Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20260928T225702Z-paired-ft-scsemi-delta/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw3_scsemi5/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
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+scsemi5,T cell,welch_top50_deg,5927,,,,,104,31,104,31,241386,116137,True,15,0.17647058823529413,50,50,873,682,16,0.19047619047619047,50,2000
+scsemi5,B cell,welch_top50_deg,5927,,,,,104,31,103,30,46374,9941,True,9,0.0989010989010989,50,50,215,153,13,0.14942528735632185,50,2000
+scsemi5,Monocyte,welch_top50_deg,5927,,,,,103,31,102,31,74900,32762,True,12,0.13636363636363635,50,50,270,310,20,0.25,50,2000
+scsemi5,NK cell,welch_top50_deg,5927,,,,,104,31,101,31,67818,36680,True,14,0.16279069767441862,50,50,321,539,15,0.17647058823529413,50,2000
+scsemi5,DC,welch_top50_deg,5927,,,,,104,31,103,31,12959,4416,True,10,0.1111111111111111,50,50,272,228,12,0.13636363636363635,50,2000

paper-releases/pbisc-diffusion/updates/20260928T225702Z-paired-ft-scsemi-delta/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw3_scsemi5/per_donor.csv

@@ -0,0 +1,146 @@
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+   "B cell": {
+    "n_significant": 215,
+    "n_tested": 5636,
+    "n_case_donors": 104,
+    "n_control_donors": 31,
+    "n_cells": 46374,
+    "top_list_size": 50
+   },
+   "Monocyte": {
+    "n_significant": 270,
+    "n_tested": 5628,
+    "n_case_donors": 103,
+    "n_control_donors": 31,
+    "n_cells": 74900,
+    "top_list_size": 50
+   },
+   "NK cell": {
+    "n_significant": 321,
+    "n_tested": 5579,
+    "n_case_donors": 104,
+    "n_control_donors": 31,
+    "n_cells": 67818,
+    "top_list_size": 50
+   },
+   "DC": {
+    "n_significant": 272,
+    "n_tested": 5426,
+    "n_case_donors": 104,
+    "n_control_donors": 31,
+    "n_cells": 12959,
+    "top_list_size": 50
+   }
+  },
+  "broadcast": "welch_bh_deg + top_deg_genes on the arm's condition bulk_counts (scoring genes, case|control donors), one list compared with every type's observed list (sle_broadcast.py broadcast_input_bulk); not part of the published benchmark"
+ },
+ "headline": {
+  "scsemi5": {
+   "comp_l1": 0.5938606551152373,
+   "comp_l1_obs_loo_null": 0.5045022619268063,
+   "precision": 0.2963287519601513,
+   "coverage": 0.04390738861728623,
+   "precision_dm": 0.4286966147034406,
+   "coverage_dm": 0.14034683147311133,
+   "pb_r_median": 0.5839404229272007,
+   "pb_top1": 0.013793103448275862,
+   "mono_pct_gen": 16.065287356321836,
+   "mono_pct_obs": 17.381107161241506,
+   "median_umi_gen": 1584.0,
+   "median_umi_obs": 1331.0,
+   "contrast_mean_cp10k_r_gen": 0.5914775900543578,
+   "contrast_pseudobulk_log1p_cpm_r_gen": 0.35730888726074056,
+   "contrast_mean_cp10k_r_broadcast": 0.6538959233815048,
+   "contrast_pseudobulk_log1p_cpm_r_broadcast": 0.5814675058221703,
+   "deg_top50_T": 15,
+   "deg_top50_B": 9,
+   "deg_top50_Mono": 12,
+   "deg_top50_NK": 14,
+   "deg_top50_DC": 10,
+   "deg_top50_sum": 60,
+   "deg_top50_max": 250,
+   "deg_top50_sum_4type": 50,
+   "deg_top50_sum_broadcast": 76,
+   "seconds": 30.3
+  }
+ },
+ "per_arm": {
+  "scsemi5": {
+   "arm": "scsemi5",
+   "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/imperial_eskd/fewshot_n5/draw3/scsemi5__raw.npz",
+   "npz_sha256": "dada388bc27ade843f68466badf374af95bd5c0f9d6af8bc0f476935bf351739",
+   "receipt_verified": true,
+   "generation": {
+    "checkpoint": null,
+    "checkpoint_sha256": null,
+    "input_condition_path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd/fewshot_n5/draw3/test_raw.npz",
+    "input_condition_sha256": "1bb5ea46702a37ba35cf81b4e82426c5cfa539ef881c477be37e40621df26b7f",
+    "seed": 20260927,
+    "steps": null,
+    "support_readout": "upstream (values <= 10 normalized units zeroed by fast_semi)",
+    "weights": null
+   },
+   "condition_for_broadcast": {
+    "path": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd/fewshot_n5/draw3/test_raw.npz",
+    "sha256": "1bb5ea46702a37ba35cf81b4e82426c5cfa539ef881c477be37e40621df26b7f"
+   },
+   "poisson": {
+    "seed": 20260916,
+    "rule": "whole NPZ, rows in NPZ order",
+    "nnz_whole_npz": 27217222,
+    "median_library_whole_npz_panel": 1584.0,
+    "n_rows_whole_npz": 217500
+   },
+   "annotation": {
+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 0,
+    "label_counts": {
+     "T cell": 126366,
+     "NK cell": 38206,
+     "Monocyte": 34942,
+     "B cell": 10390,
+     "DC": 4784,
+     "Other": 2812
+    },
+    "mean_l2_max_prob": 0.927854809916773,
+    "predict_seconds": 6.3
+   },
+   "n_donors": 145,
+   "n_generated_cells": 217500,
+   "composition": {
+    "l1_mean": 0.5938606551152373,
+    "l1_median": 0.5609776331842211,
+    "l1_obs_loo_null_mean": 0.5045022619268063,
+    "l1_generated_loo_mean_vs_observed_mean": 0.50703438088864,
+    "composition_top1": 0.006896551724137931,
+    "mean_fraction_generated": {
+     "T cell": 0.580993,
+     "B cell": 0.04777,
+     "Monocyte": 0.160653,
+     "NK cell": 0.17566,
+     "DC": 0.021995,
+     "Other": 0.012929
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.489671,
+     "B cell": 0.123619,
+     "Monocyte": 0.173811,
+     "NK cell": 0.167078,
+     "DC": 0.031077,
+     "Other": 0.014744
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5927,
+    "r_median": 0.5839404229272007,
+    "r_mean": 0.5881505145410274,
+    "top1": 0.013793103448275862,
+    "n_top1": 2,
+    "own_rank_median": 45.0,
+    "chance_top1": 0.006896551724137931
+   },
+   "precision_coverage": {
+    "precision": 0.2963287519601513,
+    "coverage": 0.04390738861728623,
+    "density": 0.04372290379116318,
+    "n_query": 21682,
+    "n_reference": 21682
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.4286966147034406,
+    "coverage": 0.14034683147311133,
+    "density": 0.15388340558989022,
+    "detected_median_before": {
+     "R1": 344.0,
+     "Q": 122.0
+    },
+    "detected_median_after": {
+     "R1": 122.0,
+     "Q": 122.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 16.065287356321836,
+    "observed_mean_over_donors": 17.381107161241506,
+    "generated_pooled_cells": 16.06528735632184
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1584.0,
+    "observed_pooled": 1331.0,
+    "generated_mean_of_donor_medians": 1583.7965087890625,
+    "observed_mean_of_donor_medians": 1258.7103448275861
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.6061803082837304,
+       "r_broadcast": 0.6363131785460265,
+       "partial_r_generated_given_broadcast": 0.5407485211579497,
+       "r_generated_vs_broadcast": 0.3349365208264034,
+       "n_genes": 3031
+      },
+      "B cell": {
+       "r_generated": 0.485353746615838,
+       "r_broadcast": 0.6181604529907424,
+       "partial_r_generated_given_broadcast": 0.35492619320968816,
+       "r_generated_vs_broadcast": 0.36496658657279124,
+       "n_genes": 3186
+      },
+      "Monocyte": {
+       "r_generated": 0.5994553306842716,
+       "r_broadcast": 0.7038117222497147,
+       "partial_r_generated_given_broadcast": 0.35989597147959185,
+       "r_generated_vs_broadcast": 0.5478285778796022,
+       "n_genes": 2879
+      },
+      "NK cell": {
+       "r_generated": 0.6749209746335916,
+       "r_broadcast": 0.6572983397395356,
+       "partial_r_generated_given_broadcast": 0.5785783793398267,
+       "r_generated_vs_broadcast": 0.4269332339414219,
+       "n_genes": 2964
+      }
+     },
+     "mean_r_generated": 0.5914775900543578,
+     "mean_r_broadcast": 0.6538959233815048,
+     "mean_partial_r": 0.4585372662967641
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.43528385554171845,
+       "r_broadcast": 0.6151584600190607,
+       "partial_r_generated_given_broadcast": 0.30337219618746525,
+       "r_generated_vs_broadcast": 0.34226986924131564,
+       "n_genes": 3031
+      },
+      "B cell": {
+       "r_generated": 0.14083503826669272,
+       "r_broadcast": 0.4479445109520174,
+       "partial_r_generated_given_broadcast": 0.00746248227369372,
+       "r_generated_vs_broadcast": 0.30019531822471085,
+       "n_genes": 3186
+      },
+      "Monocyte": {
+       "r_generated": 0.435801881305859,
+       "r_broadcast": 0.6894081488945825,
+       "partial_r_generated_given_broadcast": 0.1691980327052629,
+       "r_generated_vs_broadcast": 0.4757698343084072,
+       "n_genes": 2879
+      },
+      "NK cell": {
+       "r_generated": 0.41731477392869204,
+       "r_broadcast": 0.5733589034230205,
+       "partial_r_generated_given_broadcast": 0.27503630325815914,
+       "r_generated_vs_broadcast": 0.3613894291204923,
+       "n_genes": 2964
+      }
+     },
+     "mean_r_generated": 0.35730888726074056,
+     "mean_r_broadcast": 0.5814675058221703,
+     "mean_partial_r": 0.18876725360614527
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 15,
+      "top50_jaccard": 0.17647058823529413,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 16,
+      "top50_jaccard_broadcast": 0.19047619047619047,
+      "n_cells_obs": 241386,
+      "n_cells_gen": 116137
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 9,
+      "top50_jaccard": 0.0989010989010989,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 13,
+      "top50_jaccard_broadcast": 0.14942528735632185,
+      "n_cells_obs": 46374,
+      "n_cells_gen": 9941
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 12,
+      "top50_jaccard": 0.13636363636363635,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 20,
+      "top50_jaccard_broadcast": 0.25,
+      "n_cells_obs": 74900,
+      "n_cells_gen": 32762
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 14,
+      "top50_jaccard": 0.16279069767441862,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 15,
+      "top50_jaccard_broadcast": 0.17647058823529413,
+      "n_cells_obs": 67818,
+      "n_cells_gen": 36680
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 10,
+      "top50_jaccard": 0.1111111111111111,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 12,
+      "top50_jaccard_broadcast": 0.13636363636363635,
+      "n_cells_obs": 12959,
+      "n_cells_gen": 4416
+     }
+    },
+    "sum": 60,
+    "sum_4type": 50,
+    "max": 250,
+    "mean": 12.0,
+    "sum_broadcast": 76,
+    "sum_broadcast_4type": 64,
+    "broadcast_n_significant": 2000,
+    "seconds": 0.2
+   },
+   "seconds": 30.3
+  }
+ },
+ "code_sha256": {
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py": "492e929e9bc7b716f6f9c8bbeedc98e7dec8cc759a20cdc6ec39b1e65194eb74",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/ft_common.py": "4a6366b0f22208cd58714fc0261b8840c643b9d4569704d560cfd93534cbc747",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/umap/cell_immicro_5arm/tools/stage_b_embed.py": "d056cfdf38fb0e5f478756f370174e993b75d0c973c48e02e21749175a4ca2da",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py": "48b12d794782b840838853e93e0fc55fab00532c4b3698f4588e926b985d2881",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/utility_studies_final6k_20260925/immicro/tools/immicro_common.py": "c5c30e917148cc5e664e91ea30c11bbdff9b6d1f53a3e0a13e16b1917eff37e9",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/predict_scanvi.py": "482ee5dcf4d72b54bc36cbb6dcff62e5030bc7afc46cb5fb01eca73a82ee45a5",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/aifi_scanvi_rebuild_20260916/poisson_readout.py": "8bfa080942fbcdc89ae23b276524a1a83266fb8cbc56b9af279e50f82e6cde38"
+ },
+ "seconds": 38.8
+}

paper-releases/pbisc-diffusion/updates/20260928T225702Z-paired-ft-scsemi-delta/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw0_scsemi5/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: ji2025_chili, 25 scored donors
+
+Arms: scsemi5.  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/ji2025_chili/reference.npz` (sha 19c30f56db69), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili;
+  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/ji2025_chili/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 (1004 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5752 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 25 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) (3750 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 0.9960, 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
+   0.9957, 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 ['TOL', 'FU1'] (n = 14) vs control groups ['BC', 'AC'] (n = 11); 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 5752 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/ji2025_chili/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 14 + 11
+   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).