5050,,,,,,,,,,,
+trunk4x,NK cell,pseudobulk_log1p_cpm,3166,0.055045352493944565,-0.01648978393037816,0.057250737287631935,0.11158935300421387,57,20,57,20,45427,5050,,,,,,,,,,,
+trunk4x,T cell,welch_top50_deg,4243,,,,,57,20,57,20,283471,52247,True,12,0.13636363636363635,50,50,1523,223,6,0.06382978723404255,50,893
+trunk4x,B cell,welch_top50_deg,4243,,,,,57,20,57,20,95857,13343,True,0,0.0,50,33,1071,33,4,0.041666666666666664,50,893
+trunk4x,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,101105,37936,True,6,0.06382978723404255,50,50,2271,1386,9,0.0989010989010989,50,893
+trunk4x,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,45427,5050,True,0,0.0,50,50,1148,109,7,0.07526881720430108,50,893
+trunk4x,DC,welch_top50_deg,4243,,,,,53,20,56,20,3055,908,True,0,0.0,50,21,1702,21,2,0.02040816326530612,50,893
+panel_matched_1x,T cell,mean_cp10k,3218,0.14817431372490072,0.20765192025922913,0.06764631064899795,0.4251357253617505,57,20,57,20,283471,51097,,,,,,,,,,,
+panel_matched_1x,T cell,pseudobulk_log1p_cpm,3218,0.15162094797539924,0.14131653144015197,0.1020862367636381,0.42586348016814435,57,20,57,20,283471,51097,,,,,,,,,,,
+panel_matched_1x,B cell,mean_cp10k,3209,0.1973473664682647,0.23485845967110092,0.13105679931073738,0.32784405647133635,57,20,57,20,95857,15255,,,,,,,,,,,
+panel_matched_1x,B cell,pseudobulk_log1p_cpm,3209,0.12396125401542592,0.19476906775520855,0.06489584097501308,0.3276838161419165,57,20,57,20,95857,15255,,,,,,,,,,,
+panel_matched_1x,Monocyte,mean_cp10k,3210,0.3384746681771963,0.5252118359841644,0.16494829118497056,0.39944417573636054,57,20,57,20,101105,37533,,,,,,,,,,,
+panel_matched_1x,Monocyte,pseudobulk_log1p_cpm,3210,0.3236538258671369,0.5559408683182179,0.08025552546235516,0.47668882195784135,57,20,57,20,101105,37533,,,,,,,,,,,
+panel_matched_1x,NK cell,mean_cp10k,3166,0.08874839632329426,0.1386632578207905,0.04701600042252582,0.32213774305605786,57,20,57,20,45427,4939,,,,,,,,,,,
+panel_matched_1x,NK cell,pseudobulk_log1p_cpm,3166,-0.0012019077077977515,-0.01648978393037816,0.002089797146258901,0.19711745353994392,57,20,57,20,45427,4939,,,,,,,,,,,
+panel_matched_1x,T cell,welch_top50_deg,4243,,,,,57,20,57,20,283471,51097,True,8,0.08695652173913043,50,50,1523,291,6,0.06382978723404255,50,893
+panel_matched_1x,B cell,welch_top50_deg,4243,,,,,57,20,57,20,95857,15255,True,0,0.0,50,50,1071,75,4,0.041666666666666664,50,893
+panel_matched_1x,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,101105,37533,True,4,0.041666666666666664,50,50,2271,385,9,0.0989010989010989,50,893
+panel_matched_1x,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,45427,4939,True,5,0.05263157894736842,50,50,1148,260,7,0.07526881720430108,50,893
+panel_matched_1x,DC,welch_top50_deg,4243,,,,,53,20,57,20,3055,1315,True,2,0.021505376344086023,50,45,1702,45,2,0.02040816326530612,50,893
+current_f01,T cell,mean_cp10k,3218,-0.022070266100938655,0.20765192025922913,-0.1196453960257496,0.4082340071583913,57,20,57,20,283471,51849,,,,,,,,,,,
+current_f01,T cell,pseudobulk_log1p_cpm,3218,0.006670534996294207,0.14131653144015197,-0.06240357651999685,0.43980859856327076,57,20,57,20,283471,51849,,,,,,,,,,,
+current_f01,B cell,mean_cp10k,3209,0.09261334129898775,0.23485845967110092,0.021693061420864492,0.3089460559064314,57,20,57,20,95857,11495,,,,,,,,,,,
+current_f01,B cell,pseudobulk_log1p_cpm,3209,0.10606345918676852,0.19476906775520855,0.048328276661671964,0.31344565851340817,57,20,57,20,95857,11495,,,,,,,,,,,
+current_f01,Monocyte,mean_cp10k,3210,0.2638464890999806,0.5252118359841644,0.08378574651213727,0.3766035632363429,57,20,57,20,101105,39805,,,,,,,,,,,
+current_f01,Monocyte,pseudobulk_log1p_cpm,3210,0.26765823751068585,0.5559408683182179,0.003092900811411204,0.4773874934948241,57,20,57,20,101105,39805,,,,,,,,,,,
+current_f01,NK cell,mean_cp10k,3166,0.026808126720655155,0.1386632578207905,-0.024720646125939175,0.3581749228746607,57,20,56,20,45427,4258,,,,,,,,,,,
+current_f01,NK cell,pseudobulk_log1p_cpm,3166,-0.00010548382482434815,-0.01648978393037816,0.003392288043593683,0.20760793015088,57,20,56,20,45427,4258,,,,,,,,,,,
+current_f01,T cell,welch_top50_deg,4243,,,,,57,20,57,20,283471,51849,True,14,0.16279069767441862,50,50,1523,475,6,0.06382978723404255,50,893
+current_f01,B cell,welch_top50_deg,4243,,,,,57,20,57,20,95857,11495,True,1,0.010101010101010102,50,50,1071,218,4,0.041666666666666664,50,893
+current_f01,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,101105,39805,True,3,0.030927835051546393,50,50,2271,586,9,0.0989010989010989,50,893
+current_f01,NK cell,welch_top50_deg,4243,,,,,57,20,56,20,45427,4258,True,1,0.010101010101010102,50,50,1148,351,7,0.07526881720430108,50,893
+current_f01,DC,welch_top50_deg,4243,,,,,53,20,57,20,3055,1010,True,3,0.030927835051546393,50,50,1702,95,2,0.02040816326530612,50,893
+scsemi,T cell,mean_cp10k,3218,0.2369147632911281,0.20765192025922913,0.1507451130910294,0.5459785636382704,57,20,57,20,283471,49461,,,,,,,,,,,
+scsemi,T cell,pseudobulk_log1p_cpm,3218,0.05607034572019391,0.14131653144015197,0.013095759611093608,0.309537191026519,57,20,57,20,283471,49461,,,,,,,,,,,
+scsemi,B cell,mean_cp10k,3209,0.3381241197150194,0.23485845967110092,0.2625903888451574,0.49598651411214806,57,20,57,20,95857,8821,,,,,,,,,,,
+scsemi,B cell,pseudobulk_log1p_cpm,3209,0.062027533145725844,0.19476906775520855,0.014181661829570487,0.24930379857010754,57,20,57,20,95857,8821,,,,,,,,,,,
+scsemi,Monocyte,mean_cp10k,3210,0.47185509970659145,0.5252118359841644,0.23111661339182352,0.5983838630821534,57,20,57,20,101105,35209,,,,,,,,,,,
+scsemi,Monocyte,pseudobulk_log1p_cpm,3210,0.14830331247652784,0.5559408683182179,-0.06217461043082742,0.35371253340673753,57,20,57,20,101105,35209,,,,,,,,,,,
+scsemi,NK cell,mean_cp10k,3166,0.2803420971440841,0.1386632578207905,0.24835826301526648,0.591385181542937,57,20,55,18,45427,6380,,,,,,,,,,,
+scsemi,NK cell,pseudobulk_log1p_cpm,3166,0.007783991601142461,-0.01648978393037816,0.012715199010454458,0.2702524218643844,57,20,55,18,45427,6380,,,,,,,,,,,
+scsemi,T cell,welch_top50_deg,4243,,,,,57,20,57,20,283471,49461,True,2,0.022727272727272728,50,40,1523,40,6,0.06382978723404255,50,893
+scsemi,B cell,welch_top50_deg,4243,,,,,57,20,57,20,95857,8821,True,1,0.013888888888888888,50,23,1071,23,4,0.041666666666666664,50,893
+scsemi,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,101105,35209,True,0,0.0,50,50,2271,106,9,0.0989010989010989,50,893
+scsemi,NK cell,welch_top50_deg,4243,,,,,57,20,55,18,45427,6380,True,0,0.0,50,37,1148,37,7,0.07526881720430108,50,893
+scsemi,DC,welch_top50_deg,4243,,,,,53,20,57,20,3055,2363,True,0,0.0,50,45,1702,45,2,0.02040816326530612,50,893

paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw1/summary.json

@@ -0,0 +1,1612 @@
+{
+ "schema": "paired_ft_scores.v1",
+ "dataset": "ye2026_sepsis",
+ "written_utc": "2026-10-13T13:15:07.301985+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/draw1",
+  "--threads",
+  "4",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/scores/draw1/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/draw1/trunk4x.npz",
+  "--condition",
+  "trunk4x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw1/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/draw1/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/draw1/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/draw1/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/draw1/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/draw1/scsemi.npz",
+  "--condition",
+  "scsemi=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw1/ye2026_sepsis/test_raw.npz"
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