Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw0_scsemi5nt/per_donor.csv

@@ -0,0 +1,26 @@
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paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw0_scsemi5nt/summary.json

@@ -0,0 +1,537 @@
+{
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+ "dataset": "ji2025_chili",
+ "written_utc": "2026-10-03T14:33:25.969243+00:00",
+ "command": [
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
+  "--dataset",
+  "ji2025_chili",
+  "--out",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw0_scsemi5nt",
+  "--threads",
+  "6",
+  "--donors",
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili/fewshot_n5/draw0/test_samples.txt",
+  "--case-groups",
+  "TOL,FU1",
+  "--control-groups",
+  "BC,AC",
+  "--arm",
+  "scsemi5nt=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ji2025_chili/fewshot_n5/draw0/scsemi5nt__raw.npz"
+ ],
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+  "score": "top-50 DEG overlap per cell type (benchmark total_top50_overlap, Welch backend)",
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+    "n_tested": 5459,
+    "n_case_donors": 14,
+    "n_control_donors": 11,
+    "n_cells": 14219,
+    "top_list_size": 50
+   },
+   "DC": {
+    "n_significant": 6,
+    "n_tested": 5020,
+    "n_case_donors": 12,
+    "n_control_donors": 11,
+    "n_cells": 1176,
+    "top_list_size": 6
+   }
+  },
+  "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": {
+  "scsemi5nt": {
+   "comp_l1": 0.5604744795506982,
+   "comp_l1_obs_loo_null": 0.4407987171459659,
+   "precision": 0.8872,
+   "coverage": 0.5832,
+   "precision_dm": 0.9469333333333333,
+   "coverage_dm": 0.4888,
+   "pb_r_median": 0.856885500816245,
+   "pb_top1": 0.12,
+   "mono_pct_gen": 26.695999999999998,
+   "mono_pct_obs": 24.326385304685406,
+   "median_umi_gen": 1026.0,
+   "median_umi_obs": 1066.0,
+   "contrast_mean_cp10k_r_gen": 0.056683021703307496,
+   "contrast_pseudobulk_log1p_cpm_r_gen": 0.11034644811546425,
+   "contrast_mean_cp10k_r_broadcast": 0.21311861411338662,
+   "contrast_pseudobulk_log1p_cpm_r_broadcast": 0.2060804786994818,
+   "deg_top50_T": 0,
+   "deg_top50_B": 0,
+   "deg_top50_Mono": 0,
+   "deg_top50_NK": 0,
+   "deg_top50_DC": 0,
+   "deg_top50_sum": 0,
+   "deg_top50_max": 125,
+   "deg_top50_sum_4type": 0,
+   "deg_top50_sum_broadcast": 0,
+   "seconds": 11.0
+  }
+ },
+ "per_arm": {
+  "scsemi5nt": {
+   "arm": "scsemi5nt",
+   "npz": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/generated/ji2025_chili/fewshot_n5/draw0/scsemi5nt__raw.npz",
+   "npz_sha256": "d90fd13542e4d4b165429d594c75b3c05c2f655a93a6fd59b42d5e724a2f312f",
+   "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/ji2025_chili/fewshot_n5/draw0/test_raw.npz",
+    "input_condition_sha256": "ca5da7eba08208ca5e2ebf107830ad72db4a1e739698e0412fff48edf8d2b2b9",
+    "seed": 20260927,
+    "steps": null,
+    "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",
+    "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/ji2025_chili/fewshot_n5/draw0/test_raw.npz",
+    "sha256": "ca5da7eba08208ca5e2ebf107830ad72db4a1e739698e0412fff48edf8d2b2b9"
+   },
+   "poisson": {
+    "seed": 20260916,
+    "rule": "whole NPZ, rows in NPZ order",
+    "nnz_whole_npz": 6896947,
+    "median_library_whole_npz_panel": 1026.0,
+    "n_rows_whole_npz": 37500
+   },
+   "annotation": {
+    "model_pt_sha256": "bf3655899e5abee435340fc008656c4a17342da0f30b70cfd43de8e9223e1930",
+    "zero_padded_model_genes": 1,
+    "label_counts": {
+     "T cell": 17584,
+     "Monocyte": 10011,
+     "NK cell": 5324,
+     "B cell": 1573,
+     "DC": 1528,
+     "Other": 1480
+    },
+    "mean_l2_max_prob": 0.8802247133918604,
+    "predict_seconds": 3.9
+   },
+   "n_donors": 25,
+   "n_generated_cells": 37500,
+   "composition": {
+    "l1_mean": 0.5604744795506982,
+    "l1_median": 0.5492710280373831,
+    "l1_obs_loo_null_mean": 0.4407987171459659,
+    "l1_generated_loo_mean_vs_observed_mean": 0.43817147154629943,
+    "composition_top1": 0.0,
+    "mean_fraction_generated": {
+     "T cell": 0.468907,
+     "B cell": 0.041947,
+     "Monocyte": 0.26696,
+     "NK cell": 0.141973,
+     "DC": 0.040747,
+     "Other": 0.039467
+    },
+    "mean_fraction_observed": {
+     "T cell": 0.495831,
+     "B cell": 0.050844,
+     "Monocyte": 0.243264,
+     "NK cell": 0.166912,
+     "DC": 0.012828,
+     "Other": 0.030322
+    }
+   },
+   "pseudobulk": {
+    "n_genes": 5752,
+    "r_median": 0.856885500816245,
+    "r_mean": 0.8487589016780513,
+    "top1": 0.12,
+    "n_top1": 3,
+    "own_rank_median": 9.0,
+    "chance_top1": 0.04
+   },
+   "precision_coverage": {
+    "precision": 0.8872,
+    "coverage": 0.5832,
+    "density": 0.8618399999999999,
+    "n_query": 3750,
+    "n_reference": 3750
+   },
+   "precision_coverage_depth_matched": {
+    "precision": 0.9469333333333333,
+    "coverage": 0.4888,
+    "density": 0.7662133333333333,
+    "detected_median_before": {
+     "R1": 363.0,
+     "Q": 171.0
+    },
+    "detected_median_after": {
+     "R1": 171.0,
+     "Q": 171.0
+    }
+   },
+   "monocyte_pct": {
+    "generated_mean_over_donors": 26.695999999999998,
+    "observed_mean_over_donors": 24.326385304685406,
+    "generated_pooled_cells": 26.695999999999998
+   },
+   "median_umi_per_cell_scoring_genes": {
+    "generated_pooled": 1026.0,
+    "observed_pooled": 1066.0,
+    "generated_mean_of_donor_medians": 1026.0799560546875,
+    "observed_mean_of_donor_medians": 1166.7
+   },
+   "contrast": {
+    "mean_cp10k": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.16342029693743088,
+       "r_broadcast": 0.21197028589936057,
+       "partial_r_generated_given_broadcast": 0.07511491963475975,
+       "r_generated_vs_broadcast": 0.46422292593271747,
+       "n_genes": 3038
+      },
+      "B cell": {
+       "r_generated": -0.12823852897894103,
+       "r_broadcast": 0.10073938447302222,
+       "partial_r_generated_given_broadcast": -0.16800294325207313,
+       "r_generated_vs_broadcast": 0.30642494188113745,
+       "n_genes": 3233
+      },
+      "Monocyte": {
+       "r_generated": 0.22733272634800397,
+       "r_broadcast": 0.3732268158395985,
+       "partial_r_generated_given_broadcast": 0.09798233193865992,
+       "r_generated_vs_broadcast": 0.3842405248283729,
+       "n_genes": 3193
+      },
+      "NK cell": {
+       "r_generated": -0.03578240749326384,
+       "r_broadcast": 0.1665379702415652,
+       "partial_r_generated_given_broadcast": -0.10979968835663259,
+       "r_generated_vs_broadcast": 0.38510048576380823,
+       "n_genes": 3059
+      }
+     },
+     "mean_r_generated": 0.056683021703307496,
+     "mean_r_broadcast": 0.21311861411338662,
+     "mean_partial_r": -0.026176345008821513
+    },
+    "pseudobulk_log1p_cpm": {
+     "per_type": {
+      "T cell": {
+       "r_generated": 0.10759290416652097,
+       "r_broadcast": 0.3111080127833571,
+       "partial_r_generated_given_broadcast": 0.03138520270552734,
+       "r_generated_vs_broadcast": 0.25308336369106504,
+       "n_genes": 3038
+      },
+      "B cell": {
+       "r_generated": -0.051348273839640995,
+       "r_broadcast": 0.012072638770403557,
+       "partial_r_generated_given_broadcast": -0.05364738734232042,
+       "r_generated_vs_broadcast": 0.1438820040382055,
+       "n_genes": 3233
+      },
+      "Monocyte": {
+       "r_generated": 0.3091283980582289,
+       "r_broadcast": 0.3506580485024196,
+       "partial_r_generated_given_broadcast": 0.26271508550521844,
+       "r_generated_vs_broadcast": 0.19314398126317553,
+       "n_genes": 3193
+      },
+      "NK cell": {
+       "r_generated": 0.07601276407674813,
+       "r_broadcast": 0.15048321474174686,
+       "partial_r_generated_given_broadcast": 0.05388040747611344,
+       "r_generated_vs_broadcast": 0.15545580606391174,
+       "n_genes": 3059
+      }
+     },
+     "mean_r_generated": 0.11034644811546425,
+     "mean_r_broadcast": 0.2060804786994818,
+     "mean_partial_r": 0.0735833270861347
+    }
+   },
+   "deg_top50": {
+    "types_scored": [
+     "T cell",
+     "B cell",
+     "Monocyte",
+     "NK cell",
+     "DC"
+    ],
+    "types_not_scored": {},
+    "per_type": {
+     "T cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 44851,
+      "n_cells_gen": 17584
+     },
+     "B cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 9,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 4424,
+      "n_cells_gen": 1573
+     },
+     "Monocyte": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 10,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 22847,
+      "n_cells_gen": 10011
+     },
+     "NK cell": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 50,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 14219,
+      "n_cells_gen": 5324
+     },
+     "DC": {
+      "deg_scored": true,
+      "top50_overlap": 0,
+      "top50_jaccard": 0.0,
+      "obs_top_list_size": 6,
+      "gen_top_list_size": 0,
+      "top50_overlap_broadcast": 0,
+      "top50_jaccard_broadcast": 0.0,
+      "n_cells_obs": 1176,
+      "n_cells_gen": 1528
+     }
+    },
+    "sum": 0,
+    "sum_4type": 0,
+    "max": 125,
+    "mean": 0.0,
+    "sum_broadcast": 0,
+    "sum_broadcast_4type": 0,
+    "broadcast_n_significant": 4,
+    "seconds": 0.07
+   },
+   "seconds": 11.0
+  }
+ },
+ "code_sha256": {
+  "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py": "ec5f40b4cea6d5062d0138f3b934abb6465e34cbfeac869c8a0906924c033aec",
+  "/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": 12.6
+}

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw0_scsemi5rt/README.md

@@ -0,0 +1,73 @@
+# score_ft.py output: ji2025_chili, 25 scored donors
+
+Arms: scsemi5rt.  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).

paper-releases/pbisc-diffusion/updates/20261003T174003Z-scsemi-readout-sensitivity/diffusion/dataset/server_1/model8/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw0_scsemi5rt/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
+scsemi5rt,T cell,mean_cp10k,3038,0.11547474985640313,0.21197028589936057,0.039405682318534134,0.3764562875510768,14,11,14,11,44851,19077,,,,,,,,,,,
+scsemi5rt,T cell,pseudobulk_log1p_cpm,3038,0.13461070401829223,0.3111080127833571,0.05015548210410561,0.2858597632324631,14,11,14,11,44851,19077,,,,,,,,,,,
+scsemi5rt,B cell,mean_cp10k,3233,-0.09244414035009216,0.10073938447302222,-0.11424074070033793,0.1900370278101038,14,11,11,9,4424,1145,,,,,,,,,,,
+scsemi5rt,B cell,pseudobulk_log1p_cpm,3233,-0.052988838157227366,0.012072638770403557,-0.056411918699425995,0.19422446892651796,14,11,11,9,4424,1145,,,,,,,,,,,
+scsemi5rt,Monocyte,mean_cp10k,3193,0.09487985600822,0.3732268158395985,-0.01357550029342054,0.2865449602730492,14,11,14,10,22847,8666,,,,,,,,,,,
+scsemi5rt,Monocyte,pseudobulk_log1p_cpm,3193,0.11658901167032525,0.3506580485024196,0.0530267915839915,0.1935453435797445,14,11,14,10,22847,8666,,,,,,,,,,,
+scsemi5rt,NK cell,mean_cp10k,3059,0.0608195729082749,0.1665379702415652,0.012175999873344655,0.29634630134051226,14,11,11,10,14219,4756,,,,,,,,,,,
+scsemi5rt,NK cell,pseudobulk_log1p_cpm,3059,0.03426716129598247,0.15048321474174686,-0.0021445557668943152,0.24138640182875032,14,11,11,10,14219,4756,,,,,,,,,,,
+scsemi5rt,T cell,welch_top50_deg,5752,,,,,14,11,14,11,44851,19077,True,0,0.0,50,0,1831,0,0,0.0,4,4
+scsemi5rt,B cell,welch_top50_deg,5752,,,,,14,11,11,9,4424,1145,True,0,0.0,9,0,9,0,0,0.0,4,4
+scsemi5rt,Monocyte,welch_top50_deg,5752,,,,,14,11,14,10,22847,8666,True,0,0.0,10,0,10,0,0,0.0,4,4
+scsemi5rt,NK cell,welch_top50_deg,5752,,,,,14,11,11,10,14219,4756,True,0,0.0,50,0,358,0,0,0.0,4,4
+scsemi5rt,DC,welch_top50_deg,5752,,,,,12,11,11,10,1176,1701,True,0,0.0,6,0,6,0,0,0.0,4,4