Archive 4 checkpoints (1-4)
Browse files- paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/README.md +73 -0
- paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/contrast.csv +53 -0
- paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/per_donor.csv +0 -0
- paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/summary.json +1612 -0
paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/README.md
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# score_ft.py output: ye2026_sepsis, 77 scored donors
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Arms: trunk4x, panel_matched_1x, current_f01, scsemi. Every arm is scored on the same donors against the same observed reference.
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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/trunk4x_lora_sepsis_20261013/reference/reference.npz` (sha 8c5f2d794d13), built from datadir:/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/evaluation_data/ye2026_sepsis;
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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/trunk4x_lora_sepsis_20261013/reference/observed_labels/labels/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 (1006 genes).
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- Scoring genes: 6k panel genes measured by both assays in every sample of the dataset (4243 genes).
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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 77 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) (11550 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 0.9807, coverage 0.9997.
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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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0.9786, 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): case groups ['adult_sepsis', 'ped_sepsis'] (n = 57) vs control groups ['adult_HC', 'adult_ICU_ctrl', 'ped_HC', 'ped_ICU_ctrl'] (n = 20); rule: explicit --case-groups / --control-groups; groups from features 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`): the SLE benchmark's score (`total_top50_overlap` of
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`bulk2sc-paper/scripts/evaluate_generated_vs_truth.py`, Welch backend: the "top-50 DEG 81 / 84" of the final model), run
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with the evaluator's own functions on the same donor x type pseudobulks as the contrast. Per cell type, observed and
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generated separately: donor x type summed counts on all 4243 scoring genes (donors of the two groups with
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>= 1 cell of the type) -> `welch_bh_deg` (log1p CPM, two-sided Welch t-test case vs control, Benjamini-Hochberg over the
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genes with a defined p-value; needs >= 2 donors per group) -> `top_deg_genes` (FDR < 0.05, descending |logFC|,
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first 50; shorter when fewer genes pass). `top50_overlap` = |observed list & generated list|; `top50_jaccard`
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the evaluator's Jaccard. Summary `sum` = total over the scored types (the quantity reported as 81), `max` = the sum of
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the observed list lengths (the "of 262"), `sum_4type` = T + B + Monocyte + NK. A type is scored when its observed test
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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
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(aggregates: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/reference/deg_reference.npz).
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**Broadcast analogue** (`top50_overlap_broadcast`, as `sle_broadcast.py` `broadcast_input_bulk`; not part of the
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published benchmark): the same test on the arm's condition bulk_counts (scoring genes, the 57 + 20
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donors), one whole-PBMC top-50 list, compared with every type's observed list.
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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).
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paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/contrast.csv
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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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trunk4x,T cell,mean_cp10k,3210,0.2870702714037037,0.28417788192518073,0.2665216587387086,0.11716962712798713,57,20,57,20,279996,73456,,,,,,,,,,,
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trunk4x,T cell,pseudobulk_log1p_cpm,3210,0.3881076237989945,0.2528077636701587,0.35385159440330893,0.21164730128627224,57,20,57,20,279996,73456,,,,,,,,,,,
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trunk4x,B cell,mean_cp10k,3205,0.3538743444538308,0.29788887309509887,0.34815898557466446,0.07542510877815839,57,20,57,20,95354,21242,,,,,,,,,,,
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| 5 |
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trunk4x,B cell,pseudobulk_log1p_cpm,3205,0.30178331034532974,0.2708791238271775,0.2871833190925479,0.0984981902842706,57,20,57,20,95354,21242,,,,,,,,,,,
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| 6 |
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trunk4x,Monocyte,mean_cp10k,3210,0.27032632846200105,0.5567220126394566,0.23097644212449236,0.14454135322602896,57,20,57,20,107968,12711,,,,,,,,,,,
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| 7 |
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trunk4x,Monocyte,pseudobulk_log1p_cpm,3210,0.337796225280278,0.5978473455208166,0.267481264535883,0.2147416079559134,57,20,57,20,107968,12711,,,,,,,,,,,
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| 8 |
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trunk4x,NK cell,mean_cp10k,3161,0.22430722630504263,0.20828713319632092,0.21489574474443748,0.07031066343780458,57,20,57,20,45869,4058,,,,,,,,,,,
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| 9 |
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trunk4x,NK cell,pseudobulk_log1p_cpm,3161,0.20436086225111227,0.05503020199393339,0.20223126117200582,0.048604123366663594,57,20,57,20,45869,4058,,,,,,,,,,,
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| 10 |
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trunk4x,T cell,welch_top50_deg,4243,,,,,57,20,57,20,279996,73456,True,12,0.13636363636363635,50,50,1505,812,6,0.06382978723404255,50,1075
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| 11 |
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trunk4x,B cell,welch_top50_deg,4243,,,,,57,20,57,20,95354,21242,True,12,0.13636363636363635,50,50,1011,182,4,0.041666666666666664,50,1075
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trunk4x,Monocyte,welch_top50_deg,4243,,,,,57,20,57,20,107968,12711,True,3,0.030927835051546393,50,50,2342,271,10,0.1111111111111111,50,1075
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trunk4x,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,45869,4058,True,1,0.010101010101010102,50,50,1182,100,5,0.05263157894736842,50,1075
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| 14 |
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trunk4x,DC,welch_top50_deg,4243,,,,,53,20,42,19,3221,225,True,0,0.0,50,0,1483,0,2,0.02040816326530612,50,1075
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| 15 |
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panel_matched_1x,T cell,mean_cp10k,3210,0.3602182910286651,0.28417788192518073,0.3400110989342174,0.13020119193174146,57,20,57,20,279996,74218,,,,,,,,,,,
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| 16 |
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panel_matched_1x,T cell,pseudobulk_log1p_cpm,3210,0.45158356448300535,0.2528077636701587,0.41767589077147815,0.2310398943266545,57,20,57,20,279996,74218,,,,,,,,,,,
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| 17 |
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panel_matched_1x,B cell,mean_cp10k,3205,0.4176046858759658,0.29788887309509887,0.41205499129843615,0.08636238355830167,57,20,57,20,95354,22358,,,,,,,,,,,
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| 18 |
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panel_matched_1x,B cell,pseudobulk_log1p_cpm,3205,0.4161311784237891,0.2708791238271775,0.4001685845369041,0.12538214925458604,57,20,57,20,95354,22358,,,,,,,,,,,
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| 19 |
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panel_matched_1x,Monocyte,mean_cp10k,3210,0.20520843935549252,0.5567220126394566,0.16816981131552372,0.11946802992293727,57,20,55,20,107968,12045,,,,,,,,,,,
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| 20 |
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panel_matched_1x,Monocyte,pseudobulk_log1p_cpm,3210,0.2846560526817334,0.5978473455208166,0.22915307336444551,0.17354250627580894,57,20,55,20,107968,12045,,,,,,,,,,,
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| 21 |
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panel_matched_1x,NK cell,mean_cp10k,3161,0.27321073661040374,0.20828713319632092,0.2696792589027904,0.046735348781462745,57,20,57,20,45869,3850,,,,,,,,,,,
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| 22 |
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panel_matched_1x,NK cell,pseudobulk_log1p_cpm,3161,0.17832966029489664,0.05503020199393339,0.17541012366389303,0.06451352245405642,57,20,57,20,45869,3850,,,,,,,,,,,
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| 23 |
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panel_matched_1x,T cell,welch_top50_deg,4243,,,,,57,20,57,20,279996,74218,True,13,0.14942528735632185,50,50,1505,416,6,0.06382978723404255,50,1075
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| 24 |
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panel_matched_1x,B cell,welch_top50_deg,4243,,,,,57,20,57,20,95354,22358,True,10,0.1111111111111111,50,50,1011,166,4,0.041666666666666664,50,1075
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| 25 |
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panel_matched_1x,Monocyte,welch_top50_deg,4243,,,,,57,20,55,20,107968,12045,True,1,0.01639344262295082,50,12,2342,12,10,0.1111111111111111,50,1075
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| 26 |
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panel_matched_1x,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,45869,3850,True,1,0.010101010101010102,50,50,1182,56,5,0.05263157894736842,50,1075
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| 27 |
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panel_matched_1x,DC,welch_top50_deg,4243,,,,,53,20,35,17,3221,308,True,0,0.0,50,0,1483,0,2,0.02040816326530612,50,1075
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| 28 |
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current_f01,T cell,mean_cp10k,3210,0.3475254712197549,0.28417788192518073,0.3352046413091245,0.09736022010926025,57,20,57,20,279996,74305,,,,,,,,,,,
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| 29 |
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current_f01,T cell,pseudobulk_log1p_cpm,3210,0.4743533046018932,0.2528077636701587,0.45185144719564235,0.17320230434917558,57,20,57,20,279996,74305,,,,,,,,,,,
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| 30 |
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current_f01,B cell,mean_cp10k,3205,0.4094875306359168,0.29788887309509887,0.42306369432943636,0.019150619895150427,57,20,57,20,95354,19751,,,,,,,,,,,
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| 31 |
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current_f01,B cell,pseudobulk_log1p_cpm,3205,0.34002496055742293,0.2708791238271775,0.34683235162023024,0.023066876232327588,57,20,57,20,95354,19751,,,,,,,,,,,
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| 32 |
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current_f01,Monocyte,mean_cp10k,3210,0.29244694076797595,0.5567220126394566,0.26620998956129155,0.13153414113945086,57,20,55,20,107968,12960,,,,,,,,,,,
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| 33 |
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current_f01,Monocyte,pseudobulk_log1p_cpm,3210,0.284202090246288,0.5978473455208166,0.22886991857128774,0.17313507286968016,57,20,55,20,107968,12960,,,,,,,,,,,
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| 34 |
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current_f01,NK cell,mean_cp10k,3161,0.26162285691709686,0.20828713319632092,0.2511897332853757,0.08035409317555907,57,20,57,20,45869,4274,,,,,,,,,,,
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| 35 |
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current_f01,NK cell,pseudobulk_log1p_cpm,3161,0.22725742797836326,0.05503020199393339,0.2252028079607215,0.04830804057273376,57,20,57,20,45869,4274,,,,,,,,,,,
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| 36 |
+
current_f01,T cell,welch_top50_deg,4243,,,,,57,20,57,20,279996,74305,True,13,0.14942528735632185,50,50,1505,568,6,0.06382978723404255,50,1075
|
| 37 |
+
current_f01,B cell,welch_top50_deg,4243,,,,,57,20,57,20,95354,19751,True,11,0.12359550561797752,50,50,1011,271,4,0.041666666666666664,50,1075
|
| 38 |
+
current_f01,Monocyte,welch_top50_deg,4243,,,,,57,20,55,20,107968,12960,True,1,0.010101010101010102,50,50,2342,507,10,0.1111111111111111,50,1075
|
| 39 |
+
current_f01,NK cell,welch_top50_deg,4243,,,,,57,20,57,20,45869,4274,True,4,0.041666666666666664,50,50,1182,109,5,0.05263157894736842,50,1075
|
| 40 |
+
current_f01,DC,welch_top50_deg,4243,,,,,53,20,36,18,3221,284,True,0,0.0,50,0,1483,0,2,0.02040816326530612,50,1075
|
| 41 |
+
scsemi,T cell,mean_cp10k,3210,0.3687857657303425,0.28417788192518073,0.3107179554834221,0.2965810368595624,57,20,57,20,279996,70397,,,,,,,,,,,
|
| 42 |
+
scsemi,T cell,pseudobulk_log1p_cpm,3210,0.24157790028155127,0.2528077636701587,0.21135516512175404,0.15669783091425477,57,20,57,20,279996,70397,,,,,,,,,,,
|
| 43 |
+
scsemi,B cell,mean_cp10k,3205,0.27034367544047044,0.29788887309509887,0.23935212477874757,0.14908769419727183,57,20,56,20,95354,16783,,,,,,,,,,,
|
| 44 |
+
scsemi,B cell,pseudobulk_log1p_cpm,3205,0.22091899797156925,0.2708791238271775,0.21286345495831324,0.06050367236616134,57,20,56,20,95354,16783,,,,,,,,,,,
|
| 45 |
+
scsemi,Monocyte,mean_cp10k,3210,0.256947219101129,0.5567220126394566,0.260145805694905,0.07444293393764446,57,20,54,20,107968,17045,,,,,,,,,,,
|
| 46 |
+
scsemi,Monocyte,pseudobulk_log1p_cpm,3210,-0.02178388118289749,0.5978473455208166,-0.009467224228652434,-0.0237468644682939,57,20,54,20,107968,17045,,,,,,,,,,,
|
| 47 |
+
scsemi,NK cell,mean_cp10k,3161,0.3783494548514196,0.20828713319632092,0.3408211093157626,0.28015109399620514,57,20,45,17,45869,4185,,,,,,,,,,,
|
| 48 |
+
scsemi,NK cell,pseudobulk_log1p_cpm,3161,0.09856412377222237,0.05503020199393339,0.09121963948121495,0.15632250686620788,57,20,45,17,45869,4185,,,,,,,,,,,
|
| 49 |
+
scsemi,T cell,welch_top50_deg,4243,,,,,57,20,57,20,279996,70397,True,3,0.030927835051546393,50,50,1505,405,6,0.06382978723404255,50,1075
|
| 50 |
+
scsemi,B cell,welch_top50_deg,4243,,,,,57,20,56,20,95354,16783,True,0,0.0,50,3,1011,3,4,0.041666666666666664,50,1075
|
| 51 |
+
scsemi,Monocyte,welch_top50_deg,4243,,,,,57,20,54,20,107968,17045,True,0,0.0,50,0,2342,0,10,0.1111111111111111,50,1075
|
| 52 |
+
scsemi,NK cell,welch_top50_deg,4243,,,,,57,20,45,17,45869,4185,True,0,0.0,50,50,1182,141,5,0.05263157894736842,50,1075
|
| 53 |
+
scsemi,DC,welch_top50_deg,4243,,,,,53,20,56,19,3221,1537,True,0,0.0,50,31,1483,31,2,0.02040816326530612,50,1075
|
paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/per_donor.csv
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
paper-releases/pbisc-diffusion/updates/20261013T142727Z-trunk4x-sepsis-results/diffusion/dataset/server_1/model8/plans/trunk4x_lora_sepsis_20261013/scores/draw2/summary.json
ADDED
|
@@ -0,0 +1,1612 @@
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| 1 |
+
{
|
| 2 |
+
"schema": "paired_ft_scores.v1",
|
| 3 |
+
"dataset": "ye2026_sepsis",
|
| 4 |
+
"written_utc": "2026-10-13T13:38:04.278548+00:00",
|
| 5 |
+
"command": [
|
| 6 |
+
"/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py",
|
| 7 |
+
"--dataset",
|
| 8 |
+
"ye2026_sepsis",
|
| 9 |
+
"--reference",
|
| 10 |
+
"/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/reference",
|
| 11 |
+
"--out",
|
| 12 |
+
"/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/scores/draw2",
|
| 13 |
+
"--threads",
|
| 14 |
+
"4",
|
| 15 |
+
"--donors",
|
| 16 |
+
"/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/scores/draw2/test_samples.txt",
|
| 17 |
+
"--case-groups",
|
| 18 |
+
"adult_sepsis,ped_sepsis",
|
| 19 |
+
"--control-groups",
|
| 20 |
+
"adult_HC,adult_ICU_ctrl,ped_HC,ped_ICU_ctrl",
|
| 21 |
+
"--arm",
|
| 22 |
+
"trunk4x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw2/trunk4x.npz",
|
| 23 |
+
"--condition",
|
| 24 |
+
"trunk4x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw2/ye2026_sepsis/test_raw.npz",
|
| 25 |
+
"--arm",
|
| 26 |
+
"panel_matched_1x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw2/panel_matched_1x.npz",
|
| 27 |
+
"--condition",
|
| 28 |
+
"panel_matched_1x=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw2/ye2026_sepsis/test_raw.npz",
|
| 29 |
+
"--arm",
|
| 30 |
+
"current_f01=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw2/current_f01.npz",
|
| 31 |
+
"--condition",
|
| 32 |
+
"current_f01=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw2/ye2026_sepsis/test_raw.npz",
|
| 33 |
+
"--arm",
|
| 34 |
+
"scsemi=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/projected/draw2/scsemi.npz",
|
| 35 |
+
"--condition",
|
| 36 |
+
"scsemi=/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/data/draw2/ye2026_sepsis/test_raw.npz"
|
| 37 |
+
],
|
| 38 |
+
"arms": [
|
| 39 |
+
"trunk4x",
|
| 40 |
+
"panel_matched_1x",
|
| 41 |
+
"current_f01",
|
| 42 |
+
"scsemi"
|
| 43 |
+
],
|
| 44 |
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"donors": [
|
| 45 |
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"Abd-PS04",
|
| 46 |
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"Abd-PS05",
|
| 47 |
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"Abd-PS07",
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| 48 |
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"Abd-PS19",
|
| 49 |
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"Abd-PS20",
|
| 50 |
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"Abd-PS23",
|
| 51 |
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"Abd-PS27",
|
| 52 |
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"Abd-S102",
|
| 53 |
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| 54 |
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| 55 |
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| 56 |
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| 57 |
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| 59 |
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| 61 |
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| 62 |
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| 63 |
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| 64 |
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| 65 |
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| 66 |
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| 67 |
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| 108 |
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| 109 |
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| 110 |
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| 111 |
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| 112 |
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| 113 |
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| 114 |
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| 115 |
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| 116 |
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| 118 |
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| 119 |
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| 120 |
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| 121 |
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| 122 |
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"cache": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/trunk4x_lora_sepsis_20261013/reference",
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},
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| 147 |
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|
| 148 |
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"enabled": true,
|
| 149 |
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"rule": "explicit --case-groups / --control-groups",
|
| 150 |
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"group_source": "features groups",
|
| 151 |
+
"case_groups": [
|
| 152 |
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"adult_sepsis",
|
| 153 |
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|
| 154 |
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],
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| 156 |
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|
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|
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],
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| 324 |
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| 343 |
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|
| 344 |
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|
| 345 |
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|
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|
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|
| 349 |
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|
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|
| 351 |
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|
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|
| 400 |
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|
| 401 |
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},
|
| 402 |
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"deg_definition": {
|
| 403 |
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"enabled": true,
|
| 404 |
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"score": "top-50 DEG overlap per cell type (benchmark total_top50_overlap, Welch backend)",
|
| 405 |
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"source": "/home/sj_server_1/PBISC/immun-pbmc-fm/bulk2sc-paper/scripts/evaluate_generated_vs_truth.py",
|
| 406 |
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|
| 408 |
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|
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|
| 410 |
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],
|
| 411 |
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|
| 412 |
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"top_n": 50,
|
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