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paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw4_scsemi5nt/README.md ADDED
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+ # score_ft.py output: immicro, 102 scored donors
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+
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+ Arms: scsemi5nt. Every arm is scored on the same donors against the same observed reference.
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+
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+ ## Inputs and fixed contracts
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+ - Observed reference: `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/immicro/reference.npz` (sha c5e2547be3ec), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro;
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+ 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/utility_studies_final6k_20260925/immicro/labels/observed_immicro/labels.parquet.
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+ - Generated cells: `generated_counts` read out once with Poisson(seed 20260916) over the **whole NPZ in row order**
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+ (`poisson_readout.poisson_integerise`), then the scored donors' rows. These integer counts are used for labels, embedding,
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+ depth and the contrast. Pseudobulk uses the summed `generated_counts` (rates), as `score_generated.py`.
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+ - 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 =
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+ the 1,006-gene annotation list minus genes the dataset's sc assay did not measure in every sample (1004 genes).
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+ - Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5508 genes).
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+
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+ ## Definitions
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+ 1. **Composition**: fraction of each donor's cells per six-type label (all generated cells; all observed QC cells).
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+ `comp_l1` = sum over the six labels of |generated - observed| (immicro_common.l1). `comp_l1_obs_loo_null` = L1 of the
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+ donor's observed composition to the mean of the *other scored donors'* observed compositions. `composition_top1` =
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+ fraction of donors whose generated composition is closest (L1) to their own observed composition.
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+ 2. **Pseudobulk**: `pb_r` = Pearson r of CPM-log1p(summed generated_counts) vs CPM-log1p(observed pseudobulk) on the scoring
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+ genes (evaluate_realbulk.cpm_log1p / paired_r). `pb_top1` = the donor's generated pseudobulk is closest (Pearson) to its own
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+ observed pseudobulk among the 102 scored donors (immicro_common.retrieval = evaluate_realbulk.top1 arithmetic).
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+ 3. **Precision / coverage / density** (stage_b_embed.py functions): per-cell CP10K over the scoring genes + log1p; PCA-50
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+ (randomized, random_state 20260925) and k = 10 real-real k-NN balls fitted on **R1 of the scored donors only**.
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+ Reference rule (independent of fold and arm): per donor, cells in canonical order (library_id+barcode), one
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+ permutation from `default_rng([20260925, uint64(sha256(donor)[:8])])`; R1 = first n, R2 = next n,
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+ n = min(150, n_cells // 2) (15300 R1 cells here). Generated query: per donor n cells (the donor's R1 size),
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+ without replacement, `default_rng([20260926, same key])`. Precision = fraction of generated cells inside any R1 ball
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+ (Kynkaanniemi 2019); coverage = fraction of R1 cells whose ball holds a generated cell; density = Naeem 2020.
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+ Per-donor precision = that donor's generated cells; per-donor coverage = that donor's R1 cells (the pooled values are
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+ their n-weighted means; plain means when every donor has 150 reference cells). R2 (held-out real cells of the same donors) is the
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+ perfect-generator control: precision 0.9650, coverage 0.9999.
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+ **Depth matched**: stage_b_embed depth matching run pairwise (R1 vs this arm): per donor and within-donor rank of detected
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+ genes, both cells are thinned by UMI sampling without replacement to the smaller detected-gene count; PCA kept from
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+ un-thinned R1, radii recomputed on thinned R1; fresh rng(20260929) per arm. R2 control: precision
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+ 0.9629, coverage 0.9997.
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+ 4. **Monocyte %** = 100 x Monocyte fraction (all cells) per donor; **median UMI per cell** = median over cells of the
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+ Poisson-read (generated) / observed counts summed over the scoring genes (per donor, and pooled).
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+ 5. **Group contrast** (T, B, Monocyte, NK): not computed (dataset immicro has no group contrast (use --groups-csv / --case-groups)).
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+ Genes per type = scoring genes detected (count > 0) in >= 1 % of the scored donors' observed
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+ cells of that type. Primary statistic `mean_cp10k`: per-cell CP10K over the scoring genes; per donor the mean over its
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+ cells of the type (donors with >= 1 such cell); group mean = mean over the group's donors (donor-balanced);
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+ logFC = log2(case mean + 1) - log2(control mean + 1). Secondary `pseudobulk_log1p_cpm`: the SLE benchmark evaluator's
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+ statistic (donor x type summed counts, log1p CPM over the scoring genes, mean case - mean control;
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+ evaluate_generated_vs_truth.group_log_fold_change). Both are computed on the generated cells (their frozen labels) and on
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+ the observed cells; `r_generated` = Pearson r of the two logFC vectors. Broadcast baseline `r_broadcast`: the same
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+ observed vector against the logFC of the input bulk (condition NPZ `bulk_counts` of the arm, scoring genes, per-donor
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+ CP10K resp. log1p CPM, group means). `partial_r_generated_given_broadcast`: r of the OLS residuals of observed and
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+ generated logFC on the bulk logFC (residual_specificity.py).
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+ 6. **Top-50 DEG overlap** (contrast.csv rows `statistic = welch_top50_deg`; summary `deg_top50`): not computed (contrast off).
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+
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+ ## Files
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+ - `per_donor.csv`: one row per arm x donor (all per-donor quantities above).
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+ - `summary.json`: per arm the donor means / pooled values, provenance (NPZ sha, receipt check, generation metadata,
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+ Poisson nnz of the whole NPZ, annotation contract), and the reference block (PCA, R2 control).
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+ - `contrast.csv`: per arm x cell type x statistic (`mean_cp10k`, `pseudobulk_log1p_cpm`: logFC r; `welch_top50_deg`:
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+ top-50 DEG overlap, T / B / Monocyte / NK / DC; columns of the other kind are empty).
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+ - `arms/<arm>/labels/` (predict_scanvi.py labels + manifest), `arms/<arm>/generated_cells.parquet` (NPZ row, donor, label,
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+ UMI on the scoring genes, P/C subsample flag), `arms/<arm>/deg_top50_lists.csv` (observed, generated and broadcast
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+ top-50 gene lists, Ensembl ids, rank order).
paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw4_scsemi5nt/per_donor.csv ADDED
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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
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+ scsemi5nt,HS7,DB1,1500,4271,150,0.7806666666666666,0.7035822992273473,0.042,0.03839850152189183,0.09,0.13135097166939827,0.048,0.09740107703114025,0.034666666666666665,0.02130648560056193,0.004666666666666667,0.0079606649496605,0.1880920939670646,0.1449175671324591,0.8273954381166201,13,False,0.8733333333333333,0.28,0.96,0.5733333333333334,9.0,13.135097166939827,1922.0,1748.0
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+ scsemi5nt,HS8,DB1,1500,3652,150,0.588,0.6251369112814896,0.102,0.06489594742606791,0.142,0.15553121577217963,0.10466666666666667,0.11829134720700986,0.05733333333333333,0.028477546549835708,0.006,0.007667031763417305,0.13191967871485952,0.07004174855950035,0.8962524905292149,5,False,0.72,0.31333333333333335,0.9466666666666667,0.64,14.2,15.553121577217963,1922.0,1817.0
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+ scsemi5nt,HS9,DB3,1500,3942,150,0.6893333333333334,0.6958396752917301,0.042,0.028665651953323185,0.16666666666666666,0.18670725520040587,0.062,0.049974632166412986,0.032,0.03373921867072552,0.008,0.0050735667174023336,0.056572298325722956,0.17673539661541704,0.8579421219178456,1,True,0.7,0.16,0.9333333333333333,0.66,16.666666666666664,18.670725520040587,1917.0,2389.0
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+ scsemi5nt,HS11,DB6,1500,2886,150,0.6386666666666667,0.6056826056826057,0.13933333333333334,0.061330561330561334,0.148,0.24358974358974358,0.05333333333333334,0.04054054054054054,0.018,0.04573804573804574,0.0026666666666666666,0.0031185031185031187,0.24755925155925163,0.20344570271112788,0.8481315584625155,28,False,0.7466666666666667,0.20666666666666667,0.9333333333333333,0.5666666666666667,14.799999999999999,24.358974358974358,1921.0,2216.5
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+ scsemi5nt,HS12,DB2,1500,5035,150,0.66,0.6798411122144985,0.126,0.06971201588877855,0.132,0.1529294935451837,0.05533333333333333,0.064746772591857,0.024666666666666667,0.025223435948361468,0.002,0.007547169811320755,0.11257596822244284,0.08917300293412955,0.8669725969996094,9,False,0.7066666666666667,0.30666666666666664,0.9066666666666666,0.6133333333333333,13.200000000000001,15.29294935451837,1921.0,1550.0
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+ scsemi5nt,HS15,DB4,1500,4592,150,0.5006666666666667,0.46972996515679444,0.092,0.04638501742160279,0.29333333333333333,0.37282229965156793,0.07666666666666666,0.08776132404181185,0.034,0.0195993031358885,0.0033333333333333335,0.0037020905923344947,0.1819047619047619,0.4180291532419459,0.816017048528953,33,False,0.8666666666666667,0.13333333333333333,0.92,0.38666666666666666,29.333333333333332,37.282229965156795,1920.0,2302.0
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+ scsemi5nt,HS16,DB4,1500,4842,150,0.6253333333333333,0.5846757538207352,0.136,0.08736059479553904,0.14666666666666667,0.23048327137546468,0.064,0.07166460140437836,0.022,0.020859149111937217,0.006,0.004956629491945477,0.18296241222635273,0.15710997250030045,0.8540337689990666,75,False,0.68,0.10666666666666667,0.8133333333333334,0.5866666666666667,14.666666666666666,23.04832713754647,1919.0,2066.0
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+ scsemi5nt,HS20,DB7,1500,1366,150,0.824,0.7657393850658858,0.02666666666666667,0.09882869692532943,0.04466666666666667,0.08345534407027819,0.022,0.029282576866764276,0.06933333333333333,0.015373352855051245,0.013333333333333334,0.007320644216691069,0.23646656905807706,0.30694846087839417,0.7433882973396824,77,False,0.8533333333333334,0.06666666666666667,0.9933333333333333,0.22,4.466666666666667,8.345534407027818,1918.0,2271.0
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+ scsemi5nt,HS21,DB1,1500,4748,150,0.7653333333333333,0.5945661331086773,0.042,0.06887110362257792,0.106,0.1941870261162595,0.054,0.11141533277169334,0.02666666666666667,0.02401010951979781,0.006,0.0069502948609941025,0.3468475147430497,0.10290383023481701,0.8582270853764098,16,False,0.7533333333333333,0.36,0.9133333333333333,0.66,10.6,19.41870261162595,1920.0,1849.5
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+ scsemi5nt,HS110,DB1,1500,4176,150,0.6953333333333334,0.5744731800766284,0.052,0.1285919540229885,0.15,0.15158045977011494,0.06533333333333333,0.10871647509578544,0.036,0.03089080459770115,0.0013333333333333333,0.005747126436781609,0.25193869731800766,0.16155552915613075,0.8302449828960476,37,False,0.5866666666666667,0.38666666666666666,0.84,0.62,15.0,15.158045977011495,1921.0,1704.5
103
+ scsemi5nt,HS111,DB1,1500,4787,150,0.7953333333333333,0.6772508878211824,0.043333333333333335,0.09024441194902862,0.06,0.12784625026112387,0.021333333333333333,0.077501566743263,0.06866666666666667,0.02360559849592647,0.011333333333333334,0.003551284729475663,0.34185112457349764,0.10901041288948196,0.7758321883021687,16,False,0.9733333333333334,0.26,1.0,0.6,6.0,12.784625026112387,1918.0,1865.0
paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw4_scsemi5nt/summary.json ADDED
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+ {
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+ "schema": "paired_ft_scores.v1",
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+ "dataset": "immicro",
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+ "written_utc": "2026-10-03T16:13:23.500430+00:00",
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+ "command": [
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+ "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
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+ "--dataset",
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+ "immicro",
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+ "--out",
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+ "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw4_scsemi5nt",
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+ "--threads",
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+ "6",
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+ "--donors",
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+ "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/fewshot_n5/draw4/test_samples.txt",
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+ "--arm",
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+ "scsemi5nt=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/fewshot_n5/draw4/scsemi5nt__raw.npz"
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+ ],
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+ "HS86",
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+ "HS104",
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+ "HS105",
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+ "HS106",
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+ "HS107",
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+ "HS108",
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+ "HS109",
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+ "HS110",
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+ "HS111"
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+ ],
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+ "n_donors": 102,
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+ "reference": {
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+ "cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/immicro",
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+ "reference_sha256": "c5e2547be3ec3b12cb0c0b404a49e14a046ae59d40be70427f98f083a76bce17",
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+ "features_sha256": "735255066ce7ebbac13a1fdfd058e26f31edd60314cc02fd589b0a292dbbc838",
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+ "n_r1_cells": 15300,
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+ "pca": {
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+ "n_components": 50,
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+ "explained_variance_ratio_sum": 0.28678637742996216,
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+ "n_fit_cells": 15300
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+ },
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+ },
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+ "observed_l1_loo_null_mean": 0.15939575114092489
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+ },
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+ "contrast_definition": {
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+ "enabled": false,
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+ "reason": "dataset immicro has no group contrast (use --groups-csv / --case-groups)"
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+ },
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+ "deg_definition": {
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+ "enabled": false,
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+ "reason": "contrast off"
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+ },
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+ "headline": {
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+ "mono_pct_gen": 14.088235294117649,
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+ "mono_pct_obs": 16.58568855464869,
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+ "median_umi_gen": 1920.0,
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+ "median_umi_obs": 1963.0,
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+ "seconds": 34.9
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+ }
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+ },
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+ "per_arm": {
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+ "scsemi5nt": {
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+ "arm": "scsemi5nt",
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+ "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/immicro/fewshot_n5/draw4/scsemi5nt__raw.npz",
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+ "npz_sha256": "54a5117f351d61514fc289282a860bee649b6dd529cd39a6a7302d104c6ad9a5",
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+ "receipt_verified": true,
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+ "generation": {
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+ "checkpoint": null,
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+ "checkpoint_sha256": null,
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+ "input_condition_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/fewshot_n5/draw4/test_raw.npz",
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+ "input_condition_sha256": "e864e90de96eaf6cec21e14dda14021922f00e4c7033786a422b6707ce5808f9",
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+ "seed": 20260927,
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+ "steps": null,
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+ "support_readout": "no output threshold: the upstream model's ZINB mean on every modeled gene (sample(bound=0), no xsemi > 10 step); read-out sensitivity variant, not the official output",
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+ "weights": null
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+ },
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+ "condition_for_broadcast": null,
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+ "poisson": {
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+ "rule": "whole NPZ, rows in NPZ order",
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+ "nnz_whole_npz": 26575576,
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+ "median_library_whole_npz_panel": 1920.0,
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+ "n_rows_whole_npz": 153000
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+ },
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+ "annotation": {
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+ "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
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+ "zero_padded_model_genes": 1,
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+ "label_counts": {
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+ "B cell": 14699,
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+ "DC": 5946,
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+ "Other": 726
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+ "mean_l2_max_prob": 0.9160382152815271,
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+ "n_donors": 102,
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+ "n_generated_cells": 153000,
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+ "composition": {
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+ "l1_generated_loo_mean_vs_observed_mean": 0.18333872525543213,
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+ "composition_top1": 0.00980392156862745,
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+ "Monocyte": 0.140882,
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+ "NK cell": 0.057621,
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+ "DC": 0.038863,
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+ "mean_fraction_observed": {
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+ "B cell": 0.074202,
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+ "NK cell": 0.091479,
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+ "DC": 0.022745,
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+ }
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+ },
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+ "pseudobulk": {
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+ "own_rank_median": 30.5,
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+ "n_reference": 15300
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+ "precision_coverage_depth_matched": {
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+ "precision": 0.9252287581699347,
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+ "coverage": 0.5188235294117647,
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+ "detected_median_before": {
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+ "Q": 167.0
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+ },
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+ "detected_median_after": {
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+ "R1": 167.0,
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+ "Q": 167.0
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+ }
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+ },
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+ "monocyte_pct": {
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+ "generated_mean_over_donors": 14.088235294117649,
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+ "observed_mean_over_donors": 16.58568855464869,
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+ "generated_pooled_cells": 14.088235294117645
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+ "median_umi_per_cell_scoring_genes": {
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+ "observed_pooled": 1963.0,
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+ "generated_mean_of_donor_medians": 1919.9754638671875,
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+ "seconds": 34.9
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+ }
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+ },
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+ "code_sha256": {
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+ "seconds": 42.7
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paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/immicro/fewshot_n5/draw4_scsemi5rt/README.md ADDED
@@ -0,0 +1,60 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # score_ft.py output: immicro, 102 scored donors
2
+
3
+ Arms: scsemi5rt. Every arm is scored on the same donors against the same observed reference.
4
+
5
+ ## Inputs and fixed contracts
6
+ - Observed reference: `/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scoring_cache/immicro/reference.npz` (sha c5e2547be3ec), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro;
7
+ 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/utility_studies_final6k_20260925/immicro/labels/observed_immicro/labels.parquet.
8
+ - Generated cells: `generated_counts` read out once with Poisson(seed 20260916) over the **whole NPZ in row order**
9
+ (`poisson_readout.poisson_integerise`), then the scored donors' rows. These integer counts are used for labels, embedding,
10
+ depth and the contrast. Pseudobulk uses the summed `generated_counts` (rates), as `score_generated.py`.
11
+ - 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 =
12
+ the 1,006-gene annotation list minus genes the dataset's sc assay did not measure in every sample (1004 genes).
13
+ - Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5508 genes).
14
+
15
+ ## Definitions
16
+ 1. **Composition**: fraction of each donor's cells per six-type label (all generated cells; all observed QC cells).
17
+ `comp_l1` = sum over the six labels of |generated - observed| (immicro_common.l1). `comp_l1_obs_loo_null` = L1 of the
18
+ donor's observed composition to the mean of the *other scored donors'* observed compositions. `composition_top1` =
19
+ fraction of donors whose generated composition is closest (L1) to their own observed composition.
20
+ 2. **Pseudobulk**: `pb_r` = Pearson r of CPM-log1p(summed generated_counts) vs CPM-log1p(observed pseudobulk) on the scoring
21
+ genes (evaluate_realbulk.cpm_log1p / paired_r). `pb_top1` = the donor's generated pseudobulk is closest (Pearson) to its own
22
+ observed pseudobulk among the 102 scored donors (immicro_common.retrieval = evaluate_realbulk.top1 arithmetic).
23
+ 3. **Precision / coverage / density** (stage_b_embed.py functions): per-cell CP10K over the scoring genes + log1p; PCA-50
24
+ (randomized, random_state 20260925) and k = 10 real-real k-NN balls fitted on **R1 of the scored donors only**.
25
+ Reference rule (independent of fold and arm): per donor, cells in canonical order (library_id+barcode), one
26
+ permutation from `default_rng([20260925, uint64(sha256(donor)[:8])])`; R1 = first n, R2 = next n,
27
+ n = min(150, n_cells // 2) (15300 R1 cells here). Generated query: per donor n cells (the donor's R1 size),
28
+ without replacement, `default_rng([20260926, same key])`. Precision = fraction of generated cells inside any R1 ball
29
+ (Kynkaanniemi 2019); coverage = fraction of R1 cells whose ball holds a generated cell; density = Naeem 2020.
30
+ Per-donor precision = that donor's generated cells; per-donor coverage = that donor's R1 cells (the pooled values are
31
+ their n-weighted means; plain means when every donor has 150 reference cells). R2 (held-out real cells of the same donors) is the
32
+ perfect-generator control: precision 0.9650, coverage 0.9999.
33
+ **Depth matched**: stage_b_embed depth matching run pairwise (R1 vs this arm): per donor and within-donor rank of detected
34
+ genes, both cells are thinned by UMI sampling without replacement to the smaller detected-gene count; PCA kept from
35
+ un-thinned R1, radii recomputed on thinned R1; fresh rng(20260929) per arm. R2 control: precision
36
+ 0.9629, coverage 0.9997.
37
+ 4. **Monocyte %** = 100 x Monocyte fraction (all cells) per donor; **median UMI per cell** = median over cells of the
38
+ Poisson-read (generated) / observed counts summed over the scoring genes (per donor, and pooled).
39
+ 5. **Group contrast** (T, B, Monocyte, NK): not computed (dataset immicro has no group contrast (use --groups-csv / --case-groups)).
40
+ Genes per type = scoring genes detected (count > 0) in >= 1 % of the scored donors' observed
41
+ cells of that type. Primary statistic `mean_cp10k`: per-cell CP10K over the scoring genes; per donor the mean over its
42
+ cells of the type (donors with >= 1 such cell); group mean = mean over the group's donors (donor-balanced);
43
+ logFC = log2(case mean + 1) - log2(control mean + 1). Secondary `pseudobulk_log1p_cpm`: the SLE benchmark evaluator's
44
+ statistic (donor x type summed counts, log1p CPM over the scoring genes, mean case - mean control;
45
+ evaluate_generated_vs_truth.group_log_fold_change). Both are computed on the generated cells (their frozen labels) and on
46
+ the observed cells; `r_generated` = Pearson r of the two logFC vectors. Broadcast baseline `r_broadcast`: the same
47
+ observed vector against the logFC of the input bulk (condition NPZ `bulk_counts` of the arm, scoring genes, per-donor
48
+ CP10K resp. log1p CPM, group means). `partial_r_generated_given_broadcast`: r of the OLS residuals of observed and
49
+ generated logFC on the bulk logFC (residual_specificity.py).
50
+ 6. **Top-50 DEG overlap** (contrast.csv rows `statistic = welch_top50_deg`; summary `deg_top50`): not computed (contrast off).
51
+
52
+ ## Files
53
+ - `per_donor.csv`: one row per arm x donor (all per-donor quantities above).
54
+ - `summary.json`: per arm the donor means / pooled values, provenance (NPZ sha, receipt check, generation metadata,
55
+ Poisson nnz of the whole NPZ, annotation contract), and the reference block (PCA, R2 control).
56
+ - `contrast.csv`: per arm x cell type x statistic (`mean_cp10k`, `pseudobulk_log1p_cpm`: logFC r; `welch_top50_deg`:
57
+ top-50 DEG overlap, T / B / Monocyte / NK / DC; columns of the other kind are empty).
58
+ - `arms/<arm>/labels/` (predict_scanvi.py labels + manifest), `arms/<arm>/generated_cells.parquet` (NPZ row, donor, label,
59
+ UMI on the scoring genes, P/C subsample flag), `arms/<arm>/deg_top50_lists.csv` (observed, generated and broadcast
60
+ top-50 gene lists, Ensembl ids, rank order).