Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/locoL_fullsat_s80000_K1000/summary.json

@@ -0,0 +1,531 @@
+{
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+ "seconds": 10.6
+}

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/locoL_lora16_K1000/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: giroux2022, 24 scored donors
+
+Arms: locoL_lora16_K1000.  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/giroux2022/reference.npz` (sha dd0152f7bde4), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022;
+  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/giroux2022/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 (5563 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 24 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) (3600 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 0.9981, 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
+   0.9981, 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 ['severe'] (n = 11) vs control groups ['mild'] (n = 13); 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 5563 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/giroux2022/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 11 + 13
+   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/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/locoL_lora16_K1000/contrast.csv

@@ -0,0 +1,14 @@
+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
+locoL_lora16_K1000,T cell,mean_cp10k,2908,0.5797892649442452,0.6665223768938867,0.38961440945212583,0.49000006209671165,11,13,11,13,145034,26683,,,,,,,,,,,
+locoL_lora16_K1000,T cell,pseudobulk_log1p_cpm,2908,0.568003175453016,0.829771536027708,0.25738518377547653,0.538676376274936,11,13,11,13,145034,26683,,,,,,,,,,,
+locoL_lora16_K1000,B cell,mean_cp10k,3053,0.27118955580447046,0.5741068019580355,0.14139339006416987,0.2787053105483543,11,13,11,13,17383,2635,,,,,,,,,,,
+locoL_lora16_K1000,B cell,pseudobulk_log1p_cpm,3053,0.24341500093761637,0.7295906969005199,0.051840739234444155,0.28708471157095133,11,13,11,13,17383,2635,,,,,,,,,,,
+locoL_lora16_K1000,Monocyte,mean_cp10k,3056,0.11978212234528374,0.6998157471451955,0.02775464834383952,0.14312401406666367,11,13,11,13,47311,3160,,,,,,,,,,,
+locoL_lora16_K1000,Monocyte,pseudobulk_log1p_cpm,3056,0.088238077012673,0.8460762046394,0.003862823383443008,0.10186986046662946,11,13,11,13,47311,3160,,,,,,,,,,,
+locoL_lora16_K1000,NK cell,mean_cp10k,2955,0.36194442993560316,0.6119759414285395,0.25976551824972943,0.2680140081952137,11,13,11,13,29061,2659,,,,,,,,,,,
+locoL_lora16_K1000,NK cell,pseudobulk_log1p_cpm,2955,0.2321566375144097,0.7735167267842559,0.11489956916876207,0.20804914534827568,11,13,11,13,29061,2659,,,,,,,,,,,
+locoL_lora16_K1000,T cell,welch_top50_deg,5563,,,,,11,13,11,13,145034,26683,True,5,0.09090909090909091,50,10,306,10,10,0.1724137931034483,18,18
+locoL_lora16_K1000,B cell,welch_top50_deg,5563,,,,,11,13,11,13,17383,2635,True,4,0.4444444444444444,9,4,9,4,7,0.35,18,18
+locoL_lora16_K1000,Monocyte,welch_top50_deg,5563,,,,,11,13,11,13,47311,3160,True,2,0.2,7,5,7,5,7,0.3888888888888889,18,18
+locoL_lora16_K1000,NK cell,welch_top50_deg,5563,,,,,11,13,11,13,29061,2659,True,2,0.04,50,2,83,2,10,0.1724137931034483,18,18
+locoL_lora16_K1000,DC,welch_top50_deg,5563,,,,,11,13,11,13,4328,698,True,1,0.14285714285714285,7,1,7,1,5,0.25,18,18

paper-releases/pbisc-diffusion/updates/20261003T103040Z-crosscohort-k1000-saturation/diffusion/dataset/server_1/model8/plans/paired_crosscohort_20261002/scores/giroux2022/locoL_lora16_K1000/per_donor.csv

@@ -0,0 +1,25 @@
+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
+locoL_lora16_K1000,450905_d0,mild,1500,5914,150,0.8273333333333334,0.30943523841731485,0.03666666666666667,0.036185322962461956,0.054,0.4293202570172472,0.03933333333333333,0.12089956036523504,0.034,0.009976327358809604,0.008666666666666666,0.09418329387893135,1.0848062225228274,0.6266504923198378,0.8526690429068206,8,False,1.0,0.6133333333333333,0.98,0.6733333333333333,5.4,42.93202570172472,563.5,574.5
+locoL_lora16_K1000,450905_d14,mild,1500,7407,150,0.6813333333333333,0.4422843256379101,0.04733333333333333,0.02929661131362225,0.116,0.27420008100445525,0.126,0.15782368030241664,0.026,0.02983664101525584,0.0033333333333333335,0.06655866072633995,0.5141714594302687,0.36155260046011806,0.8600678663480328,10,False,0.9866666666666667,0.8266666666666667,0.9733333333333334,0.9066666666666666,11.600000000000001,27.420008100445525,598.0,558.0
+locoL_lora16_K1000,0B943B_d0,mild,1500,11344,150,0.76,0.6220028208744711,0.094,0.08312764456981664,0.07266666666666667,0.12588152327221439,0.05,0.11865303244005641,0.020666666666666667,0.023977433004231313,0.0026666666666666666,0.026357545839210156,0.2977390691114245,0.1791278384873501,0.8559640041485848,10,False,1.0,0.8266666666666667,0.96,0.9133333333333333,7.2666666666666675,12.588152327221438,538.0,539.0
+locoL_lora16_K1000,0B943B_d14,mild,1500,15650,150,0.5646666666666667,0.5652396166134185,0.102,0.11175718849840256,0.216,0.17086261980830672,0.086,0.09789137380191694,0.014666666666666666,0.009968051118210863,0.016666666666666666,0.04428115015974441,0.09967199148029816,0.10063602424453227,0.8826966574856798,1,True,0.9866666666666667,0.7933333333333333,0.9733333333333334,0.8866666666666667,21.6,17.08626198083067,683.0,461.0
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+locoL_lora16_K1000,7085CA_d7,mild,1500,9515,150,0.5113333333333333,0.521492380451918,0.09933333333333333,0.0722017866526537,0.162,0.10110352075669994,0.17733333333333334,0.2725170782974251,0.043333333333333335,0.019863373620599055,0.006666666666666667,0.012821860220704152,0.22299597127342793,0.3632282731474772,0.838854763730693,7,False,0.9733333333333334,0.8933333333333333,0.98,0.8733333333333333,16.2,10.110352075669994,427.0,502.0
+locoL_lora16_K1000,7085CA_d14,mild,1500,11887,150,0.514,0.472533019264743,0.08866666666666667,0.07133843694792631,0.176,0.16210986792294102,0.17933333333333334,0.24270211155043325,0.037333333333333336,0.018591738874400604,0.004666666666666667,0.032724825439555814,0.18285387397997813,0.2993283707516418,0.8441952598792837,5,False,0.9933333333333333,0.7333333333333333,0.9533333333333334,0.78,17.599999999999998,16.2109867922941,426.0,536.0
+locoL_lora16_K1000,2AD75E_d0,mild,1500,7693,150,0.772,0.6034056934875861,0.08533333333333333,0.06408423241908229,0.051333333333333335,0.14064734173924348,0.068,0.15949564539191471,0.017333333333333333,0.025347718705316522,0.006,0.007019368256856883,0.3796868148533299,0.20681142084856263,0.8513356984574902,4,False,1.0,0.8466666666666667,1.0,0.8733333333333333,5.133333333333334,14.064734173924348,509.0,532.0
+locoL_lora16_K1000,2AD75E_d7,mild,1500,10499,150,0.5533333333333333,0.5808172206876845,0.22133333333333333,0.14372797409277074,0.142,0.10277169254214687,0.06,0.14715687208305553,0.018,0.013715591961139155,0.005333333333333333,0.011810648633203162,0.24223614947455308,0.27874882058987843,0.8434725946753222,7,False,0.9866666666666667,0.8733333333333333,0.98,0.8466666666666667,14.2,10.277169254214687,475.5,443.0
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+locoL_lora16_K1000,B85D75_d7,severe,1500,12328,150,0.908,0.6435756002595717,0.044,0.06448734587929916,0.018,0.13741077222582737,0.026,0.04201817001946788,0.0026666666666666666,0.027741726151849447,0.0013333333333333333,0.08476638546398442,0.5288487994808566,0.28301793033829503,0.8423718558547197,1,True,0.9933333333333333,0.8466666666666667,0.9733333333333334,0.8,1.7999999999999998,13.741077222582737,541.0,363.0
+locoL_lora16_K1000,180E1A_d0,severe,1500,7157,150,0.8073333333333333,0.7019700992035769,0.058,0.04135811094033813,0.03933333333333333,0.13175911694844208,0.07,0.0655302501047925,0.02266666666666667,0.013273718038284197,0.0026666666666666666,0.04610870476456616,0.2717356434260165,0.31372800769589615,0.8373927898219935,9,False,1.0,0.7533333333333333,1.0,0.7866666666666666,3.933333333333333,13.175911694844208,446.0,433.0
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