Archive 4 checkpoints (1-4)

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paper-releases/pbisc-diffusion/updates/20261015T100456Z-figure1-diffusion-readable-corpus/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/data/figure1_focus_20261015/page_map.csv

@@ -0,0 +1,157 @@
+page,parent_155_page,original_76_page,group,panel,title
+1,1,1,Introduction,Introduction,PBISC-Diffusion: manuscript evidence
+2,2,2,Introduction,Introduction,Figures 1-6: the expanded evidence map
+3,3,,Introduction,Introduction,How this expanded deck is organized
+4,4,3,Introduction,Current scope,Manuscript map: the six figures
+5,5,4,Figure 1,Figure 1,Figure 1. Overview and corpus
+6,6,5,Figure 1,Figure 1 plan,"Figure 1: overview, corpus and denoising"
+7,7,7,Figure 1,Fig. 1a,Experimental overview
+8,,,Figure 1,Fig. 1b,Pretraining data: 33.14 million training cells
+9,8,,Figure 1,Figure 1 support,Training corpus: complete counts and minor categories
+10,9,,Figure 1,Figure 1 support,"Full source corpus: training, validation and test"
+11,10,,Figure 1,Figure 1 support,From 41.40M source cells to 33.14M training cells
+12,11,,Figure 1,Figure 1 support,Recipe K and training exposure are different counts
+13,12,13,Figure 1,Fig. 1c,Corpus scale and generation workflow
+14,13,,Figure 1,Fig. 1d,From Gaussian noise to generated cell states
+15,14,15,Figure 2,Figure 2,Figure 2. SLE model ablations
+16,15,16,Figure 2,Figure 2 plan,Figure 2: architecture and gene context
+17,16,,Figure 2,Fig. 2a,Where context width and trunk width change
+18,17,,Figure 2,Fig. 2b,Predicted and oracle hints: one trained denoiser
+19,18,10,Figure 2,Fig. 2c,Architecture: full and late validation progression
+20,19,11,Figure 2,Fig. 2d,Width & context ablation: benchmark
+21,20,,Figure 2,Fig. 2 details,Architecture arms: dimensions and endpoints
+22,21,12,Figure 2,Fig. 2e,Gene context: 2k / 3k / 6k / 15k
+23,22,,Figure 2,Fig. 2f,Gene panels: progression relative to each endpoint
+24,23,,Figure 2,Fig. 2 details,Gene-panel arms: exact sizes and training provenance
+25,24,,Figure 2,Fig. 2g,All ablation arms: parameters and training time
+26,25,,Figure 2,Fig. 2h,Real-cell repeat: a sampling reference
+27,26,,Figure 2,Fig. 2i,Native-pseudobulk model comparisons
+28,27,28,Figure 3,Figure 3,Figure 3. Real-bulk adaptation
+29,28,29,Figure 3,Figure 3 plan,Figure 3: the paired-adaptation story
+30,29,17,Figure 3,Fig. 3a,Bulk and pooled cells: different expression values
+31,30,,Figure 3,Fig. 3a detail,Paired calibration reduces same-donor expression error
+32,31,19,Figure 3,Fig. 3b detail,Consequence: cells off-manifold
+33,32,,Figure 3,Fig. 3 · Adaptation,Adaptation recovers the observed cell mixture
+34,33,,Figure 3,Fig. 3 · Adaptation,Adaptation: mixture error and expression agreement
+35,34,,Figure 3,Fig. 3b,Healthy paired data: full FT and LoRA
+36,35,,Figure 3,Fig. 3c,Full FT and LoRA ranks: quality and parameters
+37,36,,Figure 3,Fig. 3 · Adaptation,What does “all pairs” actually mean?
+38,37,,Figure 3,Fig. 3d,Healthy pair budget: correlation in all cell types
+39,38,,Figure 3,Fig. 3d detail,Healthy pair budget: error in all cell types
+40,39,30,Figure 3,Fig. 3e,"Five pairs, no extra validation: LoRA vs scSemi"
+41,40,,Figure 3,Fig. 3f,Composition: error and donor-level wins
+42,41,35,Figure 4,Figure 4,Figure 4. Disease signal preservation
+43,42,36,Figure 4,Figure 4 plan,Figure 4: disease signal preservation
+44,43,38,Figure 4,Fig. 4a,Disease signals with five training donors
+45,44,39,Figure 4,Fig. 4b,5-fold CV: disease signal
+46,45,40,Figure 4,Fig. 4c,Disease genes at every list length (CAT)
+47,46,41,Figure 4,Fig. 4d,"Cell-type deconvolution, same pairs: disease signal"
+48,47,,Figure 4,Fig. 4 · Disease integrity,Disease gene rankings: complete CV comparison
+49,48,,Figure 4,Fig. 4 · Disease integrity,Disease integrity depends on cohort and endpoint
+50,49,,Figure 4,Fig. 4 · Disease integrity,Five-pair Sepsis: metrics across all support draws
+51,50,47,Figure 5,Figure 5,Figure 5. Rare-population identity
+52,51,48,Figure 5,Figure 5 plan,Figure 5: paired rare-population identity
+53,52,,Figure 5,Fig. 5 · Source-defined populations,Figure 5 targets begin with the source paper
+54,53,,Figure 5,Fig. 5 · Source-defined populations,Ye sepsis: published markers in paired scRNA
+55,54,,Figure 5,Fig. 5 · Source-defined populations,Transcriptional programs and rare-population identity
+56,55,53,Figure 6,Figure 6,Figure 6. Published-finding reproduction
+57,56,54,Figure 6,Figure 6 plan,Figure 6: published-finding reproduction
+58,57,,Figure 6,Figure 6,Figure 6: what the reproduction must establish
+59,58,56,Supplementary,Supplementary,Supplementary evidence
+60,59,57,Supplementary,Supplementary,Supplementary evidence map
+61,60,9,Supp S01,Supp. S01,Corpus reference: assay and source composition
+62,61,6,Supp S01,Supp. S01,Backbone: SDD → PBISC-Diffusion
+63,62,8,Supp S01,Supp. S01,Bulk conditioning
+64,63,14,Supp S01,Supp. S01,Denoiser: joint value + support diffusion
+65,64,20,Supp S01,Supp. S01,Fine-tuning with paired bulk + sc
+66,65,21,Supp S01,Supp. S01,Evaluation design
+67,66,22,Supp S01,Supp. S01,How we score generated cells
+68,67,,Supp S02,Supp. S02,Does the model follow its bulk conditioning?
+69,68,42,Supp S02,Supp. S02,SLE benchmark: PBISC vs scSemiProfiler
+70,69,43,Supp S02,Supp. S02,SLE benchmark: per cell type
+71,70,,Supp S02,Supp. S02,Disjoint-cell controls test the bulk-broadcast baseline
+72,71,,Supp S02,Supp. S02,SLE: what is added beyond the bulk broadcast?
+73,72,18,Supp S03,Supp. S03,Same shift in three paired cohorts
+74,73,,Supp S03,Supp. S03,Input corrections on one real-reference UMAP
+75,74,,Supp S03,Supp. S03,Where generated-cell density differs from real cells
+76,75,,Supp S03,Supp. S03,Input calibration changes cell-type composition
+77,76,,Supp S03,Supp. S03,"Same noise, different input: matched-cell shifts"
+78,77,,Supp S03,Supp. S03,Matched noise separates cell state from cell identity
+79,78,,Supp S03,Supp. S03,Input changes can switch the generated cell label
+80,79,,Supp S03,Supp. S03,Nearest real cells: distance and label agreement
+81,80,,Supp S03,Supp. S03,Composition agreement does not ensure state agreement
+82,81,,Supp S03,Supp. S03,Gene-length effects in the bulk input
+83,82,,Supp S03,Supp. S03,Gene-length effects within label-stable cells
+84,83,,Supp S03,Supp. S03,Raw and TPM shifts persist within cell labels
+85,84,,Supp S03,Supp. S03,Reference calibration leaves specific gene-state shifts
+86,85,,Supp S03,Supp. S03,Donor trait axes and donor identification differ
+87,86,,Supp S03,Supp. S03,Reference choice changes different quality endpoints
+88,87,,Supp S04,Supp. S04,Why the historical and strict protocols differ
+89,88,,Supp S04,Supp. S04,Technical repeats and follow-up are different controls
+90,89,,Supp S04,Supp. S04,Annotation agreement and difficult categories
+91,90,,Supp S04,Supp. S04,Depth matching changes precision and coverage
+92,91,,Supp S04,Supp. S04,Detection and library size are separate diagnostics
+93,92,23,Supp S05,Supp. S05,Healthy cohort: LoRA cells cover the real ones
+94,93,24,Supp S05,Supp. S05,Healthy cohort: realism beyond precision/coverage
+95,94,25,Supp S05,Supp. S05,Healthy cohort: full vs LoRA vs condition encoder
+96,95,32,Supp S05,Supp. S05,Representative-dependent composition
+97,96,64,Supp S05,Supp. S05,Full vs LoRA vs cond.-encoder-only
+98,97,26,Supp S06,Supp. S06,"Pair-budget summary: composition, coverage and steps"
+99,98,27,Supp S06,Supp. S06,Healthy cohort: LoRA rank and K
+100,99,31,Supp S06,Supp. S06,Healthy paired-data learning curves
+101,100,33,Supp S06,Supp. S06,"Time: days for scSemiProfiler, minutes for LoRA"
+102,101,34,Supp S06,Supp. S06,5 donors: time to apply
+103,102,65,Supp S06,Supp. S06,"Pairs needed: composition, coverage, training steps"
+104,103,66,Supp S06,Supp. S06,"LoRA rank: r = 4, 16, 64"
+105,104,67,Supp S06,Supp. S06,K diversification: no gain
+106,105,37,Supp S07,Supp. S07,Five paired cohorts
+107,106,,Supp S07,Supp. S07,Sepsis: exact-five reconstruction across five draws
+108,107,,Supp S07,Supp. S07,Sepsis: realism beyond precision and coverage
+109,108,,Supp S07,Supp. S07,Imperial: first completed exact-five comparison
+110,109,58,Supp S07,Supp. S07,5 donors: LoRA vs scSemiProfiler — cells
+111,110,59,Supp S07,Supp. S07,5 donors: realism beyond precision/coverage
+112,111,60,Supp S07,Supp. S07,"5 donors: precision/coverage at k = 3, 5, 10"
+113,112,61,Supp S07,Supp. S07,5-fold: realism beyond precision/coverage
+114,113,62,Supp S07,Supp. S07,Same training pairs: LoRA vs scSemiProfiler
+115,114,63,Supp S07,Supp. S07,scSemiProfiler and the representative-copy baseline
+116,115,,Supp S08,Supp. S08,Sepsis: patient-aware disease-gene agreement
+117,116,,Supp S08,Supp. S08,Imperial: patient-aware disease-gene agreement
+118,117,44,Supp S08,Supp. S08,Sepsis: fine-tuning variants
+119,118,45,Supp S08,Supp. S08,Pairs needed: donor and disease signal
+120,119,46,Supp S08,Supp. S08,"Cell-type deconvolution, same pairs: composition"
+121,120,,Supp S08,Supp. S08,Deconvolution: expression coverage limits the comparison
+122,121,,Supp S08,Supp. S08,"K=4,000 recipe: paired reconstruction quality"
+123,122,,Supp S08,Supp. S08,"K=4,000 recipe: disease signal does not improve uniformly"
+124,123,,Supp S08,Supp. S08,"K=4,000 recipe: calibration gains are selective"
+125,124,,Supp S09,Supp. S09,Influenza: response order versus response genes
+126,125,,Supp S09,Supp. S09,COVID: calibration on a shared real-cell map
+127,126,,Supp S09,Supp. S09,COVID monocytes: direction after depth matching
+128,127,,Supp S09,Supp. S09,"COVID monocytes: direction, magnitude and null"
+129,128,,Supp S09,Supp. S09,"COVID CD14 monocytes: direction, magnitude and null"
+130,129,,Supp S09,Supp. S09,COVID: biological endpoints give a mixed verdict
+131,130,,Supp S10,Supp. S10,Rare-state frequencies in observed cells
+132,131,,Supp S10,Supp. S10,Rare-state misses: rules 1–8
+133,132,,Supp S10,Supp. S10,Rare-state misses: rules 9–15
+134,133,,Supp S10,Supp. S10,Rare-state fidelity: zero-aware reanalysis
+135,134,,Supp S10,Supp. S10,Sepsis rare states across paired-data budgets
+136,135,,Supp S10,Supp. S10,Sepsis rare-state direction across support budgets
+137,136,,Supp S11,Supp. S11,"Rare-state screen in DRESS, Kawasaki and SLE"
+138,137,52,Supp S11,Supp. S11,Consequence: disease direction flips
+139,138,,Supp S11,Supp. S11,C1Q: single-gene detection versus co-detection
+140,139,,Supp S11,Supp. S11,"Behçet: K=4,000 changes direction more than identity"
+141,140,,Supp S11,Supp. S11,Behcet: one-gene and multi-gene C1Q rules
+142,141,68,Supp S12,Supp. S12,Pairs from other cohorts do not transfer
+143,142,69,Supp S12,Supp. S12,Longer training on other cohorts: gap stays
+144,143,70,Supp S12,Supp. S12,"No pretraining: same cohort, from scratch"
+145,144,71,Supp S12,Supp. S12,"No pretraining: other cohorts, from scratch"
+146,145,72,Supp S12,Supp. S12,Rare states: from scratch vs pretrained
+147,146,74,Supp S13,Supp. S13,Appendix: scSemiProfiler without output threshold
+148,147,,Supp S14,Supp. S14,Bulk-only exploration: calibration populations differ
+149,148,73,Historical archive,Historical archive,Historical scoring and plans
+150,149,49,Historical archive,Historical only,Rare-state fractions: original analysis
+151,150,50,Historical archive,Historical only,Rare-state ranking: original analysis
+152,151,51,Historical archive,Historical only,Rare states: original five-fold analysis
+153,152,55,Historical archive,Historical archive,Bulk-only cohort plan: original snapshot
+154,153,,Historical archive,Historical archive,Historical bulk-only search: no confirmed new finding
+155,154,75,Discussion,Discussion,Discussion and outlook
+156,155,76,Discussion,Discussion,Next: other tissues from a few dozen pairs