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/draw2_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
+scsemi5,T cell,mean_cp10k,3036,0.6303084207739375,0.6311553277299028,0.575486171136399,0.33137624044505765,103,31,103,31,249798,101244,,,,,,,,,,,
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+scsemi5,B cell,mean_cp10k,3187,0.49036614830717173,0.6245632309702648,0.394073194357464,0.317941843970975,103,31,102,31,46668,10503,,,,,,,,,,,
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+scsemi5,Monocyte,mean_cp10k,2886,0.6047453753942459,0.6837051334204964,0.36554231497527695,0.5617173759440347,102,31,102,31,75478,39857,,,,,,,,,,,
+scsemi5,Monocyte,pseudobulk_log1p_cpm,2886,0.39128867943202456,0.6802082395314715,0.1594600320663849,0.41923862429931846,102,31,102,31,75478,39857,,,,,,,,,,,
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+scsemi5,NK cell,pseudobulk_log1p_cpm,2961,0.2637650789441471,0.5753918871563406,0.14475695448330875,0.25970820176082243,103,31,102,31,66389,36488,,,,,,,,,,,
+scsemi5,T cell,welch_top50_deg,5927,,,,,103,31,103,31,249798,101244,True,12,0.13636363636363635,50,50,655,1166,13,0.14942528735632185,50,2026
+scsemi5,B cell,welch_top50_deg,5927,,,,,103,31,102,31,46668,10503,True,5,0.05263157894736842,50,50,240,680,10,0.1111111111111111,50,2026
+scsemi5,Monocyte,welch_top50_deg,5927,,,,,102,31,102,31,75478,39857,True,7,0.07526881720430108,50,50,235,537,18,0.21951219512195122,50,2026
+scsemi5,NK cell,welch_top50_deg,5927,,,,,103,31,102,31,66389,36488,True,8,0.08695652173913043,50,50,359,411,13,0.14942528735632185,50,2026
+scsemi5,DC,welch_top50_deg,5927,,,,,103,31,101,31,12425,9224,True,8,0.08695652173913043,50,50,214,394,11,0.12359550561797752,50,2026

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/draw2_scsemi5/per_donor.csv

@@ -0,0 +1,143 @@
+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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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/draw2_scsemi5/summary.json

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+   "median_umi_obs": 1372.0,
+   "contrast_mean_cp10k_r_gen": 0.5623860563967511,
+   "contrast_pseudobulk_log1p_cpm_r_gen": 0.2593472152544092,
+   "contrast_mean_cp10k_r_broadcast": 0.6433613217171205,
+   "contrast_pseudobulk_log1p_cpm_r_broadcast": 0.5930746251539698,
+   "deg_top50_T": 12,
+   "deg_top50_B": 5,
+   "deg_top50_Mono": 7,
+   "deg_top50_NK": 8,
+   "deg_top50_DC": 8,
+   "deg_top50_sum": 40,
+   "deg_top50_max": 250,
+   "deg_top50_sum_4type": 32,
+   "deg_top50_sum_broadcast": 65,
+   "seconds": 29.7
+  }
+ },
+ "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/draw2/scsemi5__raw.npz",
+   "npz_sha256": "56d7277764410416d6b38f7ddb91d02aa2d4c138129080b7875eaf11890ca3c6",
+   "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/draw2/test_raw.npz",
+    "input_condition_sha256": "11ba7c4e0f734b8eeab115536093d16205a1f592833e54115aa10cafd131e466",
+    "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/draw2/test_raw.npz",
+    "sha256": "11ba7c4e0f734b8eeab115536093d16205a1f592833e54115aa10cafd131e466"
+   },
+   "poisson": {
+    "seed": 20260916,
+    "rule": "whole NPZ, rows in NPZ order",
+    "nnz_whole_npz": 28068206,
+    "median_library_whole_npz_panel": 1013.0,
+    "n_rows_whole_npz": 213000
+   },
+   "annotation": {
+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 0,
+    "label_counts": {
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+     "NK cell": 39395,
+     "B cell": 10682,
+     "DC": 9713,
+     "Other": 3851
+    },
+    "mean_l2_max_prob": 0.9235517253050922,
+    "predict_seconds": 5.8
+   },
+   "n_donors": 142,
+   "n_generated_cells": 213000,
+   "composition": {
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+    "l1_median": 0.6726027121872784,
+    "l1_obs_loo_null_mean": 0.47851633327026316,
+    "l1_generated_loo_mean_vs_observed_mean": 0.5036088327087789,
+    "composition_top1": 0.007042253521126761,
+    "mean_fraction_generated": {
+     "T cell": 0.503437,
+     "B cell": 0.05015,
+     "Monocyte": 0.197779,
+     "NK cell": 0.184953,
+     "DC": 0.045601,
+     "Other": 0.01808
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.50954,
+     "B cell": 0.123023,
+     "Monocyte": 0.162058,
+     "NK cell": 0.163853,
+     "DC": 0.028277,
+     "Other": 0.013248
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5927,
+    "r_median": 0.6185093949777509,
+    "r_mean": 0.6199610427597534,
+    "top1": 0.04225352112676056,
+    "n_top1": 6,
+    "own_rank_median": 44.5,
+    "chance_top1": 0.007042253521126761
+   },
+   "precision_coverage": {
+    "precision": 0.3373210248681236,
+    "coverage": 0.059721175584024115,
+    "density": 0.058708553127354936,
+    "n_query": 21232,
+    "n_reference": 21232
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.41423323285606634,
+    "coverage": 0.1705444611906556,
+    "density": 0.11880180859080633,
+    "detected_median_before": {
+     "R1": 347.0,
+     "Q": 131.0
+    },
+    "detected_median_after": {
+     "R1": 131.0,
+     "Q": 131.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 19.777934272300467,
+    "observed_mean_over_donors": 16.205826518972017,
+    "generated_pooled_cells": 19.77793427230047
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1013.0,
+    "observed_pooled": 1372.0,
+    "generated_mean_of_donor_medians": 1013.0352172851562,
+    "observed_mean_of_donor_medians": 1311.5598591549297
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.6303084207739375,
+       "r_broadcast": 0.6311553277299028,
+       "partial_r_generated_given_broadcast": 0.575486171136399,
+       "r_generated_vs_broadcast": 0.33137624044505765,
+       "n_genes": 3036
+      },
+      "B cell": {
+       "r_generated": 0.49036614830717173,
+       "r_broadcast": 0.6245632309702648,
+       "partial_r_generated_given_broadcast": 0.394073194357464,
+       "r_generated_vs_broadcast": 0.317941843970975,
+       "n_genes": 3187
+      },
+      "Monocyte": {
+       "r_generated": 0.6047453753942459,
+       "r_broadcast": 0.6837051334204964,
+       "partial_r_generated_given_broadcast": 0.36554231497527695,
+       "r_generated_vs_broadcast": 0.5617173759440347,
+       "n_genes": 2886
+      },
+      "NK cell": {
+       "r_generated": 0.5241242811116497,
+       "r_broadcast": 0.6340215947478178,
+       "partial_r_generated_given_broadcast": 0.38874369486241706,
+       "r_generated_vs_broadcast": 0.3900772667930024,
+       "n_genes": 2961
+      }
+     },
+     "mean_r_generated": 0.5623860563967511,
+     "mean_r_broadcast": 0.6433613217171205,
+     "mean_partial_r": 0.43096134383288925
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.356856949420089,
+       "r_broadcast": 0.6350373450250355,
+       "partial_r_generated_given_broadcast": 0.2713132079444818,
+       "r_generated_vs_broadcast": 0.24169651651795887,
+       "n_genes": 3036
+      },
+      "B cell": {
+       "r_generated": 0.025478153221376126,
+       "r_broadcast": 0.4816610289030315,
+       "partial_r_generated_given_broadcast": -0.08059674919730155,
+       "r_generated_vs_broadcast": 0.1966740332907685,
+       "n_genes": 3187
+      },
+      "Monocyte": {
+       "r_generated": 0.39128867943202456,
+       "r_broadcast": 0.6802082395314715,
+       "partial_r_generated_given_broadcast": 0.1594600320663849,
+       "r_generated_vs_broadcast": 0.41923862429931846,
+       "n_genes": 2886
+      },
+      "NK cell": {
+       "r_generated": 0.2637650789441471,
+       "r_broadcast": 0.5753918871563406,
+       "partial_r_generated_given_broadcast": 0.14475695448330875,
+       "r_generated_vs_broadcast": 0.25970820176082243,
+       "n_genes": 2961
+      }
+     },
+     "mean_r_generated": 0.2593472152544092,
+     "mean_r_broadcast": 0.5930746251539698,
+     "mean_partial_r": 0.12373336132421847
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 12,
+      "top50_jaccard": 0.13636363636363635,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 13,
+      "top50_jaccard_broadcast": 0.14942528735632185,
+      "n_cells_obs": 249798,
+      "n_cells_gen": 101244
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 5,
+      "top50_jaccard": 0.05263157894736842,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 10,
+      "top50_jaccard_broadcast": 0.1111111111111111,
+      "n_cells_obs": 46668,
+      "n_cells_gen": 10503
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 7,
+      "top50_jaccard": 0.07526881720430108,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 18,
+      "top50_jaccard_broadcast": 0.21951219512195122,
+      "n_cells_obs": 75478,
+      "n_cells_gen": 39857
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 8,
+      "top50_jaccard": 0.08695652173913043,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 13,
+      "top50_jaccard_broadcast": 0.14942528735632185,
+      "n_cells_obs": 66389,
+      "n_cells_gen": 36488
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 8,
+      "top50_jaccard": 0.08695652173913043,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 50,
+      "top50_overlap_broadcast": 11,
+      "top50_jaccard_broadcast": 0.12359550561797752,
+      "n_cells_obs": 12425,
+      "n_cells_gen": 9224
+     }
+    },
+    "sum": 40,
+    "sum_4type": 32,
+    "max": 250,
+    "mean": 8.0,
+    "sum_broadcast": 65,
+    "sum_broadcast_4type": 54,
+    "broadcast_n_significant": 2026,
+    "seconds": 0.2
+   },
+   "seconds": 29.7
+  }
+ },
+ "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": 37.8
+}

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/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: imperial_eskd, 145 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/imperial_eskd/reference.npz` (sha 34b4caa3f8a6), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd;
+  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/imperial_eskd/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 (1005 genes).
+- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (5927 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 145 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) (21682 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 0.9691, coverage 0.9998.
+   **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
+   0.9693, 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 ['covid_positive'] (n = 104) vs control groups ['covid_negative'] (n = 31); 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 5927 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/imperial_eskd/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 104 + 31
+   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).