diff --git a/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/slides_spec.json b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/slides_spec.json new file mode 100644 index 0000000000000000000000000000000000000000..50332c2c4471123f79bad741e9f30c9ff5f241ee --- /dev/null +++ b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/slides_spec.json @@ -0,0 +1,1362 @@ +{ + "deck": { + "output_pptx": "PBISC-Diffusion_paper_summary_20260930.pptx", + "paths": "figure paths are relative to the deck folder", + "note": "footnote = on-slide text (max 2 lines, may be a shortened copy); footnote_full = section author's full text, put in speaker notes when it differs. Generated slide text: section 9 main slides from data/sec09_slide_text.json (sec09_index), other generated slides from the file named in text_source (data/sec09_extra_slide_text.json, data/sec07_rank_sweep_slide_text.json); refresh with tools/sync_sec09_text.py after rerunning the scripts that write them." + }, + "slides": [ + { + "kind": "title", + "title": "PBISC-Diffusion: bulk-to-single-cell generation", + "subtitle": "Paper summary · 2026-09-30, updated 2026-10-02" + }, + { + "kind": "agenda", + "title": "Outline", + "items": [ + "Model overview: diffusion from bulk to cells", + "Backbone: SDD, scDiffusion, bulk conditioning", + "Architecture ablation: width, context, gene panel", + "How we score cells; SLE benchmark vs scSemiProfiler", + "Real bulk ≠ pseudobulk: why zero-shot fails", + "Fine-tuning on paired bulk–sc data; evaluation design", + "LoRA: compute, rank and K ablations", + "How many pairs, and do other cohorts’ pairs transfer", + "Paired-cohort validation vs scSemiProfiler", + "Next: cohorts with a paired subset (planned)" + ] + }, + { + "kind": "content", + "section": "1", + "section_label": "1 Model overview", + "title": "PBISC-Diffusion: bulk in, cells out", + "layout": "full", + "figures": [ + "figures/sec01_slide_a.png" + ], + "keywords": [ + "Train pair: K-cell pseudobulk → each member cell" + ], + "footnote": "Fig. 1b redraw. Final model corpus: 33.1M train cells; K = 1–4,000 cells per pseudobulk.", + "footnote_full": "Top: deck redraw of manuscript Fig. 1b (larger labels; cells differ by x_T and the Bernoulli support draw, Methods 'Support read-out'). Bottom: corpus of final run f01_6k_1x_seed20260627 (index fingerprint matches). Cells per dataset = base + overlay rows (Model 7 e2_full_source/e2_full_index manifests); 33.1M cells in the donor-level 80/10/10 train split. Registered K grid = 8,192 recipes/epoch. Plus 3,658 top-up recipes over 16 epochs (196-283 per epoch, K 1-4,000), so every train cell is a target once (ALLCELL_DATA_CONTRACT.json coverage_after_topup).", + "sources": "tools/sec01_overview.py; data/sec01_model_numbers.csv, data/sec01_corpus_datasets.csv, data/sec01_recipe_k.csv", + "refs": [ + "Ostheimer 2025, arXiv", + "Song 2021, ICLR" + ], + "refs_full": [ + "Ostheimer et al. Sparse Data Diffusion for scientific simulations in biology and physics. arXiv:2502.02448 (2025)", + "Song, Meng & Ermon. Denoising Diffusion Implicit Models. ICLR 2021" + ] + }, + { + "kind": "content", + "section": "1", + "section_label": "1 Model overview", + "title": "Denoiser: joint value + support diffusion", + "layout": "diagram", + "figures": [ + "figures/sec01_fig1a_denoiser.png" + ], + "keywords": [ + "6,000 genes · 54.2M params · Bernoulli support draw" + ], + "footnote": "Fig. 1a redraw. Final model f01 6k 1x, epoch 16, EMA; 50 DDIM steps.", + "footnote_full": "Deck redraw of manuscript Fig. 1a (larger labels, legend dropped). Purple dashed = soft self-conditioning feedback; gold dashed = context to all blocks. Final model f01_6k_1x_seed20260627, checkpoint_epoch_016, EMA; 7 blocks 1024-1024-512-256-512-1024-1024 (effective_config.json, RUN_STATUS.json). Sampling: 50 DDIM steps, η = 0, drawn support read-out, keep gene if u < σ(logit) (methods.tex l.58, l.99). 16 epochs = 16 disjoint 8,192-recipe slots = one pass over the 131,072-recipe pool (manuscript: part 16).", + "sources": "tools/sec01_overview.py; data/sec01_model_numbers.csv, data/sec01_fig1_label_pt.csv", + "refs": [ + "Ostheimer 2025, arXiv" + ], + "refs_full": [ + "Ostheimer et al. Sparse Data Diffusion for scientific simulations in biology and physics. arXiv:2502.02448 (2025)" + ] + }, + { + "kind": "content", + "section": "2", + "section_label": "2 Backbone", + "title": "Backbone: SDD → PBISC-Diffusion", + "layout": "diagram", + "figures": [ + "figures/sec02_backbone_compare.png" + ], + "keywords": [ + "Kept from SDD: value + bit, skip-MLP", + "Changed: bulk condition, masked loss, Bernoulli read-out" + ], + "footnote": "SDD: Ostheimer et al., arXiv 2502.02448. scDiffusion: Luo et al., Bioinformatics 2024.", + "sources": "tools/sec02_backbone.py; data/sec02_backbone_compare.csv, data/sec02_slide_text.csv, data/refs/", + "footnote_full": "Luo et al., Bioinformatics 2024; Ostheimer et al., arXiv 2502.02448 (code 1d5423c). PBISC: final model f01 6k 1x, part 16. σ = sigmoid; Bernoulli is the final read-out; the logit > 0 cut is also reported.", + "refs": [ + "Ostheimer 2025, arXiv", + "Luo 2024, Bioinformatics" + ], + "refs_full": [ + "Ostheimer et al. Sparse Data Diffusion for scientific simulations in biology and physics. arXiv:2502.02448 (2025)", + "Luo et al. scDiffusion: conditional generation of high-quality single-cell data using diffusion model. Bioinformatics 40(9):btae518 (2024)" + ] + }, + { + "kind": "content", + "section": "2", + "section_label": "2 Backbone", + "title": "Bulk conditioning", + "layout": "diagram", + "figures": [ + "figures/sec02_bulk_conditioning.png" + ], + "keywords": [ + "Same bulk condition for every generated cell", + "Final: no cell types, proportions, sum constraint", + "18,006 → 512 context into every block" + ], + "footnote": "Final model f01 6k 1x. Bar heights schematic.", + "sources": "tools/sec02_backbone.py; data/sec02_bulk_conditioning.csv, data/sec02_slide_text.csv", + "footnote_full": "Final model f01 6k 1x, part 16. Bar heights schematic." + }, + { + "kind": "content", + "section": "3", + "section_label": "3 Architecture ablation", + "title": "Width & context ablation: loss saturation", + "layout": "full", + "figures": [ + "figures/sec03_width_loss.png" + ], + "keywords": [ + "Trunk x4: val loss −31% (0.197 → 0.136)", + "ctx x4, prop hint: loss = 1x", + "1x flat E8; trunk x4 still falling" + ], + "footnote": "Old 6k panel (1% floor); one validation set. 16x: memory-floor stop after part 2.", + "say": "All arms share the 6k panel (1% floor), corpus, seed and schedule; validation = 192 recipes x 64 cells. ctx x4, both x4 and 16x were read from server_2 snapshots.", + "sources": "tools/sec03_width_loss.py; data/sec03_width_loss.csv, data/sec03_width_saturation.csv, data/sec03_width_loss_headline.csv" + }, + { + "kind": "content", + "section": "3", + "section_label": "3 Architecture ablation", + "title": "Width & context ablation: benchmark", + "layout": "full", + "figures": [ + "figures/sec03_width_bench.png" + ], + "keywords": [ + "trunk x4: loss −31%, no DEG gain", + "Prop hint: oracle = predicted, L1 worse", + "1x kept: ties ctx x4, 54M vs 102M" + ], + "footnote": "SLE, 42 donors, 2,980 genes; old 6k panel. ctx x4: better L1 (0.161 vs 0.197).", + "say": "Bars = mean of parts 14-16 with the cut p > 0.5; diamonds = Bernoulli read-out (4 width arms only). Prop hint = 9 L1 cell-type proportions; older panel, not the final model.", + "sources": "tools/sec03_width_bench.py; data/sec03_width_bench.csv, data/sec03_width_bench_summary.csv, data/sec03_width_bench_headline.csv" + }, + { + "kind": "content", + "section": "3", + "section_label": "3 Architecture ablation", + "title": "Gene context: 2k / 3k / 6k / 15k", + "layout": "full", + "figures": [ + "figures/sec03_panel.png" + ], + "keywords": [ + "6k ≈ 3k: 3k r, DEG inside 6k seed range", + "2k best L1; 15k worst r, L1", + "6k kept: first panel with CLEC9A (cDC1)" + ], + "footnote": "SLE, 42 donors, 1,006 genes in every panel. 6k: 5 seeds; base seed best of 5 on r and DEG.", + "say": "Runs f01_{2k,3k,6k,p15k}_1x, part 16, Bernoulli, EMA, DDIM 50. Different gene axis from the previous slide, so the two slides cannot be compared. Loss curves are divided by each panel's own part-16 value.", + "sources": "tools/sec03_panel.py; data/sec03_panel_bench.csv, data/sec03_panel_seeds.csv, data/sec03_panel_loss.csv, data/sec03_panel_headline.csv" + }, + { + "kind": "content", + "section": "4", + "title": "How we score generated cells", + "layout": "full", + "figures": [ + "figures/sec04_metrics_overview.png" + ], + "keywords": [ + "Cells, composition and disease signal, separately", + "Every metric next to its control or ceiling", + "Same donors and reference cells for every method" + ], + "footnote": "Definitions follow the cited papers; scorers: tools/score_ft.py, extra_metrics.py, contrast_extra.py.", + "sources": "tools/sec04_metrics_overview.py; data/sec04_metrics_overview.csv", + "refs_full": [ + "Kynkäänniemi et al. Improved precision and recall metric for assessing generative models. NeurIPS 2019", + "Naeem et al. Reliable fidelity and diversity metrics for generative models. ICML 2020", + "Lopez-Paz & Oquab. Revisiting classifier two-sample tests. ICLR 2017", + "Gretton et al. A kernel two-sample test. JMLR 13:723-773 (2012)", + "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1", + "Roohani, Huang & Leskovec. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol 42:927-935 (2024). doi:10.1038/s41587-023-01905-6", + "Viñas Torné et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nat Biotechnol 44:1050-1059 (2026). doi:10.1038/s41587-025-02777-8", + "Li et al. A systematic comparison of single-cell perturbation response prediction models. Sci Adv 12:eaed3414 (2026). doi:10.1126/sciadv.aed3414", + "Squair et al. Confronting false discoveries in single-cell differential expression. Nat Commun 12:5692 (2021). doi:10.1038/s41467-021-25960-2", + "Law, Chen, Shi & Smyth. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29 (2014)", + "Irizarry et al. Multiple-laboratory comparison of microarray platforms. Nat Methods 2:345-350 (2005). doi:10.1038/nmeth756" + ], + "section_label": "4 Metrics + SLE benchmark" + }, + { + "kind": "content", + "section": "4", + "section_label": "4 Metrics + SLE benchmark", + "title": "SLE benchmark: PBISC vs scSemiProfiler", + "layout": "full", + "figures": [ + "figures/sec04_sle_benchmark.png" + ], + "keywords": [ + "42 held-out donors, sc-pseudobulk input", + "Beats scSemiProfiler on all three metrics", + "Bulk logFC alone scores higher (slide 13)" + ], + "footnote": "GSE135779, 42 held-out donors (30 SLE / 12 HC). PBISC: 5 seeds, mean ± SD; scSemiProfiler: 1 run.", + "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_benchmark.csv, data/sec04_sle_numbers.csv", + "footnote_full": "GSE135779, 42 held-out donors (30 SLE / 12 HC). PBISC f01 6k E16, 5 seeds (mean ± SD); baselines 1 draw. scSemiProfiler also gets real sc of 14 in-cohort donors (1,500 cells each). Dashed: input bulk logFC copied to every type (broadcast slide).", + "refs": [ + "Wang 2024, Nat Commun", + "Xu 2021, Mol Syst Biol" + ], + "refs_full": [ + "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1", + "Xu et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol Syst Biol 17:e9620 (2021)" + ] + }, + { + "kind": "content", + "section": "4", + "section_label": "4 Metrics + SLE benchmark", + "title": "SLE benchmark: per cell type", + "layout": "full", + "figures": [ + "figures/sec04_sle_per_celltype.png" + ], + "keywords": [ + "Beats scSemiProfiler in T, B, Mono, NK", + "Loses in Other; DC r ties, fewer DEGs", + "DC: ~15 generated cells per donor" + ], + "footnote": "Same 42 donors, 1,006 genes. PBISC: 5 seeds, mean ± SD; scSemiProfiler: 1 run. Other: top-50 of 12.", + "say": "PBISC's DC top list in the base seed has only 13 genes, because few DC cells are generated.", + "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_per_celltype.csv, data/sec04_sle_numbers.csv", + "footnote_full": "Same 42 donors, 1,006 genes. PBISC 5 seeds (mean ± SD), baselines 1 draw. Tick %: share of 251,179 real cells. Other top-50 is out of 12 truth genes. Bulk copy: r 0.68, top-50 113 (broadcast slide).", + "refs": [ + "Wang 2024, Nat Commun" + ], + "refs_full": [ + "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1" + ] + }, + { + "kind": "content", + "section": "4", + "section_label": "4 Metrics + SLE benchmark", + "title": "Bulk broadcast baseline", + "layout": "full", + "figures": [ + "figures/sec04_sle_broadcast.png" + ], + "keywords": [ + "Bulk logFC copied: r 0.68 vs 0.37", + "Type-specific part: partial r 0.21", + "scSemiProfiler: r 0.32, partial r 0.17" + ], + "footnote": "SLE, 42 donors, 1,006 genes. Partial r: both logFCs residualised on bulk logFC.", + "sources": "tools/sec04_sle_benchmark.py; data/sec04_sle_broadcast.csv, data/sec04_sle_numbers.csv", + "footnote_full": "SLE held-out, 42 donors, 1,006 genes; PBISC 5 seeds. Partial r: truth and method logFC residualised on bulk logFC. Broadcast has no residual, so it is not scored. Split-half and BayesPrism checks: sle_broadcast/RESULTS.md.", + "refs": [ + "Viñas Torné 2026, Nat Biotechnol", + "Ahlmann-Eltze 2025, Nat Methods", + "Chu 2022, Nat Cancer" + ], + "refs_full": [ + "Viñas Torné et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nat Biotechnol 44:1050-1059 (2026). doi:10.1038/s41587-025-02777-8", + "Ahlmann-Eltze, Huber & Anders. Deep-learning-based gene perturbation effect prediction does not yet outperform simple linear baselines. Nat Methods 22:1657-1661 (2025). doi:10.1038/s41592-025-02772-6", + "Chu et al. Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology. Nat Cancer 3:505-517 (2022)" + ] + }, + { + "kind": "content", + "section": "5", + "section_label": "5 Real bulk vs pseudobulk", + "title": "Real bulk ≠ pseudobulk (model input)", + "layout": "figure_keywords", + "figures": [ + "figures/sec05_condition_space.png" + ], + "keywords": [ + "Real bulk: 2.46 D from own pseudobulk", + "95% of squared offset is one shift", + "Shift removed (diagnostic): own donor 7.5% → 75.7%" + ], + "footnote": "ImMicro, 107 healthy donors. Model input space (log1p CPM). D = median donor-to-donor distance.", + "say": "The two pseudobulk clusters on PC1 are sc pools DB1-3 vs DB4-7, so the pairing lines cross mainly because of sc batch. Shift removal subtracts the mean offset of the other 106 donors; a bulk-only deployment would not have them.", + "sources": "tools/sec05_condition_space.py; data/sec05_condition_space_numbers.csv, data/sec05_condition_space_coords.csv", + "footnote_full": "ImMicro, 107 healthy donors. Model inputs (log1p CPM, 5,508 genes), not learned ctx. D = median donor-to-donor distance; PCA-30 values are lower bounds (full space 3.19 D, 0.91 shared). Shift removal is a diagnostic that uses paired pseudobulks. Pseudobulk PC1 is sc pool batch (pool R² 0.96 on PC1)." + }, + { + "kind": "content", + "section": "5", + "section_label": "5 Real bulk vs pseudobulk", + "title": "Same shift in three paired cohorts", + "layout": "full", + "figures": [ + "figures/sec05_shift_cohorts.png" + ], + "keywords": [ + "Shared shift in all 3 cohorts" + ], + "footnote": "Model inputs only, no generation. PCA fitted per cohort on sc pseudobulks.", + "sources": "tools/sec05_shift_cohorts.py; data/sec05_shift_cohorts.csv", + "footnote_full": "Model inputs only, no generation. PCA is fitted on each cohort's sc pseudobulks (Arunachalam: 11 PCs, n = 12). D uses same-condition pairs only for Randolph and Arunachalam. Shift removal is a leave-one-out diagnostic." + }, + { + "kind": "content", + "section": "5", + "section_label": "5 Real bulk vs pseudobulk", + "title": "Consequence: cells off-manifold", + "layout": "two_figures", + "figures": [ + "figures/sec05_cells_umap.png", + "figures/sec05_cells_metrics.png" + ], + "keywords": [ + "Monocytes: raw 30.6% vs real 16.7%", + "Same noise: 19% of T cells become monocytes", + "Precision: raw 0.36 vs pseudobulk 0.92" + ], + "footnote": "ImMicro, 107 donors; same starting noise for every input. Coverage: raw 0.08, sc pseudobulk 0.51.", + "footnote_full": "ImMicro, 107 donors. Final model f01 6k 1× E16 (DDIM 50, EMA); each cell starts from the same noise under every input. Precision/coverage from one scoring call. Composition L1: raw 0.455, pseudobulk 0.166, cohort-mean null 0.161. The UMAP is fitted on real cells and overstates overlap.", + "sources": "tools/sec05_cells_offmanifold.py; data/sec05_cells_metrics.csv, data/sec05_cells_umap_coords.csv", + "refs": [ + "Kynkäänniemi 2019, NeurIPS", + "Naeem 2020, ICML" + ], + "refs_full": [ + "Kynkäänniemi et al. Improved precision and recall metric for assessing generative models. NeurIPS 2019", + "Naeem et al. Reliable fidelity and diversity metrics for generative models. ICML 2020" + ] + }, + { + "kind": "content", + "section": "5", + "section_label": "5 Real bulk vs pseudobulk", + "title": "Consequence: disease direction flips", + "layout": "figure_keywords", + "figures": [ + "figures/sec05_behcet_direction.png" + ], + "keywords": [ + "CD16 gap: raw −3.97 vs observed +4.94", + "C1Q flip vanishes at common depth", + "sc pseudobulk: right sign in 5/5 seeds" + ], + "footnote": "Behçet. Bulk GSE198533 (9 BD / 10 HC); sc GSE198616 (4 / 4, different people) gives observed and pseudobulk.", + "say": "Raw bulk gives the wrong sign for CD16 and C1Q in 5/5 seeds. At one common monocyte depth the C1Q gap is −0.08 ± 0.09 pp (1/5 seeds positive), so the C1Q flip is a depth effect; the CD16 flip stays at −3.59. sc pseudobulk of the 8 sc donors gives the observed sign in 5/5 seeds (CD16 +5.48, C1Q +1.82). Observed p is descriptive (4 vs 4). No paired data in this cohort, so no fine-tune here.", + "sources": "tools/sec05_behcet_direction.py; data/sec05_behcet_direction.csv", + "footnote_full": "Behçet, final model. Bulk GSE198533 (BD 9, HC 10), 5,000 cells per sample; observed = sc GSE198616 (BD 4, HC 4, different people; descriptive p). At common depth, raw CD16 is −3.59 (seed 0) and raw C1Q is −0.08 ± 0.09 pp." + }, + { + "kind": "content", + "section": "6", + "section_label": "6 Paired fine-tuning", + "title": "Fine-tuning with paired bulk + sc", + "layout": "diagram", + "figures": [ + "figures/sec06_ft_method.png" + ], + "keywords": [ + "LoRA 2–14 min vs pretraining 15.9 h" + ], + "footnote": "Base: final model f01 6k 1x E16. Times from 40 LoRA runs on this host.", + "sources": "tools/sec06_finetune_method.py; data/sec06_ft_method.csv", + "footnote_full": "Receipts: immicro/fold0/real_lora and ye2026_sepsis/fewshot_n5/draw0/lora RUN.json. Base: f01_6k_1x E16. Times: 40 local LoRA runs.", + "refs": [ + "Hu 2022, ICLR" + ], + "refs_full": [ + "Hu et al. LoRA: Low-Rank Adaptation of Large Language Models. ICLR 2022" + ] + }, + { + "kind": "content", + "section": "6", + "section_label": "6 Paired fine-tuning", + "title": "Evaluation design", + "layout": "full", + "figures": [ + "figures/sec06_eval_design.png" + ], + "keywords": [ + "Donor-level splits; test donors never trained", + "5-fold: other folds · few: 5 donors", + "Giroux few-pair trains 12–14 of 24 pairs" + ], + "footnote": "Bars: Ye 2026 sepsis. QC-failed pairs excluded.", + "sources": "tools/sec06_finetune_method.py; data/sec06_eval_design.csv, data/sec06_eval_design_sepsis_roles.csv", + "footnote_full": "Counts: data//folds.csv and fewshot_n5/DRAWS.json; QC-failed pairs (fold −1) excluded. Bars: Ye 2026 sepsis." + }, + { + "kind": "content", + "section": "7", + "section_label": "7 LoRA ablations", + "title": "Full vs LoRA vs cond.-encoder-only", + "layout": "full", + "figures": [ + "figures/sec07_ft_modes.png" + ], + "keywords": [ + "LoRA ≈ full FT, 42× fewer params", + "Cond.-encoder-only ≈ ZS + cal. on ImMicro", + "LoRA beats both on coverage" + ], + "footnote": "Fold 0, cond.-enc. vs ZS + cal.: coverage 0.73 vs 0.73 / 0.74 vs 0.63, L1 p 0.06 / 0.93 (ImMicro / sepsis).", + "sources": "tools/sec07_ft_modes.py; data/sec07_ft_modes_accuracy.csv, data/sec07_ft_modes_compute.csv, data/sec07_ft_modes_wilcoxon.csv", + "footnote_full": "Base f01_6k_1x E16. ImMicro / sepsis; paired Wilcoxon on donor values, uncorrected. Full vs LoRA L1 p 0.25 / 0.71 (pooled); 42× fewer trainable params (1.3 M). Zero-shot + calibration: α_g = mean CPM(pseudobulk_g) / mean CPM(bulk_g) over the fold's 85–86 / 65–66 paired training donors (train + inner-val); bulk CPM × α, renormalised. The sc-only FT never saw real bulk, so it is fed calibrated bulk. Raw → calibrated zero-shot (pooled): L1 0.45 → 0.20 / 0.51 → 0.40, coverage 0.19 → 0.53 / 0.07 → 0.43, sepsis logFC r −0.01 → 0.12. LoRA vs ZS + cal. (pooled): coverage 0.78 vs 0.53 / 0.83 vs 0.43 (p 7e-19 / 4e-15); L1 0.16 vs 0.20 (p 2e-7) / 0.36 vs 0.40 (p 0.19); logFC r 0.53 vs 0.12. Fold 0, cond.-enc. vs ZS + cal.: L1 0.20 vs 0.17 (p 0.06) / 0.38 vs 0.39 (p 0.93), coverage 0.73 vs 0.73 (p 0.68) / 0.74 vs 0.63 (p 7e-4). Fold 0, LoRA: coverage 0.91 / 0.86; vs cond.-enc. p 4e-5 / 1e-3, vs ZS + cal. p 6e-5 / 3e-4; L1 vs cond.-enc. p 3e-5 / 0.52, vs ZS + cal. p 0.29 / 0.96. sc-only FT (+ cal.), pooled: coverage 0.62 / 0.73, logFC r 0.30. Open = fold-0 pilot; compare same marker only. Per-step time: runs early-stop differently.", + "refs": [ + "Hu 2022, ICLR" + ], + "refs_full": [ + "Hu et al. LoRA: Low-Rank Adaptation of Large Language Models. ICLR 2022" + ] + }, + { + "kind": "content", + "section": "7", + "section_label": "7 LoRA ablations", + "text_source": { + "file": "data/sec07_rank_sweep_slide_text.json", + "index": 0 + }, + "title": "LoRA rank: r = 4, 16, 64", + "layout": "full", + "figures": [ + "figures/sec07_rank_sweep.png" + ], + "keywords": [ + "ImMicro r64 vs r16: coverage 0.78 → 0.82", + "ImMicro r4 vs r16: L1 0.162 → 0.186", + "Kept r16; r64 precision 0.92 → 0.90" + ], + "footnote": "Sepsis, 58–59 pairs/fold: r64 logFC r 0.53 → 0.55, DEG 23 → 26; r4 0.48, DEG 18. Pooled 5-fold scores.", + "footnote_full": "LoRA fine-tune of the final model (f01 6k·1x·E16) with rank r and alpha = r, same recipe as the deck's r = 16 runs (early stop on 7–8 inner-val pairs, 20k step cap); n = training pairs, nested subsets; donor-level 5-fold CV, each pair scored once by its fold model (pooled call). Trainable parameters r4 324,056, r16 1,296,224, r64 5,184,896. ImMicro n = 5: r4: L1 0.162, P/C 0.937 / 0.789 (L1 vs r16 -0.009, Wilcoxon p 0.00088, 107 donors); r16: L1 0.171, P/C 0.946 / 0.839; r64: L1 0.177, P/C 0.946 / 0.847 (L1 vs r16 +0.005, Wilcoxon p 0.0076, 107 donors) | ImMicro n = 20: r4: L1 0.171, P/C 0.925 / 0.715 (L1 vs r16 -0.001, Wilcoxon p 0.32, 107 donors); r16: L1 0.172, P/C 0.920 / 0.755; r64: L1 0.172, P/C 0.905 / 0.794 (L1 vs r16 -0.001, Wilcoxon p 0.66, 107 donors) | ImMicro n = 78–79 (every training donor of the fold): r4: L1 0.186, P/C 0.933 / 0.712 (L1 vs r16 +0.024, Wilcoxon p 9.8e-09, 107 donors); r16: L1 0.162, P/C 0.924 / 0.784; r64: L1 0.161, P/C 0.904 / 0.816 (L1 vs r16 -0.001, Wilcoxon p 0.89, 107 donors) | Sepsis n = 5: r4: L1 0.426, P/C 0.907 / 0.742, logFC r 0.27, DEG 2 (L1 vs r16 -0.002, Wilcoxon p 0.97, 82 donors); r16: L1 0.428, P/C 0.911 / 0.767, logFC r 0.29, DEG 5; r64: L1 0.429, P/C 0.906 / 0.766, logFC r 0.31, DEG 1 (L1 vs r16 +0.001, Wilcoxon p 0.66, 82 donors) | Sepsis n = 20: r4: L1 0.378, P/C 0.929 / 0.769, logFC r 0.31, DEG 14 (L1 vs r16 -0.003, Wilcoxon p 0.58, 82 donors); r16: L1 0.380, P/C 0.927 / 0.807, logFC r 0.37, DEG 18; r64: L1 0.376, P/C 0.911 / 0.808, logFC r 0.43, DEG 18 (L1 vs r16 -0.004, Wilcoxon p 0.046, 82 donors) | Sepsis n = 58–59 (every training donor of the fold): r4: L1 0.363, P/C 0.923 / 0.789, logFC r 0.48, DEG 18 (L1 vs r16 +0.002, Wilcoxon p 0.77, 82 donors); r16: L1 0.362, P/C 0.926 / 0.829, logFC r 0.53, DEG 23; r64: L1 0.367, P/C 0.907 / 0.824, logFC r 0.55, DEG 26 (L1 vs r16 +0.005, Wilcoxon p 0.66, 82 donors). r64 vs r16 brings small gains in coverage and disease signal but slightly lower precision and no composition change; r4 is worse with many pairs; at n = 5 the ranks are mixed. r = 16 is kept. Scores: scores//lora_rank_20261001/pooled (r4, r64) and scores//pooled{,_deg} (r16); table: scores//rankcal_20261001.md.", + "sources": "tools/sec07_rank_sweep.py; data/sec07_rank_sweep.csv, data/sec07_rank_sweep_runs.csv; plans/paired_finetune_intradataset_20260927/scores//rankcal_20261001.md", + "numbers": [ + { + "value": "ImMicro 78–79 pairs/fold coverage r16 0.784 -> r64 0.816; precision 0.924 -> 0.904; L1 r16 0.162, r4 0.186 (Wilcoxon p 9.8e-09)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/pooled/summary.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/immicro/lora_rank_20261001/pooled/summary.json, per_donor.csv" + }, + { + "value": "Sepsis 58–59 pairs/fold logFC r r16 0.527, r64 0.553, r4 0.476; DEG 23 / 26 / 18", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json, contrast.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/lora_rank_20261001/pooled/summary.json, contrast.csv" + }, + { + "value": "trainable parameters: r4 324056, r16 1296224, r64 5184896", + "source": "runs//fold//RUN.json (trainable.n_trainable)" + } + ], + "refs": [ + "Hu 2022, ICLR" + ], + "refs_full": [ + "Hu et al. LoRA: Low-Rank Adaptation of Large Language Models. ICLR 2022" + ] + }, + { + "kind": "content", + "section": "7", + "section_label": "7 LoRA ablations", + "title": "K diversification: no gain", + "layout": "figure_keywords", + "figures": [ + "figures/sec07_k_aug.png" + ], + "keywords": [ + "No gain; keep single K 1000", + "Multi-K slightly worse on ImMicro (p 0.04)", + "Inner-val loss Δ ≤ 1.2e-4" + ], + "footnote": "LoRA r16, 5-fold pooled. p = paired Wilcoxon on donor L1, uncorrected.", + "sources": "tools/sec07_k_aug.py; data/sec07_k_aug.csv, data/sec07_k_aug_wilcoxon.csv, data/sec07_k_aug_inner_val_delta.csv", + "footnote_full": "LoRA r16 on f01_6k_1x E16, 5-fold pooled. p: paired Wilcoxon, donor L1 vs plain LoRA, uncorrected. Recipes arm: K 1000 real bulk + ½-batch pretraining pseudobulk, no multi-K." + }, + { + "kind": "content", + "section": "8", + "section_label": "8 Pairs needed", + "title": "Pairs needed: composition, coverage, training steps", + "layout": "full", + "figures": [ + "figures/sec08_pairs_realism.png" + ], + "keywords": [ + "No pairs: coverage 0.19 / 0.07; length fix 0.50 / 0.40", + "Calibration from n pairs: flat at 0.53 / 0.43", + "LoRA with 5 pairs: coverage 0.84 / 0.77" + ], + "footnote": "ImMicro / sepsis, 5-fold CV (107 / 82 donors). 0 = no pairs; squares = calibration fitted on the same n pairs.", + "footnote_full": "ImMicro / sepsis, donor-level 5-fold CV, pooled scoring (107 / 82 donors). x = LoRA training pairs per fold; each run also early-stops on 7–8 paired inner-val donors; the last point is every training donor of the fold (78–79 / 58–59; the other four folds minus inner-val, never the test fold). x = 0 uses no paired data: the base model fed the real bulk as measured (pale) and after exon-length correction (sand: counts divided by Ensembl union-exon kb before the CPM step; scores//zeroshot_tpm_20261002/pooled). Squares: zero-shot + gene-wise calibration with alpha fitted on exactly the pairs that LoRA point saw (cal_budget_20261001; the last square uses every training pair of the fold). Coverage ImMicro / sepsis: as measured 0.19 / 0.07, length-corrected 0.50 / 0.40, calibration 0.52–0.53 / 0.43–0.44 at every n, LoRA 5 pairs 0.84 / 0.77, every training donor 0.78 / 0.83. Composition L1: length correction does not fix it (ImMicro 0.45 → 0.42, sepsis 0.51 → 0.56).", + "say": "Keywords say +7; 2 of 5 sepsis folds use 8 inner-val pairs.", + "sources": "tools/sec08_pairs_needed.py; data/sec08_pairs_realism.csv, data/sec08_selected_steps.csv, data/sec08_slide_numbers.csv", + "refs": [ + "Kynkäänniemi 2019, NeurIPS", + "Naeem 2020, ICML" + ], + "refs_full": [ + "Kynkäänniemi et al. Improved precision and recall metric for assessing generative models. NeurIPS 2019", + "Naeem et al. Reliable fidelity and diversity metrics for generative models. ICML 2020" + ] + }, + { + "kind": "content", + "section": "8", + "section_label": "8 Pairs needed", + "title": "Pairs needed: donor and disease signal", + "layout": "figure_keywords", + "figures": [ + "figures/sec08_pairs_signal.png" + ], + "keywords": [ + "No pairs or calibration only: logFC r ≤ 0.12", + "LoRA logFC r above broadcast from 10 pairs", + "LoRA top-50 DEG ≥ broadcast from 40 pairs" + ], + "footnote": "Sepsis: 61 case vs 21 control. logFC r = mean over T / B / Mono / NK. 0 = no pairs.", + "footnote_full": "Sepsis 61 case vs 21 control, pooled 5-fold scoring. Disease logFC r: as measured −0.01, length-corrected 0.11, calibration from the same n pairs 0.08–0.12, LoRA 0.29 / 0.42 / 0.37 / 0.48 / 0.53 at 5 / 10 / 20 / 40 / 58–59 pairs; broadcast 0.32; zero-shot from sc pseudobulk 0.47. Top-50 DEG (Welch, sum over 5 types): as measured 9, length-corrected 11, calibration 7–12, LoRA 5 / 8 / 18 / 22 / 23, broadcast 21. ImMicro donor NK fraction r: as measured 0.49, length-corrected 0.49, calibration 0.50–0.53, LoRA 0.20 → 0.70.", + "sources": "tools/sec08_pairs_needed.py; data/sec08_pairs_signal.csv, data/sec08_slide_numbers.csv", + "refs": [ + "Roohani 2024, Nat Biotechnol", + "Li 2026, Sci Adv" + ], + "refs_full": [ + "Roohani, Huang & Leskovec. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol 42:927-935 (2024). doi:10.1038/s41587-023-01905-6", + "Li et al. A systematic comparison of single-cell perturbation response prediction models. Sci Adv 12:eaed3414 (2026). doi:10.1126/sciadv.aed3414" + ] + }, + { + "kind": "content", + "section": "8", + "section_label": "8 Pairs needed", + "layout": "full", + "sources": "tools/sec08_cross_cohort.py; data/sec08_cross_cohort.csv, data/sec08_cross_cohort_steps.csv; plans/paired_crosscohort_20261002/ (README, scores/aggregate.csv)", + "text_source": { + "file": "data/sec08_cross_cohort_slide_text.json", + "index": 0 + }, + "title": "Pairs from other cohorts do not transfer", + "figures": [ + "figures/sec08_cross_cohort.png" + ], + "keywords": [ + "Train on the other cohorts, test the held-out one", + "Calibration ties or beats them on composition in 4/5", + "Own-cohort pairs best on coverage 5/5, disease r 4/4" + ], + "footnote": "Leave-one-cohort-out, K = 1000; train on the other cohorts (+ Liu 2021), test the held-out one.", + "say": "Why one LoRA per cohort: a model trained on four other cohorts' pairs (plus Liu 2021) does not beat a simple gene-wise calibration on the held-out cohort, and nothing without the cohort's own pairs comes close to the own-cohort LoRA.", + "footnote_full": "Each held-out cohort is scored on all its usable samples against the same frozen reference as its within-cohort benchmark. Zero-shot = base model on real bulk. Calibration from other cohorts = gene-wise alpha (mean pseudobulk CPM / mean bulk CPM) fitted on the training cohorts' pairs, cohort-balanced; 0 target pairs. LoRA r16 / r64 / full fine-tune = trained on the other PBMC cohorts' pairs plus Liu 2021 (training only: its cells are in the pretraining corpus; 44 pairs after excluding 14 hashtag-negative-heavy rows), cohort-balanced batches, inner-validation on 10 % of each training cohort's donors, step-saturation protocol (20k-step blocks, stop when a block lowers the inner-validation loss by < 1 % of the total), value at the last milestone (number in the bar = steps). Ji's bulk is whole blood and is never trained on. Own-cohort LoRA = LoRA r16 on the cohort's own pairs, 5-fold pooled. Giroux's logFC r excludes 196 sex-linked genes. Best on composition among zero-target-pair arms: ImMicro Calibration from other cohorts, Sepsis Calibration from other cohorts, Imperial Full fine-tune, other cohorts, Giroux Calibration from other cohorts, Ji (whole blood) Calibration from other cohorts.", + "numbers": [ + { + "value": "ImMicro: L1 zs 0.446 / xcal 0.172 / lora16sat 0.175 / lora64sat 0.199 / fullsat 0.213 / own 0.162; coverage zs 0.19 / xcal 0.57 / lora16sat 0.45 / lora64sat 0.38 / fullsat 0.35 / own 0.78", + "source": "data/sec08_cross_cohort.csv" + }, + { + "value": "Sepsis: L1 zs 0.509 / xcal 0.444 / lora16sat 0.491 / lora64sat 0.570 / fullsat 0.585 / own 0.362; coverage zs 0.07 / xcal 0.46 / lora16sat 0.34 / lora64sat 0.29 / fullsat 0.35 / own 0.83; logFC r zs -0.02 / xcal 0.10 / lora16sat 0.10 / lora64sat 0.23 / fullsat 0.07 / own 0.53", + "source": "data/sec08_cross_cohort.csv" + }, + { + "value": "Imperial: L1 zs 0.803 / xcal 0.651 / lora16sat 0.635 / lora64sat 0.594 / fullsat 0.580 / own 0.376; coverage zs 0.11 / xcal 0.64 / lora16sat 0.68 / lora64sat 0.72 / fullsat 0.61 / own 0.85; logFC r zs 0.64 / xcal 0.69 / lora16sat 0.49 / lora64sat 0.58 / fullsat 0.46 / own 0.82", + "source": "data/sec08_cross_cohort.csv" + }, + { + "value": "Giroux: L1 zs 0.337 / xcal 0.202 / lora16sat 0.431 / lora64sat 0.410 / fullsat 0.465 / own 0.258; coverage zs 0.29 / xcal 0.89 / lora16sat 0.82 / lora64sat 0.81 / fullsat 0.78 / own 0.93; logFC r zs 0.34 / xcal 0.24 / lora16sat 0.30 / lora64sat 0.28 / fullsat 0.20 / own 0.46", + "source": "data/sec08_cross_cohort.csv" + }, + { + "value": "Ji (whole blood): L1 zs 0.725 / xcal 0.565 / lora16sat 0.740 / lora64sat 0.620 / fullsat 1.030 / own 0.504; coverage zs 0.06 / xcal 0.07 / lora16sat 0.34 / lora64sat 0.40 / fullsat 0.08 / own 0.86; logFC r zs 0.07 / xcal 0.13 / lora16sat 0.05 / lora64sat 0.16 / fullsat 0.14 / own 0.48", + "source": "data/sec08_cross_cohort.csv" + } + ] + }, + { + "kind": "content", + "section": "8", + "section_label": "8 Pairs needed", + "layout": "full", + "sources": "tools/sec08_cross_cohort.py; data/sec08_cross_cohort.csv, data/sec08_cross_cohort_steps.csv; plans/paired_crosscohort_20261002/ (README, scores/aggregate.csv)", + "text_source": { + "file": "data/sec08_cross_cohort_slide_text.json", + "index": 1 + }, + "title": "Longer training on other cohorts: no gain", + "figures": [ + "figures/sec08_cross_cohort_steps.png" + ], + "keywords": [ + "Saturation: r16 by 40k, r64 by 69k, full by 120k steps", + "Full fine-tune drifts away (Ji L1 0.84 → 1.03)", + "So PBISC fits LoRA on a few pairs per cohort" + ], + "footnote": "Test metrics at every 20k-step milestone (best inner-validation state so far). K = 1000.", + "say": "Each run trained until its inner-validation loss saturated. More steps on other cohorts never closed the gap; full fine-tuning drifted further from the held-out cohort, most in Ji (whole-blood bulk).", + "footnote_full": "Same runs as the previous slide. Each point = the milestone checkpoint at the end of a 20k-step block (or at the early stop), sampled and scored on the held-out cohort. Dashed = calibration from other cohorts; dotted = LoRA on the cohort's own pairs. Training stopped (steps): LoRA r16 ImMicro 28k, Sepsis 26k, Imperial 40k, Giroux 35k, Ji 40k; LoRA r64 ImMicro 39k, Sepsis 59k, Imperial 69k, Giroux 69k, Ji 51k; Full fine-tune ImMicro 65k, Sepsis 108k, Imperial 93k, Giroux 120k, Ji 86k.", + "numbers": [ + { + "value": "ImMicro LoRA r16: 20k L1 0.163 cov 0.45, 28k L1 0.175 cov 0.45", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "ImMicro LoRA r64: 20k L1 0.196 cov 0.44, 39k L1 0.199 cov 0.38", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "ImMicro Full fine-tune: 20k L1 0.208 cov 0.39, 40k L1 0.211 cov 0.37, 60k L1 0.223 cov 0.35, 65k L1 0.213 cov 0.35", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Sepsis LoRA r16: 20k L1 0.500 cov 0.35, 26k L1 0.491 cov 0.34", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Sepsis LoRA r64: 20k L1 0.550 cov 0.29, 40k L1 0.564 cov 0.29, 59k L1 0.570 cov 0.29", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Sepsis Full fine-tune: 20k L1 0.498 cov 0.33, 40k L1 0.530 cov 0.33, 60k L1 0.546 cov 0.34, 80k L1 0.576 cov 0.34, 100k L1 0.577 cov 0.35, 108k L1 0.585 cov 0.35", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Imperial LoRA r16: 20k L1 0.657 cov 0.68, 40k L1 0.635 cov 0.68", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Imperial LoRA r64: 20k L1 0.622 cov 0.68, 40k L1 0.603 cov 0.71, 60k L1 0.577 cov 0.72, 69k L1 0.594 cov 0.72", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Imperial Full fine-tune: 20k L1 0.632 cov 0.60, 40k L1 0.609 cov 0.60, 60k L1 0.565 cov 0.61, 80k L1 0.576 cov 0.60, 93k L1 0.580 cov 0.61", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Giroux LoRA r16: 20k L1 0.416 cov 0.79, 35k L1 0.431 cov 0.82", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Giroux LoRA r64: 20k L1 0.491 cov 0.80, 40k L1 0.469 cov 0.82, 60k L1 0.430 cov 0.82, 69k L1 0.410 cov 0.81", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Giroux Full fine-tune: 20k L1 0.543 cov 0.75, 40k L1 0.532 cov 0.75, 60k L1 0.499 cov 0.76, 80k L1 0.466 cov 0.76, 100k L1 0.490 cov 0.76, 120k L1 0.465 cov 0.78", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Ji (whole blood) LoRA r16: 20k L1 0.757 cov 0.27, 40k L1 0.740 cov 0.34", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Ji (whole blood) LoRA r64: 20k L1 0.639 cov 0.38, 40k L1 0.668 cov 0.39, 51k L1 0.620 cov 0.40", + "source": "data/sec08_cross_cohort_steps.csv" + }, + { + "value": "Ji (whole blood) Full fine-tune: 20k L1 0.838 cov 0.17, 40k L1 1.006 cov 0.11, 60k L1 1.032 cov 0.08, 80k L1 1.052 cov 0.07, 86k L1 1.030 cov 0.08", + "source": "data/sec08_cross_cohort_steps.csv" + } + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "sec09_index": 0, + "title": "Five paired cohorts", + "layout": "full", + "figures": [ + "figures/sec09_cohorts.png" + ], + "keywords": [ + "403 paired samples, 275 donors", + "Only identity-pass pairs; donor-level splits", + "Ji bulk: whole blood, not PBMC" + ], + "footnote": "Usable pair = passed identity check and ≥ 200 cells: 403 of 430 pairs; donor-level splits.", + "footnote_full": "Usable pair = passed the pairing identity check and >= 200 cells (fold >= 0 in data//folds.csv); dropped 15 identity-fail, 11 borderline and 1 low-cell pairs of 430. ImMicro: no identity flag, all used. Imperial, Giroux, Ji: several pairs per donor. Case vs control = samples in scores//pooled/summary.json. Giroux 'moderate' = GEO 'severe' (paper term); its moderate vs mild subjects are 4F/0M vs 0F/5M (scored), so its disease metrics exclude sex-linked genes. Labels: data//QC_REPORT.md; ImMicro: GEO GSE314922/GSE314416.", + "sources": "tools/sec09_paired_validation.py; data/sec09_cohorts.csv, data/sec09_quoted_numbers.csv, data/sec09_slide_text.json", + "numbers": [ + { + "value": "403 paired samples (sum of usable rows)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data//fewshot_n5/DRAWS.json (n_usable_rows)" + }, + { + "value": "275 donors (sum of usable donors)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data//fewshot_n5/DRAWS.json (n_usable_donors)" + }, + { + "value": "dropped 15 identity-fail, 11 borderline, 1 < 200 cells, of 430", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data//features.npz (identity_pass, identity_check); samples.csv (n_cells); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/prepare_dataset.py (MIN_CELLS)" + }, + { + "value": "ImMicro 107 pairs / 107 donors", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/immicro/fewshot_n5/DRAWS.json" + }, + { + "value": "Sepsis 82 pairs / 82 donors; sepsis 61 vs control 21", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ye2026_sepsis/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled/summary.json" + }, + { + "value": "Imperial 157 pairs / 57 donors; COVID+ 113 vs COVID− 34 (+10 recovery, neither)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/imperial_eskd/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/pooled/summary.json" + }, + { + "value": "Giroux 24 pairs / 9 donors; moderate 11 vs mild 13", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/giroux2022/pooled/summary.json" + }, + { + "value": "Ji 33 pairs / 20 donors; ChILI 18 vs ICI control 15", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/ji2025_chili/fewshot_n5/DRAWS.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/pooled/summary.json" + } + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "sec09_index": 1, + "title": "5 donors: LoRA vs scSemiProfiler — cells", + "layout": "full", + "figures": [ + "figures/sec09_fewpair_cells.png" + ], + "keywords": [ + "LoRA-5 > scSemi-5: coverage, precision 5/5", + "Beats zero-shot + calibration: coverage 5/5", + "Lower L1 than calibration: ImMicro only" + ], + "footnote": "5 draws per cohort; test = all other usable pairs. Calibration α from the same 5 donors. scANVI labels.", + "footnote_full": "Base f01 6k·1x·E16 (EMA, DDIM 50) + LoRA r=16, 3,000 steps. LoRA-5 / scSemi-5 = LoRA / scSemiProfiler trained on the same 5 donors (5–15 pairs); test = all other usable pairs. Zero-shot = base model without fine-tuning, fed each test pair's real bulk as measured; zero-shot + calibration = the same model fed that bulk after gene-wise calibration, alpha fitted on the same 5 training donors' pairs (both in the same call as LoRA-5). scSemi-5 scored in a separate call, same test set and reference (sha-checked); 25/25 draws. Verdict = better in >= 80% of shared draws. LoRA-5 lower L1 than scSemi-5 in 4/5 (Giroux split 3/5). LoRA-5 > zero-shot + calibration: coverage 5/5, precision 3/5, depth-matched 5/5 and 3/5 cohorts; > zero-shot (real bulk): coverage 5/5, precision 5/5, depth-matched 5/5 and 4/5. Depth-matched P/C vs scSemi-5: same ranking, coverage LoRA-5 0.83–0.97 vs scSemi-5 0.15–0.28. Composition L1, LoRA-5 minus zero-shot + calibration, per-donor Wilcoxon (gain = p < 0.05): ImMicro -0.03 (p 2e-10), Sepsis +0.02 (p 0.79), Imperial +0.02 (p 0.38), Giroux -0.06 (p 0.38), Ji -0.01 (p 0.70); minus zero-shot (real bulk): ImMicro -0.27 (p 2e-18), Sepsis -0.04 (p 0.15), Imperial -0.35 (p 2e-08), Giroux -0.11 (p 0.04), Ji -0.23 (p 0.02). scANVI labels; P/C = PCA-50, k=10 balls. Scores: scores//fewshot_n5/.", + "sources": "tools/sec09_paired_validation.py; data/sec09_fewpair_cells.csv, data/sec09_scsemi_status.csv, data/sec09_slide_text.json", + "numbers": [ + { + "value": "LoRA-5 vs scSemi-5 coverage (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.036 (5/5, lora); Sepsis 0.694 vs 0.080 (5/5, lora); Imperial 0.737 vs 0.059 (5/5, lora); Giroux 0.961 vs 0.607 (5/5, lora); Ji 0.707 vs 0.145 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json and draw_scsemi5/summary.json" + }, + { + "value": "LoRA-5 vs scSemi-5 precision (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.518 (5/5, lora); Sepsis 0.906 vs 0.728 (5/5, lora); Imperial 0.853 vs 0.330 (5/5, lora); Giroux 1.000 vs 0.992 (5/5, lora); Ji 0.978 vs 0.840 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json and draw_scsemi5/summary.json" + }, + { + "value": "LoRA-5 vs scSemi-5 comp_l1 (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.233 (5/5, lora); Sepsis 0.466 vs 0.612 (5/5, lora); Imperial 0.503 vs 0.602 (5/5, lora); Giroux 0.256 vs 0.270 (3/5, split); Ji 0.513 vs 0.592 (4/5, lora) -> LoRA verdicts 4/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json and draw_scsemi5/summary.json" + }, + { + "value": "LoRA-5 vs scSemi-5 coverage_dm (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.284 (5/5, lora); Sepsis 0.834 vs 0.158 (5/5, lora); Imperial 0.863 vs 0.152 (5/5, lora); Giroux 0.974 vs 0.238 (5/5, lora); Ji 0.908 vs 0.150 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json and draw_scsemi5/summary.json" + }, + { + "value": "LoRA-5 vs scSemi-5 precision_dm (shared-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.829 (5/5, lora); Sepsis 0.914 vs 0.504 (5/5, lora); Imperial 0.881 vs 0.429 (5/5, lora); Giroux 0.995 vs 0.426 (5/5, lora); Ji 0.974 vs 0.564 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json and draw_scsemi5/summary.json" + }, + { + "value": "LoRA-5 vs zero-shot + calibration coverage (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.535 (5/5, lora); Sepsis 0.694 vs 0.428 (5/5, lora); Imperial 0.737 vs 0.634 (5/5, lora); Giroux 0.961 vs 0.944 (5/5, lora); Ji 0.707 vs 0.114 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_cal)" + }, + { + "value": "LoRA-5 vs zero-shot + calibration precision (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.904 (5/5, lora); Sepsis 0.906 vs 0.899 (3/5, split); Imperial 0.853 vs 0.845 (3/5, split); Giroux 1.000 vs 0.996 (4/5, lora); Ji 0.978 vs 0.782 (5/5, lora) -> LoRA verdicts 3/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_cal)" + }, + { + "value": "LoRA-5 vs zero-shot + calibration comp_l1 (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.203 (5/5, lora); Sepsis 0.466 vs 0.446 (2/5, split); Imperial 0.503 vs 0.506 (2/5, split); Giroux 0.256 vs 0.288 (2/5, split); Ji 0.513 vs 0.514 (3/5, split) -> LoRA verdicts 1/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_cal)" + }, + { + "value": "LoRA-5 vs zero-shot + calibration coverage_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.780 (5/5, lora); Sepsis 0.834 vs 0.658 (5/5, lora); Imperial 0.863 vs 0.681 (5/5, lora); Giroux 0.974 vs 0.937 (5/5, lora); Ji 0.908 vs 0.357 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_cal)" + }, + { + "value": "LoRA-5 vs zero-shot + calibration precision_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.940 (5/5, lora); Sepsis 0.914 vs 0.909 (3/5, split); Imperial 0.881 vs 0.868 (3/5, split); Giroux 0.995 vs 0.989 (5/5, lora); Ji 0.974 vs 0.658 (5/5, lora) -> LoRA verdicts 3/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_cal)" + }, + { + "value": "LoRA-5 vs zero-shot (real bulk) coverage (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.792 vs 0.190 (5/5, lora); Sepsis 0.694 vs 0.076 (5/5, lora); Imperial 0.737 vs 0.114 (5/5, lora); Giroux 0.961 vs 0.472 (5/5, lora); Ji 0.707 vs 0.066 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_raw)" + }, + { + "value": "LoRA-5 vs zero-shot (real bulk) precision (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.952 vs 0.677 (5/5, lora); Sepsis 0.906 vs 0.625 (5/5, lora); Imperial 0.853 vs 0.500 (5/5, lora); Giroux 1.000 vs 0.980 (5/5, lora); Ji 0.978 vs 0.799 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_raw)" + }, + { + "value": "LoRA-5 vs zero-shot (real bulk) comp_l1 (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.176 vs 0.445 (5/5, lora); Sepsis 0.466 vs 0.510 (5/5, lora); Imperial 0.503 vs 0.806 (5/5, lora); Giroux 0.256 vs 0.351 (5/5, lora); Ji 0.513 vs 0.721 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_raw)" + }, + { + "value": "LoRA-5 vs zero-shot (real bulk) coverage_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.966 vs 0.213 (5/5, lora); Sepsis 0.834 vs 0.073 (5/5, lora); Imperial 0.863 vs 0.151 (5/5, lora); Giroux 0.974 vs 0.549 (5/5, lora); Ji 0.908 vs 0.199 (5/5, lora) -> LoRA verdicts 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_raw)" + }, + { + "value": "LoRA-5 vs zero-shot (real bulk) precision_dm (same-draw means; LoRA-better draws/n; verdict): ImMicro 0.975 vs 0.671 (5/5, lora); Sepsis 0.914 vs 0.595 (5/5, lora); Imperial 0.881 vs 0.561 (5/5, lora); Giroux 0.995 vs 0.996 (2/5, split); Ji 0.974 vs 0.480 (5/5, lora) -> LoRA verdicts 4/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (arms lora5, zs_raw)" + }, + { + "value": "coverage cohort means over all draws: LoRA-5 ImMicro 0.792, Sepsis 0.694, Imperial 0.737, Giroux 0.961, Ji 0.707; scSemi-5 ImMicro 0.036, Sepsis 0.080, Imperial 0.059, Giroux 0.607, Ji 0.145; zero-shot + calibration ImMicro 0.535, Sepsis 0.428, Imperial 0.634, Giroux 0.944, Ji 0.114; zero-shot ImMicro 0.190, Sepsis 0.076, Imperial 0.114, Giroux 0.472, Ji 0.066", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json and draw_scsemi5/summary.json" + }, + { + "value": "LoRA-5 minus zero-shot + calibration composition L1, per-donor Wilcoxon (draw-averaged): ImMicro -0.027, n=107, p=2.2e-10; Sepsis +0.018, n=82, p=0.79; Imperial +0.016, n=57, p=0.38; Giroux -0.065, n=8, p=0.38; Ji -0.012, n=20, p=0.7 -> gain in 1/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/per_donor.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data//folds.csv" + }, + { + "value": "LoRA-5 minus zero-shot (real bulk) composition L1, per-donor Wilcoxon (draw-averaged): ImMicro -0.270, n=107, p=2.2e-18; Sepsis -0.044, n=82, p=0.15; Imperial -0.348, n=57, p=1.6e-08; Giroux -0.107, n=8, p=0.039; Ji -0.232, n=20, p=0.017 -> gain in 4/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/per_donor.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data//folds.csv" + }, + { + "value": "scSemiProfiler draws used / LoRA draws: ImMicro 5/5, Sepsis 5/5, Imperial 5/5, Giroux 5/5, Ji 5/5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec09_scsemi_status.csv" + }, + { + "value": "training pairs per draw: ImMicro 5, Sepsis 5, Imperial 12–15, Giroux 12–14, Ji 7–9; test pairs: ImMicro 102, Sepsis 77, Imperial 142–145, Giroux 10–12, Ji 24–26", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data//fewshot_n5/DRAWS.json (n_train_rows, n_test_rows)" + }, + { + "value": "LoRA r = 16; 3000 fixed steps; base runs/f01_6k_1x_seed20260627/checkpoint_epoch_016.pt; sampling: test donors only, 1,500 cells each, seed 20260927, DDIM 50, EMA, Bernoulli read-out", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/runs/immicro/fewshot_n5/draw0/lora/RUN.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/README.md" + }, + { + "value": "P/C definition: precision/coverage/density (PCA-50 + k = 10 balls fitted on the scored donors' observed R1 cells); PCA components in every summary.json = 50", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/tools/score_ft.py" + } + ], + "refs": [ + "Kynkäänniemi 2019, NeurIPS", + "Naeem 2020, ICML", + "Wang 2024, Nat Commun" + ], + "refs_full": [ + "Kynkäänniemi et al. Improved precision and recall metric for assessing generative models. NeurIPS 2019", + "Naeem et al. Reliable fidelity and diversity metrics for generative models. ICML 2020", + "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1" + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "text_source": { + "file": "data/sec09_extra_slide_text.json", + "index": 0 + }, + "title": "5 donors: realism beyond precision/coverage", + "layout": "full", + "figures": [ + "figures/sec09_extra_fewpair.png" + ], + "keywords": [ + "Classifier AUC: LoRA-5 best in 25/25 draws", + "E_N: LoRA-5 below scSemi-5 in 25/25 draws", + "Still separable: LoRA-5 AUC 0.89–0.95" + ], + "footnote": "E_N: scSemiProfiler's own error metric. Misses only vs calibration, e.g. Imperial E_N 1.28 vs 1.22.", + "footnote_full": "Same 5-donor draws as the previous slides; every metric on exactly the scored cells (scores_extra/, tools/extra_metrics.py). Real side for AUC and MMD² = held-out real cells of the same samples (R2), projected like the generated cells; real vs real = R2 vs R3 (AUC 0.49–0.50). Classifier AUC: 5-fold CV ROC AUC of a kNN classifier (k = 5) separating real from generated cells, 0.5 = indistinguishable; bars start at 0.5. MMD²: unbiased, Gaussian kernel, bandwidth = median distance among real cells. E_N: scSemiProfiler's normalized error (Wang et al. 2024, eq. 17–18), their code's squared L2 on all cells, per-arm PCA-100; real vs real = within-sample half split (0.07–0.38); every generated arm is above 1. LoRA-5 best of the 4 arms in 25/25 draws on kNN AUC, 25/25 on RF AUC, 25/25 on 1-NN accuracy, 23/25 on MMD², 22/25 on E_N. LoRA-5 better than scSemi-5, zero-shot and zero-shot + calibration on both AUCs, 1-NN accuracy and coverage at k = 3, 5, 10 in every draw. E_N scSemi-5 vs LoRA-5 (cohort means): ImMicro 3.65 vs 1.55; Sepsis 3.83 vs 1.46; Imperial 2.04 vs 1.28; Giroux 3.79 vs 1.99; Ji 4.27 vs 2.60; LoRA-5 lower in 25/25 draws. Draws where LoRA-5 is worse (cohort means in brackets): vs zero-shot + calibration: Sepsis precision k5 1/5 (0.778 vs 0.752), precision k10 2/5 (0.906 vs 0.899); Imperial MMD² (σ = m) 1/5 (0.0156 vs 0.0218), MMD² (σ sum) 1/5 (0.0443 vs 0.0673), E_N (all cells) 3/5 (1.28 vs 1.22), precision k3 2/5 (0.610 vs 0.598), precision k5 2/5 (0.726 vs 0.719), precision k10 2/5 (0.853 vs 0.845); Giroux MMD² (σ = m) 1/5 (0.0178 vs 0.0257), MMD² (σ sum) 1/5 (0.0584 vs 0.0838), precision k5 1/5 (0.995 vs 0.981), precision k10 1/5 (1.000 vs 0.996). LoRA-5 AUC per draw 0.88–0.97 (kNN), RF cohort means 0.90–0.96: a classifier still tells generated from real cells. Scores: scores_extra//fewshot_n5/draw/.", + "sources": "tools/sec09_extra_metrics.py; data/sec09_extra_fewpair.csv, data/sec09_extra_quoted_numbers.csv; plans/paired_finetune_intradataset_20260927/scores_extra/ (README.md, TABLES.md)", + "numbers": [ + { + "value": "LoRA-5 best of 4 arms (draws): kNN AUC 25/25, RF AUC 25/25, 1-NN 25/25, MMD² 23/25, E_N 22/25", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + }, + { + "value": "E_N LoRA-5 < scSemi-5 in 25/25 draws; cohort means scSemi-5 vs LoRA-5: ImMicro 3.65 vs 1.55; Sepsis 3.83 vs 1.46; Imperial 2.04 vs 1.28; Giroux 3.79 vs 1.99; Ji 4.27 vs 2.60", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/en.csv (cellset all, distance sq)" + }, + { + "value": "LoRA-5 kNN AUC cohort means: ImMicro 0.943, Sepsis 0.936, Imperial 0.918, Giroux 0.893, Ji 0.955; per draw 0.878–0.975", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + }, + { + "value": "real vs real: kNN AUC ImMicro 0.502, Sepsis 0.501, Imperial 0.498, Giroux 0.495, Ji 0.491; E_N ImMicro 0.382, Sepsis 0.100, Imperial 0.260, Giroux 0.145, Ji 0.073", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + }, + { + "value": "LoRA-5 worse than a comparator (draws): vs zero-shot + calibration: Sepsis precision k5 1/5 (0.778 vs 0.752), precision k10 2/5 (0.906 vs 0.899); Imperial MMD² (σ = m) 1/5 (0.0156 vs 0.0218), MMD² (σ sum) 1/5 (0.0443 vs 0.0673), E_N (all cells) 3/5 (1.28 vs 1.22), precision k3 2/5 (0.610 vs 0.598), precision k5 2/5 (0.726 vs 0.719), precision k10 2/5 (0.853 vs 0.845); Giroux MMD² (σ = m) 1/5 (0.0178 vs 0.0257), MMD² (σ sum) 1/5 (0.0584 vs 0.0838), precision k5 1/5 (0.995 vs 0.981), precision k10 1/5 (1.000 vs 0.996)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + }, + { + "value": "cross-check: 720 cohort × arm × metric values equal scores_extra/{fewshot,pooled}_headline.csv", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/fewshot_headline.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra/pooled_headline.csv" + } + ], + "refs": [ + "Lopez-Paz 2017, ICLR", + "Gretton 2012, JMLR", + "Wang 2024, Nat Commun" + ], + "refs_full": [ + "Lopez-Paz & Oquab. Revisiting classifier two-sample tests. ICLR 2017", + "Gretton et al. A kernel two-sample test. JMLR 13:723-773 (2012)", + "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1" + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "text_source": { + "file": "data/sec09_extra_slide_text.json", + "index": 2 + }, + "title": "5 donors: precision/coverage at k = 3, 5, 10", + "layout": "full", + "figures": [ + "figures/sec09_extra_ksens.png" + ], + "keywords": [ + "Smaller k lowers every set, real included", + "Coverage: LoRA-5 first at all k, 25/25", + "Precision: LoRA-5 first in 20–23/25 draws" + ], + "footnote": "5 donors, 5 draws per cohort; error bars = SD over draws. k = 10 is the value on the main slides.", + "footnote_full": "Precision and coverage recomputed with k = 3, 5, 10 nearest-neighbour balls in the scorer's PCA-50 (k = 10 reproduces the main-slide values exactly). Means over the 5 draws per cohort; error bars = SD; grey = real vs real (R1 vs R2, same samples). Both metrics fall with smaller k for every set including the real control (real coverage at k = 3: 0.92–0.99), so compare arms with the real row, not with 1. Expected coverage of a perfect generator (Naeem et al. 2020, eq. 9) at k = 3/5/10: 0.88/0.97/1.00 in every cohort. LoRA-5 first on coverage at every k in 25/25 draws; on precision in 23/21/20 of 25 draws (k = 3/5/10); not first: Sepsis 0/1/2, Imperial 2/2/2, Giroux 0/1/1 draws at k = 3/5/10, beaten by zero-shot + calibration. 5-fold pooled design (not drawn): coverage ranking of the 4 arms across k unchanged; LoRA first at every k in 5/5 cohorts. Scores: scores_extra//fewshot_n5/draw/metrics.csv.", + "sources": "tools/sec09_extra_metrics.py; data/sec09_extra_ksens.csv; plans/paired_finetune_intradataset_20260927/scores_extra/", + "numbers": [ + { + "value": "LoRA-5 first on coverage at k = 3, 5 and 10: 25/25 draws", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + }, + { + "value": "LoRA-5 first on precision (draws) at k = 3/5/10: 23/21/20 of 25", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + }, + { + "value": "real vs real coverage at k = 3: ImMicro 0.932, Sepsis 0.925, Imperial 0.919, Giroux 0.993, Ji 0.980", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + }, + { + "value": "every set falls with smaller k (cohort means): True", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//fewshot_n5/draw/{metrics,en}.csv" + } + ], + "refs": [ + "Naeem 2020, ICML", + "Khayatkhoei 2023, ICML" + ], + "refs_full": [ + "Naeem et al. Reliable fidelity and diversity metrics for generative models. ICML 2020", + "Khayatkhoei & AbdAlmageed. Emergent asymmetry of precision and recall for measuring fidelity and diversity of generative models in high dimensions. ICML 2023" + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "text_source": { + "file": "data/sec09_scsemi_representative_slide_text.json", + "index": 0 + }, + "title": "scSemiProfiler copies its representative", + "layout": "full", + "figures": [ + "figures/sec09_scsemi_representative.png" + ], + "keywords": [ + "scSemi-5 never beats copying its representative: 5/5", + "Its error tracks the representative's: rho 0.57–0.93", + "LoRA-5 below the copy error in 4/5 cohorts" + ], + "footnote": "Copy = composition of the one training sample scSemiProfiler assigns to each test sample.", + "sources": "plans/paired_finetune_intradataset_20260927/scsemi/tools/representative_dependence.py; tools/sec09_scsemi_representative.py; data/sec09_scsemi_representative.csv", + "footnote_full": "Same 25 five-donor draws. scSemiProfiler gives every test sample exactly one representative, the nearest training sample in its bulk PCA (assignment.json), and runs its encoder on that representative's cells. Copy = composition L1 between the test sample's and its representative's observed cells (same frozen scANVI labels as the scorer). Bars: mean over draws of the per-draw mean; dots = draws. scSemi vs copy: paired Wilcoxon over test samples (ImMicro p 1e-33, Sepsis p 0.014, Imperial p 0.91, Giroux p 0.0073, Ji p 0.46). Upstream's own representative choice (KMeans on the same bulk PCA, the sample nearest each centre; proxy, not a rerun) gives a lower copy error than our 5-donor draws in Imperial, Giroux and Ji, where our draws hold all visits of 5 donors and KMeans spreads the same number of samples over as many donors; equal or higher in ImMicro and sepsis. Copy error: ImMicro (5 samples) ours 0.19, KMeans 0.18, random samples 0.19; Sepsis (5 samples) ours 0.58, KMeans 0.61, random samples 0.58; Imperial (14 samples) ours 0.61, KMeans 0.53, random samples 0.56; Giroux (13 samples) ours 0.25, KMeans 0.16, random samples 0.23; Ji (8 samples) ours 0.57, KMeans 0.46, random samples 0.51. Source: plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/.", + "numbers": [ + { + "value": "ImMicro: copy 0.187, scSemi-5 0.233 (Wilcoxon vs copy p 1e-33), LoRA-5 0.176; rho scSemi 0.57, LoRA 0.35", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/summary.csv" + }, + { + "value": "Sepsis: copy 0.580, scSemi-5 0.612 (Wilcoxon vs copy p 0.014), LoRA-5 0.466; rho scSemi 0.88, LoRA 0.56", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/summary.csv" + }, + { + "value": "Imperial: copy 0.608, scSemi-5 0.602 (Wilcoxon vs copy p 0.91), LoRA-5 0.503; rho scSemi 0.93, LoRA 0.55", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/summary.csv" + }, + { + "value": "Giroux: copy 0.253, scSemi-5 0.267 (Wilcoxon vs copy p 0.0073), LoRA-5 0.258; rho scSemi 0.81, LoRA 0.08", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/summary.csv" + }, + { + "value": "Ji: copy 0.570, scSemi-5 0.591 (Wilcoxon vs copy p 0.46), LoRA-5 0.511; rho scSemi 0.72, LoRA 0.39", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/summary.csv" + }, + { + "value": "KMeans proxy, copy error: ImMicro (5 samples) ours 0.19, KMeans 0.18, random samples 0.19; Sepsis (5 samples) ours 0.58, KMeans 0.61, random samples 0.58; Imperial (14 samples) ours 0.61, KMeans 0.53, random samples 0.56; Giroux (13 samples) ours 0.25, KMeans 0.16, random samples 0.23; Ji (8 samples) ours 0.57, KMeans 0.46, random samples 0.51", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/representative_dependence_20261002/kmeans_proxy.csv" + } + ], + "refs": [ + "Wang 2024, Nat Commun" + ], + "refs_full": [ + "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1" + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "sec09_index": 2, + "title": "5 donors: LoRA vs scSemiProfiler — disease signal", + "layout": "full", + "figures": [ + "figures/sec09_fewpair_disease.png" + ], + "keywords": [ + "LoRA-5 > scSemi-5: DEG 2/4, logFC r 2/4", + "Spearman or limma: broadcast ahead 3/4", + "Split-half ceiling 0.96–0.99: not limiting" + ], + "footnote": "Giroux: sex-linked genes excluded (severity confounded with sex: 4F/0M vs 0F/5M). Top-50 DEG over 5 types.", + "footnote_full": "Same draws and verdict rule as the previous slide; DEG tie = half the draws equal or mean gap < 1 gene. Rank and threshold-free checks (scores_contrast/, tools/contrast_extra.py): Spearman r of the same logFC vectors; split-half ceiling = observed cells of each scored sample split in two halves (5 seeds), r of half-A vs half-B logFC, Spearman-Brown corrected to full data (noise-free ceiling); limma-voom top-50 = edgeR TMM + voom + limma eBayes on the same donor x type pseudobulks, BH FDR < 0.05, top 50 by |logFC|; grey tick = chance overlap (hypergeometric mean) of that arm's lists. Repeated samples of one patient (Imperial, Giroux, Ji) enter both tests as separate samples, as in the scorer. Sepsis: Spearman LoRA-5 0.22 / scSemi-5 0.16 / broadcast 0.33, limma top-50 LoRA-5 16.2 / scSemi-5 11.4 / broadcast 35.4 (chance 2.2), ceiling 0.99; Imperial: Spearman LoRA-5 0.55 / scSemi-5 0.37 / broadcast 0.36, limma top-50 LoRA-5 102.0 / scSemi-5 35.2 / broadcast 59.6 (chance 3.1), ceiling 0.97; Giroux: Spearman LoRA-5 0.19 / scSemi-5 0.27 / broadcast 0.38, limma top-50 LoRA-5 0.8 / scSemi-5 1.6 / broadcast 14.8 (chance 0.5), ceiling 0.97; Ji: Spearman LoRA-5 0.29 / scSemi-5 0.09 / broadcast 0.12, limma top-50 LoRA-5 3.0 / scSemi-5 0.6 / broadcast 6.2 (chance 1.4), ceiling 0.96. Earlier slide lines: logFC r: LoRA-5 > calibrated zero-shot 4/4; Giroux: broadcast r beats all arms. logFC r = mean over T/B/Mono/NK of case-vs-control log2FC r (generated vs observed); top-50 DEG = Welch+BH overlap summed over 5 types (max 250; Giroux 1–63, Ji 75–250 per draw). Giroux test = 2 vs 2 subjects per draw. LoRA-5-better draws (verdict): vs scSemi-5 logFC r Sepsis split 3/5, Imperial lora 5/5, Giroux scSemi-5 0/5, Ji lora 5/5; DEG Sepsis lora 5/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Vs zero-shot + calibration: logFC r Sepsis lora 4/5, Imperial lora 5/5, Giroux lora 4/5, Ji lora 5/5; DEG Sepsis lora 4/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Vs zero-shot (real bulk): logFC r Sepsis lora 5/5, Imperial lora 5/5, Giroux split 3/5, Ji lora 5/5; DEG Sepsis lora 4/5, Imperial lora 5/5, Giroux tie 1/5, Ji tie 1/5. Broadcast = the input real bulk's own logFC. Giroux: moderate vs mild subjects are 4F/0M vs 0F/5M (scored; all paired subjects 4F/1M vs 0F/5M; sex from bulk chrY expression = sc = GEO), so its disease metrics (logFC r, partial r, top-50 DEG, broadcast) exclude 196 sex-linked genes (43 chrY non-PAR + 153 X-inactivation escape / variable-escape genes of Tukiainen et al. 2017; PAR genes kept), 65 of them scoring genes; same labels and cells as before (scores_nosex/giroux2022/). Scores: scores//fewshot_n5/draw{,_scsemi5}/.", + "sources": "tools/sec09_paired_validation.py; data/sec09_fewpair_disease.csv, data/sec09_slide_text.json", + "numbers": [ + { + "value": "Giroux sex by contrast arm (subjects, F/M): scored moderate 4F/0M vs mild 0F/5M; all paired 4F/1M vs 0F/5M", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/features.npz (sex_bulk_expr = sex_sc_expr = sex_geo); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/folds.csv" + }, + { + "value": "Giroux sex-linked genes excluded from the disease metrics: 196 listed (43 chrY non-PAR + 153 X escape), 65 of 5563 scoring genes", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/sex_linked_genes_exclude.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/sex_linked_genes/BUILD.json; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json (gene_exclusion)" + }, + { + "value": "Top-50 DEG LoRA-5 vs scSemi-5 (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 5.2 (5/5, 0 tied, lora); Imperial 113.6 vs 46.8 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)" + }, + { + "value": "Top-50 DEG LoRA-5 vs zero-shot + calibration (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 9.4 (4/5, 0 tied, lora); Imperial 113.6 vs 103.8 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)" + }, + { + "value": "Top-50 DEG LoRA-5 vs zero-shot (real bulk) (shared-draw means; LoRA-better / tied draws; verdict): Sepsis 16.4 vs 10.0 (4/5, 0 tied, lora); Imperial 113.6 vs 105.6 (5/5, 0 tied, lora); Giroux 0.2 vs 0.0 (1/5, 4 tied, tie); Ji 0.2 vs 0.0 (1/5, 4 tied, tie)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)" + }, + { + "value": "logFC r LoRA-5 vs scSemi-5 (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs 0.402 (3/5, split); Imperial 0.749 vs 0.562 (5/5, lora); Giroux 0.297 vs 0.397 (0/5, other); Ji 0.333 vs 0.153 (5/5, lora)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)" + }, + { + "value": "logFC r LoRA-5 vs zero-shot + calibration (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs 0.096 (4/5, lora); Imperial 0.749 vs 0.676 (5/5, lora); Giroux 0.297 vs 0.228 (4/5, lora); Ji 0.333 vs 0.140 (5/5, lora)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)" + }, + { + "value": "logFC r LoRA-5 vs zero-shot (real bulk) (shared-draw means; LoRA-better draws; verdict): Sepsis 0.350 vs -0.024 (5/5, lora); Imperial 0.749 vs 0.638 (5/5, lora); Giroux 0.297 vs 0.255 (3/5, split); Ji 0.333 vs 0.087 (5/5, lora)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw{,_scsemi5}/summary.json and contrast.csv (Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/)" + }, + { + "value": "Sepsis broadcast logFC r 0.320 vs best arm 0.402; broadcast DEG 20.0 vs best arm 16.4 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/fewshot_n5/draw{,_scsemi5}/summary.json" + }, + { + "value": "Imperial broadcast logFC r 0.656 vs best arm 0.749; broadcast DEG 74.8 vs best arm 113.6 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/imperial_eskd/fewshot_n5/draw{,_scsemi5}/summary.json" + }, + { + "value": "Giroux broadcast logFC r 0.492 vs best arm 0.397; broadcast DEG 0.0 vs best arm 0.2 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/fewshot_n5/draw{,_scsemi5}/summary.json" + }, + { + "value": "Ji broadcast logFC r 0.234 vs best arm 0.333; broadcast DEG 1.0 vs best arm 0.2 (means over all draws; arms: zero-shot, zero-shot + calibration, scSemi-5, LoRA-5)", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/fewshot_n5/draw{,_scsemi5}/summary.json" + }, + { + "value": "DEG maximum per draw: Sepsis 250, Imperial 250, Giroux 1–63, Ji 75–250", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//fewshot_n5/draw/summary.json (deg_top50_max; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex)" + }, + { + "value": "Giroux test subjects per draw: 2 vs 2 (moderate vs mild); test pairs 10–12", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/fewshot_n5/DRAWS.json (train_donors); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data/giroux2022/folds.csv (group)" + } + ], + "refs": [ + "Roohani 2024, Nat Biotechnol", + "Viñas Torné 2026, Nat Biotechnol", + "Li 2026, Sci Adv", + "Law 2014, Genome Biol" + ], + "refs_full": [ + "Roohani, Huang & Leskovec. Predicting transcriptional outcomes of novel multigene perturbations with GEARS. Nat Biotechnol 42:927-935 (2024). doi:10.1038/s41587-023-01905-6", + "Viñas Torné et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nat Biotechnol 44:1050-1059 (2026). doi:10.1038/s41587-025-02777-8", + "Li et al. A systematic comparison of single-cell perturbation response prediction models. Sci Adv 12:eaed3414 (2026). doi:10.1126/sciadv.aed3414", + "Law, Chen, Shi & Smyth. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29 (2014)" + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "text_source": { + "file": "data/sec09_cat_curves_slide_text.json", + "index": 0 + }, + "title": "Disease genes at every list length (CAT)", + "layout": "full", + "figures": [ + "figures/sec09_cat_curves.png" + ], + "keywords": [ + "LoRA top model: 3/4 (5 donors), 2/4 (5-fold)", + "Broadcast above LoRA: Giroux", + "Chance at k = 100: 0.012–0.016" + ], + "footnote": "Same-sign overlap of observed and generated top-k genes per cell type; no FDR cut-off.", + "sources": "tools/sec09_cat_curves.py; data/sec09_cat_curves.csv", + "refs": [ + "Irizarry 2005, Nat Methods", + "Webber 2010, ACM TOIS", + "Squair 2021, Nat Commun" + ], + "refs_full": [ + "Irizarry et al. Multiple-laboratory comparison of microarray platforms. Nat Methods 2:345-350 (2005). doi:10.1038/nmeth756", + "Webber, Moffat & Zobel. A similarity measure for indefinite rankings. ACM Trans Inf Syst 28(4):20 (2010). doi:10.1145/1852102.1852106", + "Squair et al. Confronting false discoveries in single-cell differential expression. Nat Commun 12:5692 (2021). doi:10.1038/s41467-021-25960-2" + ], + "footnote_full": "Correspondence at the top (Irizarry et al. 2005): per cell type, every gene tested on both sides is ranked by |limma-voom moderated t| (case vs control, same donor x type pseudobulks as the DEG panels); CAT(k) = share of the observed top-k genes that are also in the generated top-k with the same logFC sign. Mean over T / B / Mono / NK; 5 donors = mean over 5 draws (scSemiProfiler: its 25 draws); 5-fold = pooled call. Broadcast = the input real bulk ranked the same way. Chance = k / (2G), G = genes tested on both sides (0.012–0.016 at k = 100). No significance threshold, so cohorts where few genes pass FDR (Giroux, Ji) still get a curve. Rank-biased overlap (Webber et al. 2010, p = 0.98) gives the same order: scores_contrast/{fewshot,pooled}_rbo.csv. Giroux: sex-linked genes excluded. Sepsis CAT100 5 donors LoRA 0.092 / scSemi 0.037 / calibration 0.069 / raw 0.028 / broadcast 0.069 / chance 0.012; 5-fold LoRA 0.163 / broadcast 0.075; Imperial CAT100 5 donors LoRA 0.243 / scSemi 0.133 / calibration 0.198 / raw 0.179 / broadcast 0.214 / chance 0.012; 5-fold LoRA 0.270 / broadcast 0.225; Giroux CAT100 5 donors LoRA 0.048 / scSemi 0.075 / calibration 0.050 / raw 0.048 / broadcast 0.176 / chance 0.016; 5-fold LoRA 0.068 / broadcast 0.145; Ji CAT100 5 donors LoRA 0.091 / scSemi 0.030 / calibration 0.021 / raw 0.021 / broadcast 0.043 / chance 0.014; 5-fold LoRA 0.152 / broadcast 0.068.", + "numbers": [ + { + "value": "Sepsis CAT100 5 donors LoRA 0.092 / scSemi 0.037 / calibration 0.069 / raw 0.028 / broadcast 0.069 / chance 0.012; 5-fold LoRA 0.163 / broadcast 0.075; Imperial CAT100 5 donors LoRA 0.243 / scSemi 0.133 / calibration 0.198 / raw 0.179 / broadcast 0.214 / chance 0.012; 5-fold LoRA 0.270 / broadcast 0.225; Giroux CAT100 5 donors LoRA 0.048 / scSemi 0.075 / calibration 0.050 / raw 0.048 / broadcast 0.176 / chance 0.016; 5-fold LoRA 0.068 / broadcast 0.145; Ji CAT100 5 donors LoRA 0.091 / scSemi 0.030 / calibration 0.021 / raw 0.021 / broadcast 0.043 / chance 0.014; 5-fold LoRA 0.152 / broadcast 0.068", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_contrast/fewshot_cat.csv; /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_contrast/pooled_cat.csv" + } + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "text_source": { + "file": "data/sec09_time_to_apply_slide_text.json", + "index": 0 + }, + "title": "5 donors: time to apply", + "layout": "full", + "figures": [ + "figures/sec09_time_to_apply.png" + ], + "keywords": [ + "LoRA-5: 3–11 min per draw, every test sample", + "scSemiProfiler-5: 1.0–4.1 h per draw", + "11–63× less GPU time, same 5 donors" + ], + "footnote": "One GB10 GPU; mean of 5 draws. Data prep and scoring not counted. PBISC pretraining (15.9 h) is done once.", + "sources": "tools/sec09_time_to_apply.py; data/sec09_time_to_apply.csv, data/sec09_time_to_apply_runs.csv", + "footnote_full": "Same 25 draws as the previous slides (5 cohorts x 5 draws; same 5 training donors and test samples for both methods). GPU time on one GB10, every step alone on the GPU (shared lock). LoRA-5: fine-tune = RUN.json wall_seconds of the 3,000-step LoRA; generate = wall clock of the sampling job in the queue log (model load + DDIM 50, 1,500 cells per test sample). scSemiProfiler-5 (upstream c2aea02, default budget): stage 1 + stage 2 pretraining of every representative (model receipts) + its per-target inference (5 ministages x 150 epochs, export receipt total_target_seconds). Not counted on either side: CPU data preparation, scoring, work lost to interruptions, and PBISC's one-time pretraining (15.9 h, slide 15), which every cohort reuses. scSemiProfiler time grows with the number of test samples because it trains one model per sample. Per-draw ratio scSemiProfiler / LoRA: ImMicro 15–19×; Sepsis 26–47×; Imperial 19–26×; Giroux 54–63×; Ji 11–20×.", + "numbers": [ + { + "value": "ImMicro: LoRA-5 8.4 min (fine-tune 1.9 + generate 6.5), scSemiProfiler-5 141 min (representatives 15 + per-sample models 126); 15–19× per draw; per test sample 4.9 s vs 83 s", + "source": "data/sec09_time_to_apply.csv" + }, + { + "value": "Sepsis: LoRA-5 6.7 min (fine-tune 1.8 + generate 4.9), scSemiProfiler-5 244 min (representatives 30 + per-sample models 214); 26–47× per draw; per test sample 5.2 s vs 190 s", + "source": "data/sec09_time_to_apply.csv" + }, + { + "value": "Imperial: LoRA-5 10.8 min (fine-tune 1.8 + generate 9.0), scSemiProfiler-5 229 min (representatives 32 + per-sample models 197); 19–26× per draw; per test sample 4.5 s vs 96 s", + "source": "data/sec09_time_to_apply.csv" + }, + { + "value": "Giroux: LoRA-5 2.6 min (fine-tune 1.9 + generate 0.7), scSemiProfiler-5 156 min (representatives 117 + per-sample models 38); 54–63× per draw; per test sample 14.8 s vs 872 s", + "source": "data/sec09_time_to_apply.csv" + }, + { + "value": "Ji: LoRA-5 3.4 min (fine-tune 1.8 + generate 1.6), scSemiProfiler-5 57 min (representatives 24 + per-sample models 33); 11–20× per draw; per test sample 8.1 s vs 137 s", + "source": "data/sec09_time_to_apply.csv" + } + ], + "refs": [ + "Wang 2024, Nat Commun" + ], + "refs_full": [ + "Wang et al. scSemiProfiler: advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning. Nat Commun 15:5989 (2024). doi:10.1038/s41467-024-50150-1" + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "sec09_index": 3, + "title": "5-fold CV: train on the other 4 folds", + "layout": "full", + "figures": [ + "figures/sec09_fivefold.png" + ], + "keywords": [ + "LoRA tops coverage 5/5, logFC r 4/4", + "Ji whole blood: LoRA 0.86, others 0.06–0.11", + "Broadcast ahead: Sepsis, Giroux" + ], + "footnote": "Donor-level 5-fold CV. Giroux: sex-linked genes excluded (severity confounded with sex: 4F/0M vs 0F/5M).", + "footnote_full": "Donor-level 5-fold CV; each pair scored once, by the fold model that never trained on its donor (one pooled call per cohort). LoRA r=16 on the real bulk of the other 4 folds' training donors, early stop on inner-val donors. Zero-shot = base model without fine-tuning, fed the test pair's real bulk as measured; zero-shot + calibration = the same model fed gene-wise calibrated bulk (alpha fitted on the fold's training donors); sc-only fine-tune (+ calibration) = full fine-tune on training donors' sc pseudobulk, fed calibrated bulk. Base f01 6k·1x·E16. LoRA best of 4 arms: coverage 5/5, logFC r 4/4, top-50 DEG 4/4. Broadcast = raw real bulk. Sepsis DEG from pooled_deg. Giroux: moderate vs mild subjects are 4F/0M vs 0F/5M (scored; all paired subjects 4F/1M vs 0F/5M; sex from bulk chrY expression = sc = GEO), so its disease metrics (logFC r, partial r, top-50 DEG, broadcast) exclude 196 sex-linked genes (43 chrY non-PAR + 153 X-inactivation escape / variable-escape genes of Tukiainen et al. 2017; PAR genes kept), 65 of them scoring genes; same labels and cells as before (scores_nosex/giroux2022/). Scores: scores//pooled/summary.json.", + "sources": "tools/sec09_paired_validation.py; data/sec09_fivefold.csv, data/sec09_slide_text.json", + "numbers": [ + { + "value": "LoRA best of 4 arms: coverage 5/5, logFC r 4/4, top-50 DEG 4/4", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)" + }, + { + "value": "Ji coverage: zero-shot 0.055, zero-shot + calibration 0.106, sc-only (+ calibration) 0.113, LoRA 0.857", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ji2025_chili/pooled/summary.json" + }, + { + "value": "Giroux logFC r: broadcast 0.575 vs LoRA 0.461; DEG broadcast 5 vs LoRA 5", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json" + }, + { + "value": "LoRA logFC r vs broadcast: Sepsis 0.527 vs 0.316, Imperial 0.819 vs 0.661, Giroux 0.461 vs 0.575, Ji 0.479 vs 0.240", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)" + }, + { + "value": "coverage LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): ImMicro 0.784 / 0.527 / 0.192 / 0.623, Sepsis 0.829 / 0.431 / 0.067 / 0.726, Imperial 0.850 / 0.639 / 0.112 / 0.555, Giroux 0.928 / 0.906 / 0.281 / 0.908, Ji 0.857 / 0.106 / 0.055 / 0.113", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)" + }, + { + "value": "logFC r LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): Sepsis 0.527 / 0.123 / -0.012 / 0.296, Imperial 0.819 / 0.679 / 0.644 / 0.808, Giroux 0.461 / 0.269 / 0.334 / 0.344, Ji 0.479 / 0.142 / 0.084 / 0.245", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)" + }, + { + "value": "top-50 DEG LoRA / zero-shot + calibration / zero-shot / sc-only (+ calibration): Sepsis 23 / 11 / 9 / 15, Imperial 143 / 111 / 106 / 129, Giroux 5 / 0 / 1 / 0, Ji 15 / 0 / 0 / 0", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//pooled/summary.json (Sepsis DEG: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores/ye2026_sepsis/pooled_deg/summary.json; Giroux: /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_nosex/giroux2022/pooled/summary.json)" + }, + { + "value": "Pooled n (pairs / donors): ImMicro 107/107, Sepsis 82/82, Imperial 157/57, Giroux 24/9, Ji 33/20", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores//pooled/summary.json (n_donors); /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/data//fewshot_n5/DRAWS.json" + }, + { + "value": "early stopping: About 10 % of each fold's training donors are inner-validation, used only for early stopping", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/README.md" + } + ], + "refs": [ + "Viñas Torné 2026, Nat Biotechnol", + "Li 2026, Sci Adv", + "Law 2014, Genome Biol" + ], + "refs_full": [ + "Viñas Torné et al. Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation. Nat Biotechnol 44:1050-1059 (2026). doi:10.1038/s41587-025-02777-8", + "Li et al. A systematic comparison of single-cell perturbation response prediction models. Sci Adv 12:eaed3414 (2026). doi:10.1126/sciadv.aed3414", + "Law, Chen, Shi & Smyth. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:R29 (2014)" + ] + }, + { + "kind": "content", + "section": "9", + "section_label": "9 Paired validation", + "text_source": { + "file": "data/sec09_extra_slide_text.json", + "index": 1 + }, + "title": "5-fold: realism beyond precision/coverage", + "layout": "full", + "figures": [ + "figures/sec09_extra_fivefold.png" + ], + "keywords": [ + "Every metric: LoRA first in 5/5 cohorts", + "Ji whole blood: E_N 1.9 vs 7.1–10.1", + "Still separable: LoRA AUC 0.87–0.94" + ], + "footnote": "Donor-level 5-fold CV, one pooled call per cohort. E_N: scSemiProfiler's own error metric.", + "footnote_full": "Donor-level 5-fold CV; each pair scored once by the fold model that never trained on its donor (one pooled call per cohort, all cross-fitted pairs). Arms as on the 5-fold slide. Metric definitions as on the 5-donor realism slide. LoRA first among the 4 arms in all 5 cohorts on 12/12 headline metrics (kNN and RF AUC, 1-NN accuracy, MMD² at σ = m and summed, E_N all cells, precision and coverage at k = 3, 5, 10). LoRA kNN AUC ImMicro 0.941, Sepsis 0.886, Imperial 0.866, Giroux 0.881, Ji 0.908; real vs real ImMicro 0.505, Sepsis 0.506, Imperial 0.496, Giroux 0.501, Ji 0.494. E_N LoRA vs other arms: ImMicro 1.56 vs 1.77–4.83; Sepsis 1.20 vs 1.40–5.29; Imperial 1.13 vs 1.19–4.05; Giroux 1.70 vs 2.24–4.48; Ji 1.95 vs 7.08–10.13. scSemiProfiler 5-fold CV not available yet (no extra-metric unit with set scsemi__raw). Scores: scores_extra//pooled/.", + "sources": "tools/sec09_extra_metrics.py; data/sec09_extra_fivefold.csv, data/sec09_extra_scsemi_status.csv; plans/paired_finetune_intradataset_20260927/scores_extra/", + "numbers": [ + { + "value": "LoRA first among 4 arms in all 5 cohorts on 12/12 metrics", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//pooled/{metrics,en}.csv" + }, + { + "value": "Ji E_N: LoRA 1.948; Zero-shot, real bulk 10.125, Zero-shot + calibration 7.076, sc-only fine-tune (+ calibration) 7.215", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//pooled/{metrics,en}.csv" + }, + { + "value": "LoRA kNN AUC (pooled): ImMicro 0.941, Sepsis 0.886, Imperial 0.866, Giroux 0.881, Ji 0.908", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scores_extra//pooled/{metrics,en}.csv" + }, + { + "value": "scSemiProfiler, 5-fold CV: not available", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/data/sec09_extra_scsemi_status.csv" + } + ], + "refs": [ + "Lopez-Paz 2017, ICLR", + "Gretton 2012, JMLR" + ], + "refs_full": [ + "Lopez-Paz & Oquab. Revisiting classifier two-sample tests. ICLR 2017", + "Gretton et al. A kernel two-sample test. JMLR 13:723-773 (2012)" + ] + }, + { + "kind": "content", + "section": "10", + "section_label": "10 Paired-subset cohorts", + "title": "Next: cohorts with a paired subset (planned)", + "layout": "figure_keywords", + "figures": [ + "figures/sec10_paired_subset_cohorts.png" + ], + "keywords": [ + "Train LoRA on pairs, generate bulk-only", + "Sepsis controls: 21 paired + 54 bulk-only", + "ChILI pre-ICI: 11 bulk, 4 unpaired sc" + ], + "footnote": "Counts from pairing manifests. Model: f01 6k 1x E16 + LoRA r16.", + "say": "Planned: no LoRA cells exist yet for any bulk-only sample. Ji bulk is PAXgene whole blood. Only 7 of the Arunachalam pairs are strict same-visit pairs. Asan is omitted because it has no donor map.", + "sources": "tools/sec10_paired_subset_cohorts.py; data/sec10_cohort_counts.csv, data/sec10_bulk_only_groups.csv, data/sec10_quoted_numbers.csv", + "footnote_full": "Counts from pairing manifests. QC = identity check (Imperial also >=200 cells); sex-flag = bulk sex differs from metadata or is ambiguous. Not shown: Asan (no donor map), Giroux (24 pairs, 17 bulk-only; broadcast beats LoRA). Model: f01 6k 1x E16 + LoRA r16." + }, + { + "kind": "divider", + "title": "Appendix" + }, + { + "kind": "content", + "section": "A", + "section_label": "Appendix", + "text_source": { + "file": "data/secA_scsemi_readout_slide_text.json", + "index": 0 + }, + "title": "Appendix: scSemiProfiler without output threshold", + "layout": "full", + "figures": [ + "figures/secA_scsemi_readout.png" + ], + "keywords": [ + "Official output zeroes values ≤ 10 (fixed)", + "Removed: scSemi coverage up in 5/5 cohorts", + "No LoRA-5 comparison flips (23/23 same)" + ], + "footnote": "Modified read-out, not scSemiProfiler's official output. Main slides use the official one.", + "say": "Supplementary only. The threshold is part of scSemiProfiler's official output, so removing it is a modified run; the main comparison stays the official output. This slide shows that our conclusions do not depend on that read-out.", + "sources": "plans/paired_finetune_intradataset_20260927/scsemi/tools/{resample_output_variants,compare_output_variants}.py; tools/secA_scsemi_readout.py; data/secA_scsemi_readout.csv", + "footnote_full": "Appendix, sensitivity only. scSemiProfiler's official output (upstream commit c2aea02) is the decoder's ZINB mean with every value <= 10 normalized units set to 0; the cut is fixed in the code (vaegan.myVAE.sample, bound = 10, and fast_semi: xsemi * (xsemi > 10)), not an option. At our cohorts' depth (about 1,000-2,000 UMI per cell on the panel) it removes most low- and mid-expressed genes. Here the same trained per-target models were read out again without that cut (CPU, no retraining, target seeds as in the run), exported and scored exactly as the official arm (same 25 five-donor draws, donors, reference; Giroux disease metrics without sex-linked genes). A control re-read with the cut reproduces the official output: pseudobulk r >= 0.996 over 1790 targets, coverage within 0.015. Because this changes the method's official read-out, the main slides keep the official output. Bars = mean of 5 draws, dots = draws. Source: plans/paired_finetune_intradataset_20260927/scsemi/analysis/output_variants_20261003/ (README section 'Read-out sensitivity').", + "numbers": [ + { + "value": "ImMicro: coverage LoRA-5 0.79, official 0.04, no threshold 0.23; precision 0.95 / 0.52 / 0.74; L1 0.176 / 0.233 / 0.199", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/output_variants_20261003/per_draw.csv" + }, + { + "value": "Sepsis: coverage LoRA-5 0.69, official 0.08, no threshold 0.18; precision 0.91 / 0.73 / 0.77; L1 0.466 / 0.612 / 0.591; logFC r 0.35 / 0.40 / 0.46; top-50 DEG 16.4 / 5.2 / 14.0", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/output_variants_20261003/per_draw.csv" + }, + { + "value": "Imperial: coverage LoRA-5 0.74, official 0.06, no threshold 0.42; precision 0.85 / 0.33 / 0.72; L1 0.503 / 0.602 / 0.603; logFC r 0.75 / 0.56 / 0.66; top-50 DEG 113.6 / 46.8 / 94.6", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/output_variants_20261003/per_draw.csv" + }, + { + "value": "Giroux: coverage LoRA-5 0.96, official 0.61, no threshold 0.83; precision 1.00 / 0.99 / 1.00; L1 0.256 / 0.270 / 0.273; logFC r 0.30 / 0.40 / 0.45; top-50 DEG 0.2 / 0.0 / 0.0", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/output_variants_20261003/per_draw.csv" + }, + { + "value": "Ji: coverage LoRA-5 0.71, official 0.15, no threshold 0.53; precision 0.98 / 0.84 / 0.92; L1 0.513 / 0.592 / 0.575; logFC r 0.33 / 0.15 / 0.19; top-50 DEG 0.2 / 0.0 / 0.0", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/plans/paired_finetune_intradataset_20260927/scsemi/analysis/output_variants_20261003/per_draw.csv" + } + ] + } + ] +} diff --git a/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/NOTE.md b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/NOTE.md new file mode 100644 index 0000000000000000000000000000000000000000..a205efe7ebcc43b08987855815e5de8e7ce6f6eb --- /dev/null +++ b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/NOTE.md @@ -0,0 +1,5 @@ +# PBISC-diffusion update 20261003T224927Z-deck-appendix-scsemi-readout + +Paper summary deck: appendix added (user decision 2026-10-04, supplementary only): divider + slide 39 'Appendix: scSemiProfiler without output threshold' (tools/secA_scsemi_readout.py, figure, data CSV, slide text), slide spec, README, deck pptx + PDF (39 slides). The slide reads the read-out sensitivity tables published in 20261003T174003Z-scsemi-readout-sensitivity; main slides unchanged. + +Payload states are recorded as supplied. Inference locations are pointers, not archived generated outputs. diff --git a/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/inference_locations.json b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/inference_locations.json new file mode 100644 index 0000000000000000000000000000000000000000..7d1e64dbaa8f650b80a5f01b514baa582d521d13 --- /dev/null +++ b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/inference_locations.json @@ -0,0 +1,5 @@ +{ + "locations": [], + "project_id": "pbisc-diffusion", + "update_id": "20261003T224927Z-deck-appendix-scsemi-readout" +} diff --git a/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/request.json b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/request.json new file mode 100644 index 0000000000000000000000000000000000000000..4792af8b113200f40365d535326ea2a4ca207db2 --- /dev/null +++ b/paper-releases/pbisc-diffusion/updates/20261003T224927Z-deck-appendix-scsemi-readout/diffusion/metadata/request.json @@ -0,0 +1,1255 @@ +{ + "assets": [ + { + "host": "server_1", + "kind": "report", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/DRAFT_1150_PBISC-Diffusion_paper_summary.pdf", + "state": "completed" + }, + { + "host": "server_1", + "kind": "report", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/DRAFT_1150_PBISC-Diffusion_paper_summary.pptx", + "state": "completed" + }, + { + "host": "server_1", + "kind": "report", + "source": "/home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_paper_summary_20260930.pdf", + "state": 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slide 39 'Appendix: scSemiProfiler without output threshold' (tools/secA_scsemi_readout.py, figure, data CSV, slide text), slide spec, README, deck pptx + PDF (39 slides). The slide reads the read-out sensitivity tables published in 20261003T174003Z-scsemi-readout-sensitivity; main slides unchanged." +}