Archive 4 checkpoints (1-4)

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+# PBISC-Diffusion results decks
+
+**Current available-results review (2026-10-13):**
+[PDF](PBISC-Diffusion_results_review_20261013.pdf) ·
+[editable PPTX](PBISC-Diffusion_results_review_20261013.pptx).
+This 34-slide review reuses completed evidence in the approved Figure 1–5
+sequence. It keeps historical five-fold, exact-five healthy draws and
+multi-visit five-donor disease designs explicit. Corrected rare-state tables
+and saved healthy composition scores are redrawn on CPU; no new training or
+generation is introduced. Figure 6 awaits the user-prepared dataset.
+
+Sources and per-slide protocol labels:
+`data/results_review_20261013_provenance.json` and
+`data/results_review_20261013_slide_map.csv`.
+Builder: `tools/build_results_review_20261013.py`.
+The incomplete 8-page TODO layout and older strict-campaign review working
+copies are superseded; their immutable archive records are preserved.
+
+Current method palette: **PBISC red #B2182B; scSemiProfiler blue #2166AC**. PBISC adaptations use red/rose shades; scSemi stage/readout variants use blue shades. Cell-type/cohort encodings and observed references retain their own colors. All selected figures pass through the explicit per-asset policy in `data/method_palette_20261013/palette.json` when the review is built.
+
+- [Current composition comparison](figures/method_palette_20261013/review_style_20261013_healthy_composition.png): separate error bars and donor wins, all six types; [metric record](data/composition_error_20261013/REPORT.md).
+- [Palette verification](data/method_palette_20261013/VERIFICATION.json) · [style verification](data/style_review_20261013/VERIFICATION.json).
+- Current export command: `CUDA_VISIBLE_DEVICES= python data/style_review_20261013/render.py` (CPU, run under the host's bounded-memory policy).
+
+The original 76-slide deck below remains the prior source/reference deck.
+
+## Original 2026-09-30 r00 deck and historical change log
+
+76-slide English deck, ordered by the six main figures of the Nature-style manuscript plan (user request 2026-10-06,
+following `paper-release-20260928/reference-papers/Nature Style Manuscript Writing.pdf`):
+- Title and outline: 6 figures, Supplementary, Appendix, Discussion.
+- "Manuscript plan: six main figures": a table of each figure's claim and how many panels are ready.
+- For each figure:
+  - a divider;
+  - a plan slide, a table of panel / content / evidence slide / status (Ready, Adapt, Draft, Build, Run, Plan);
+  - the existing slides that serve its panels, each labelled "Fig. 2c" etc. in the top-right corner.
+- Supplementary: a plan slide plus the from-scratch results. Appendix: the modified scSemiProfiler run. Discussion: the
+  other-tissues outlook.
+
+User decisions of 2026-10-06:
+- No PBISC-VAE: scSemiProfiler, a VAE-based method, is the comparison (Fig. 3).
+- Fig. 2 shows LoRA first on the healthy cohort (ImMicro) with the realism metrics (precision, coverage, classifier
+  AUROC, MMD, E_N); the disease-cohort comparison comes later.
+- CYBERSORTx and other bulk deconvolution methods go to the Supplementary only (PBISC generates single cells).
+- Figs 4 and 6 stay unplanned. Their existing slides are kept in the section as "Fig. 4 candidate" / "Fig. 6 candidate".
+
+Only order, section labels, the agenda and new plan or draft slides changed. The new slides are "Overview draft (Nature
+style)", the manuscript's candidate Fig. 1, and "Pretraining corpus", the Fig. 1d strip. No new result figures were
+made.
+- The plan lives in `data/figure_plan.json`.
+- `tools/figure_plan.py` draws the plan tables and the slide text, and checks that every slide is placed exactly once.
+- `tools/restructure_figure_order_20261006.py` rebuilds the order from the plan. It is rerun after every plan change and
+  keeps all slide fields.
+- The earlier section order (10 sections, 45 slides) is in `_archive/2026-10-06_before_figure_order/`.
+
+Short keyword lines only; the figures carry the results. Every slide that uses a published method, metric or
+baseline has a one-line "References:" footer; full citations are in the speaker notes.
+
+- `PBISC-Diffusion_paper_summary_20260930.pptx`: the deck
+- `PBISC-Diffusion_paper_summary_20260930.pdf`: LibreOffice PDF of the deck
+- `data/slides_spec.json`: slide order, titles, keywords, footnotes, references, speaker-note text
+- `generate_deck.py`: builds the pptx from `data/slides_spec.json` only (python-pptx)
+- `figures/`: exactly the figures the slides show (PNG + PDF), made by `tools/sec*.py`. Intermediate panels go to a
+  temporary folder; superseded figures are deleted, not kept (2026-10-02, user request; every deleted file was
+  checked to be in the paper archive by sha256 first).
+- `data/sec*.csv` and `data/*_slide_text.json`: every plotted or quoted number with its source path
+- `render/`: PDF and 70-dpi slide JPGs for checking
+
+## Slide order
+
+| section | slides |
+|---|---|
+| 1 Model overview | bulk in, cells out; denoiser |
+| 2 Backbone | SDD → PBISC-Diffusion; bulk conditioning |
+| 3 Architecture ablation | width & context (loss, benchmark); gene panel |
+| 4 Metrics + SLE benchmark | **how we score generated cells** (metric table with references); SLE benchmark; per cell type; bulk broadcast baseline |
+| 5 Real bulk vs pseudobulk | input shift; same shift in three cohorts; cells off-manifold; disease direction flips |
+| 6 Paired fine-tuning | method; evaluation design |
+| 7 LoRA ablations | full vs LoRA vs cond.-encoder-only; LoRA rank r = 4 / 16 / 64; K diversification |
+| 8 Pairs needed | composition / coverage / steps (with scSemiProfiler at the same n); donor and disease signal; pairs from other cohorts; longer training on them (with training minutes) |
+| 9 Paired validation | cohorts; 5 donors: cells, realism beyond P/C, P/C at k = 3 / 5 / 10, scSemiProfiler copies its representative, disease signal, CAT curves, time to apply; 5-fold CV (with scSemiProfiler on the same pairs); same training pairs: LoRA vs scSemiProfiler; 5-fold realism metrics |
+| 10 Next steps | paired-subset cohorts (planned); other tissues from a few dozen pairs (Discussion framing of the from-scratch results) |
+| Supplementary | no pretraining: same cohort, from scratch (pilot); no pretraining: other cohorts, from scratch (pilot); rare states: from scratch vs pretrained; deconvolution with the same pairs: composition; deconvolution with the same pairs: disease signal |
+| Appendix | scSemiProfiler without its output threshold (sensitivity; modified read-out, not the official output) |
+
+## Rebuild
+
+Everything is CPU only; no GPU or checkpoint is touched.
+
+```bash
+# prerequisites (plan folder plans/paired_finetune_intradataset_20260927/, CPU):
+#   scsemi/tools/representative_dependence.py      -> scsemi/analysis/representative_dependence_20261002/
+#   tools/contrast_extra.py (+ tools/contrast_extra_tables.py) -> scores_contrast/   (Spearman, ceiling, limma, CAT, RBO)
+#   tools/build_tpm_zeroshot.py + tools/score_tpm_zeroshot.sh -> scores/<ds>/zeroshot_tpm_20261002/pooled/
+#     (the length-corrected zero-shot arm needs one GPU sampling step: tools/sample_lockwait.sh, coordinator only)
+cd /home/sj_server_1/PBISC/immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00
+# shared helpers, not run directly: style.py, sec03_common.py, scsemi_results.py, fold_sizes.py
+for f in sec01_overview sec02_backbone sec03_width_loss sec03_width_bench sec03_panel \
+         sec04_metrics_overview sec04_sle_benchmark sec05_condition_space sec05_cells_offmanifold \
+         sec05_behcet_direction sec05_shift_cohorts sec06_finetune_method sec07_ft_modes \
+         sec07_k_aug sec07_rank_sweep sec08_pairs_needed sec08_cross_cohort sec09_paired_validation sec09_extra_metrics \
+         sec09_time_to_apply sec09_scsemi_representative sec09_cat_curves sec10_paired_subset_cohorts \
+         secA_scsemi_readout sec07_no_pretraining sec08_cross_scratch sec09_same_pairs \
+         secS_scratch_rare_states secS_deconvolution fig3_time_days fig4_disease fig23_healthy fig5_rare_states sec10_other_tissues figure_plan; do   # keep this order (sec10 reads secS; figure_plan last)
+  python3 "tools/$f.py" || break
+done
+python3 tools/restructure_figure_order_20261006.py   # order the slides by data/figure_plan.json
+python3 tools/sync_sec09_text.py      # copy every generated slide text (sec09_index / text_source) into the spec
+python3 generate_deck.py              # -> PBISC-Diffusion_paper_summary_20260930.pptx
+soffice --headless --convert-to pdf --outdir render PBISC-Diffusion_paper_summary_20260930.pptx
+pdftoppm -jpeg -r 70 render/PBISC-Diffusion_paper_summary_20260930.pdf render/slide
+cp render/PBISC-Diffusion_paper_summary_20260930.pdf .
+```
+
+`generate_deck.py` stops with an error if a figure is missing, a title does not fit on one line, a footnote needs
+more than 2 lines (1 line when the slide has references), the reference line needs more than 1 line, or any text
+contains Korean or placeholder words. `tools/restructure_20261002.py` is the one-off script that produced the current
+slide order and the references (it refuses to run twice).
+
+## Notes
+
+- **No "all" label (2026-10-02).** The 5-fold arm trains each fold model on the other four folds minus 7–8
+  inner-validation donors; its test fold is never trained on (RUN.json `test_rows_never_loaded_for_training`). Figures
+  label it "5-fold CV (other 4 folds)" and learning-curve ticks show the training-pair count per fold (78–79 ImMicro,
+  58–59 sepsis), read by `tools/fold_sizes.py`.
+- **Learning curve x = 0 uses no pairs (2026-10-02).** It shows the zero-shot model fed the real bulk as measured
+  and after exon-length correction (counts / Ensembl union-exon kb, then the same fixed-depth arithmetic;
+  `plans/.../tools/build_tpm_zeroshot.py`, scored in `scores/<ds>/zeroshot_tpm_20261002/pooled`). The old x = 0
+  calibration point, whose alpha used every training pair of the fold, is gone; calibration appears only fitted on the
+  same n pairs as the LoRA point (squares; `cal_budget_20261001`).
+- **Disease metrics (2026-10-02).** Next to Pearson logFC r and the Welch top-50 DEG overlap: Spearman r, the
+  split-half ceiling (observed vs observed, Spearman–Brown), limma-voom top-50 DEG, each arm's chance overlap, and
+  CAT curves with the k / 2G chance line (`plans/.../scores_contrast/`, tools/contrast_extra.py; every Pearson r and
+  Welch count reproduced exactly). Repeated samples of one patient (Imperial, Giroux, Ji) count as separate samples in
+  both DE tests, as in the scorer (footnote).
+- Section 9 text is generated: `tools/sec09_paired_validation.py`, `sec09_extra_metrics.py`, `sec07_rank_sweep.py`,
+  `sec09_time_to_apply.py`, `sec09_scsemi_representative.py`, `sec09_cat_curves.py` write slide text JSON; sync copies
+  it. Hand-written: the keywords of the two "Pairs needed" slides (section 8) and of every slide in sections 1–7 and 10.
+- Giroux's disease metrics exclude 196 sex-linked genes (severity is confounded with sex), from
+  `plans/.../scores_nosex/`. The calibration comparator is "Zero-shot + calibration" (user decision 2026-10-01).
+- Story rule (user decision 2026-09-30): no PBISC-VAE and no reference calibration (refcal). The exon-length-corrected
+  input is used only as the no-pair x = 0 point of the learning curve (user decision 2026-10-02).
+  `tools/sec04_immicro.py` (refcal vs scSemiProfiler on ImMicro) is kept but is not part of the deck or the rebuild.
+- scSemiProfiler 5-fold and equal-budget results are picked up automatically when they appear (`tools/scsemi_results.py`;
+  arm `scsemi__raw` in `scores/<cohort>/scsemi_cv_20261002/pooled*/`, `scsemi_n{5,10,20,40}__raw` in
+  `scores/<cohort>/scsemi_budget_20261002/pooled*/`; same donors and reference cells as the main pooled call only).
+  Then rerun sections 8 and 9, sync, recheck the section 8 keywords and rebuild.
+- Earlier versions: `_archive/2026-09-30_before_no_vae_refcal/`, `_archive/2026-10-01_before_extra_metrics/`,
+  `_archive/2026-10-02_before_time_to_apply/`, `_archive/2026-10-02_before_restructure/` (pptx, PDF, spec; their
+  figures are in the paper archive). `DRAFT_1150_*`: the copy shown at the 2026-09-30 12:00 meeting.
+
+- Added 2026-10-03 (user decision: the cross-cohort experiment is reported at K = 1000 only and is the figure that explains
+  why PBISC fits LoRA on a few pairs of each cohort): slides 25-26 in section 8, `tools/sec08_cross_cohort.py`.
+  - Slide 25 "Pairs from other cohorts do not transfer": per held-out cohort, composition L1 / coverage / disease logFC r of
+    zero-shot, calibration fitted on the other cohorts, LoRA r16 / r64 / full fine-tune trained on the other cohorts
+    (+ Liu 2021) at step saturation, and LoRA on the cohort's own pairs (5-fold pooled).
+  - Slide 26 "Longer training on other cohorts" (": gap stays" since 2026-10-06): the same metrics at every 20k-step
+    milestone.
+  - Source plan: `plans/paired_crosscohort_20261002/` (README; same-reference and same-sample checks in the script).
+    Agenda item 8 now reads "How many pairs, and do other cohorts' pairs transfer". The deck has 37 slides.
+    Pre-update copy: `_archive/2026-10-03_before_cross_cohort/`.
+
+## Sources
+
+Final model: `runs/f01_6k_1x_seed20260627` (epoch 16, EMA). Scores and receipts come from
+`plans/utility_studies_final6k_20260925/`, `plans/paired_finetune_intradataset_20260927/`,
+`plans/immicro_scsemi_comparison_20260927/`, `plans/floor01_campaign_20260923/`,
+`analysis/scsemi_benchmark_diffusion_20260913/` and the run folders named in each `data/sec*.csv`. Reference PDFs:
+`/home/sj_server_1/PBISC/paper-release-20260928/reference-papers/` (INDEX.md). Manuscript:
+`/home/sj_server_1/PBISC/paper-release-20260928/manuscript/`.
+- 2026-10-04 appendix (user decision: supplementary, appendix only, because it changes scSemiProfiler's official
+  output). Slide 38 is the divider "Appendix" and slide 39 is "Appendix: scSemiProfiler without output threshold"
+  (`tools/secA_scsemi_readout.py`, `data/secA_scsemi_readout{.csv,_slide_text.json}`).
+  - Inputs: `plans/paired_finetune_intradataset_20260927/scsemi/analysis/output_variants_20261003/`. The script checks
+    that its LoRA-5 and official scSemiProfiler values equal the section 9 slide data draw by draw.
+  - Result: without upstream's output threshold, scSemiProfiler's coverage rises in 5/5 cohorts, and 23/23 LoRA-5
+    comparisons (coverage, precision, composition, logFC r, DEG) keep their winner.
+  - The outline keeps its 10 sections (the builder requires 10). The pre-change copy is in
+    `_archive/2026-10-04_before_appendix/`. The deck has 39 slides.
+- 2026-10-05 update (user request: put the results so far into the deck). Pre-change copy: `_archive/2026-10-05_before_scratch_cv/`.
+  - New slide 23, "No pretraining: same cohort, from scratch" (`tools/sec07_no_pretraining.py`). Pilot on sepsis fold 0
+    (17 donors). Reads `fold0_scratch_20261005/` and the runs' RUN.json. The donor composition r is recomputed from
+    per_donor.csv and reproduces ablation_extra_donor.csv.
+  - New slide 28, "No pretraining: other cohorts, from scratch" (`tools/sec08_cross_scratch.py`). Held-out sepsis pilot.
+    Every arm is read from `paired_crosscohort_20261002/scores_masked/`: genes no training cohort measured are zeroed
+    in all arms, because the unmasked scratch arm puts 91–94 % of its UMI there (read from the generated_masked logs
+    since 2026-10-06).
+  - New slide 38, "Same training pairs: LoRA vs scSemiProfiler" (`tools/sec09_same_pairs.py`). scSemiProfiler 5-fold
+    CV with LoRA's own training pairs, plus GPU hours per fold from the receipts on either host. Cohorts that are still
+    running show "in progress"; rerun the script as they finish.
+  - These two slides now draw the scSemiProfiler CV / budget runs automatically:
+    - Slide 24, "Pairs needed" (diamonds; ImMicro so far): keyword 2 reworded, footnote extended.
+    - Slide 37, "5-fold CV" (yellow bar; ImMicro and Ji so far).
+  - `tools/sec09_paired_validation.py` reads the scSemiProfiler CV arm's Spearman / limma values from
+    `scores_contrast/<cohort>/scsemi_cv_20261002/pooled/` (contrast_extra.py --design scored). Pearson r and the Welch
+    DEG must match the scorer; the values stay blank until computed. Cohorts not yet run say "in progress".
+  - Agenda item 7 now reads "LoRA: compute, rank, K; training from scratch". The deck has 42 slides.
+  - New helper: `tools/grouped_bars.py`.
+- 2026-10-06 update (user request: check that the cross-cohort LoRA / full FT, cross-cohort from-scratch and
+  same-cohort from-scratch (+ recipes) results, with benchmarks and training times, and the scratch-vs-pretrained rare
+  states are in the deck; user decision: the from-scratch results go to the paper's Supplementary and the Discussion
+  presents them as a route to other tissues). Pre-change copy: `_archive/2026-10-06_before_supplementary/`.
+  - **Supplementary block (new divider, slides 40-43).**
+    - "No pretraining: same cohort, from scratch" and "No pretraining: other cohorts, from scratch" moved here from
+      sections 7 and 8. Their content is unchanged; slide 42's notes now name the cross-cohort slides by title.
+    - New "Rare states: from scratch vs pretrained" (`tools/secS_scratch_rare_states.py`).
+      - It reads the rare-state screen (`plans/rare_states_screen_20261004/out/sepsis_fold0_*` and
+        `sepsis_loco_masked_*`).
+      - The cross-cohort arms use the masked scoring (46 never-measured genes zeroed in every arm).
+      - The donor bootstrap reproduces the verified analysis in `out/scratch_vs_lora_20261005/REPORT.md`.
+  - **Section 10 "Next steps".**
+    - New "Next: other tissues from a few dozen pairs" (`tools/sec10_other_tissues.py`): a comparison table, pretrained
+      + LoRA vs from scratch.
+    - Every value is read from the deck's own source tables. The say text is the user's Discussion framing.
+  - **Slide 26 "Longer training on other cohorts".** The first keyword is now training minutes to saturation (RUN.json
+    wall seconds), with per-cohort minutes and steps in the notes (`tools/sec08_cross_cohort.py`).
+  - **Slide 34 "5 donors: time to apply".**
+    - The pretraining hours are read from `data/sec06_ft_method.csv`, no longer hard-coded.
+    - The note names the 'Fine-tuning with paired bulk + sc' slide by title. Its old "slide 15" pointed at the wrong
+      slide.
+  - Agenda items 7 and 10 updated. Spec change: `tools/restructure_20261006.py` (refuses to run twice). The deck has 45
+    slides; no Korean text.
+  - **Review pass (same day).** An independent 5-agent review of the changed slides confirmed 39 issues; all are
+    fixed. Pre-review copies of the touched scripts and the spec: `_archive/2026-10-06_before_supplementary/*.pre_review`.
+    - Slide 39: pretraining now reads 33.1 M train cells (`train_split_cells`; the 41.4 M corpus includes val/test).
+      The rare-state row follows the bootstrap verdicts on both sides (closer on ISG-high T / on DN2 B; labels and
+      states both arms miss by > 4-fold are not counted). "No corpus" became "come close": the scratch runs still took
+      the panel, value scaler and z-score constants from the PBMC checkpoint (RUN.json `kept_from_checkpoint`, now in
+      the notes). The say text carries the one-fold pilot qualifier; the footnote names both pilots.
+    - Slide 43: "no significant difference on average" (not "no difference"). Keyword 2 is the cycling T/NK count
+      (8 of 8 comparisons). The notes add the ISG-high T split by panel, the scratch-closer states (DN2 B, MS1), the
+      52-call tally, the fold-0 caveat (fold 0 is the own-pair LoRA's best fold for C1Q-high and cycling T/NK,
+      computed from `folds.csv`), label states unthinned, and "8 of 13 states shown".
+    - Slide 26: title "...: gap stays" and "drifts in Sepsis and Ji" are computed from the milestones (gap = no milestone
+      reaches the own-cohort coverage; drift = full fine-tune L1 up by >= 0.05). Minutes are in
+      `data/sec08_cross_cohort.csv` (wall_seconds, minutes, run_receipt) and in the numbers list.
+    - Slide 42: UMI share (94 %, 91–94 % across milestones), the observed share (0.014 %, from `cells_panel6k.npz`)
+      and both arms' stopping settings are read from the receipts; the "none" label sits inside the axes.
+    - Slide 34: keywords use the cohort means of the figure (17–59×, 57 min–4.1 h); the per-draw range 11–63× is in the
+      notes. The right panel shares the bars' rows and has a title.
+    - Talk text and pointers: slides 23, 25, 26, 45 (say), 22 (recipes description), 32 and 36 (slide pointers by
+      title), slide 1 subtitle "updated 2026-10-06".
+- 2026-10-08 update (user request: put the BayesPrism, CIBERSORTx and scSemiProfiler 5-fold results into the deck).
+  Pre-change copy: `_archive/2026-10-08_before_deconvolution/`.
+  - **Two new Supplementary slides** (`tools/secS_deconvolution.py`, `data/secS_deconvolution{.csv,_slide_text.json}`):
+    "Deconvolution with the same pairs: composition" and "...: disease signal".
+    - Source: `plans/bayesprism_paired_20261006/` (README, `results/RESULTS_full.md`, `RESULTS_cibersortx.md`).
+    - BayesPrism (recommended genes) and official CIBERSORTx (Fractions S-mode + HiRes) use the same paired donors
+      as the reference and the test samples' real bulk as the mixture, with the benchmark's own truth and scorer.
+    - Keywords and notes are computed from the tables.
+  - **Supplementary plan.** The planned "Bulk deconvolution methods" row is replaced by the two Ready slides.
+    `tools/figure_plan.py` now takes an optional per-slide note (`supplementary.notes`) and drops "Planned:" when
+    nothing is planned.
+  - **New scSemiProfiler results picked up (server_1):**
+    - Sepsis and Giroux 5-fold CV. Giroux was finalized on server_1 from sha-checked copies of its fold NPZs, and
+      rescored without sex-linked genes in `scores_nosex/giroux2022/scsemi_cv_20261002/pooled`.
+    - Sepsis equal-budget n = 5/10/20/40.
+    - Imperial 5-fold: finished on server_2 at 13:45 and was finalized on server_1 from sha-checked fold copies.
+      scSemiProfiler 5-fold now covers all five cohorts.
+    - Spearman/limma extras for sepsis, Giroux and Imperial; realism extras for all five cohorts.
+  - **Text changes from the new results:**
+    - "Same training pairs": keywords are data-driven. LoRA leads on coverage and precision in 5/5 cohorts;
+      composition 4/5 and logFC r 3/4, where scSemiProfiler leads in Giroux. The GPU-time ratio is 19–62×.
+    - sec09_same_pairs.py now reads the sepsis fold-0 pilot queue tag (`sep_f0`). For Giroux it quotes DEG from
+      scores_nosex, and for older pooled LoRA calls from pooled_deg.
+    - "5-fold CV" keyword: logFC r 3/4.
+    - "Pairs needed" (hand-written): scSemiProfiler coverage 0.04 / 0.10. On sepsis disease logFC r,
+      scSemiProfiler at 5–40 pairs (0.52–0.58) is above LoRA (0.29–0.48), while its top-50 DEG stays at 2–6.
+- 2026-10-08, new figure split (user decision; supersedes the 2026-10-06 split; pre-change copy
+  `_archive/2026-10-08_before_new_figure_plan/`).
+  - Fig. 2: LoRA fine-tuning with no disease metrics.
+  - Fig. 3: PBISC vs scSemiProfiler on healthy donors, with time as a main result.
+  - Fig. 4: disease benchmarks on the paired cohorts (PBISC versions, scSemiProfiler, and BayesPrism / CIBERSORTx
+    labelled as cell-type deconvolution; the deconvolution slides moved here from the Supplementary).
+  - Fig. 5: rare-cell fractions and detection.
+  - Fig. 6: new findings from the bulk-only sepsis and COVID samples (K = 1,000 and 4,000).
+  - The cross-cohort transfer slides moved to the Supplementary; their placement is open.
+  - New slide "Time: days for scSemiProfiler, minutes for LoRA" (`tools/fig3_time_days.py`, Fig. 3d).
+    - Inputs: GPU hours per 5-fold experiment from the receipts (`data/sec09_same_pairs.csv`, checked against
+      the scSemiProfiler event logs on both hosts), the 5-donor draws (`data/sec09_time_to_apply.csv`) and the
+      one-time pretraining (`data/sec06_ft_method.csv`).
+    - Results: ImMicro 24 h vs 1.2 h, sepsis 38 h vs 37 min. All runs: scSemiProfiler 184 h (7.7 days) vs PBISC
+      22 h including pretraining.
+    - The equal-budget scSemiProfiler runs are excluded: they reuse the 5-fold run's representative models.
+  - Panel status: Build / Run marks new slides or analyses still to do; Adapt marks slides to re-scope (healthy-only
+    Fig. 2-3, disease columns into Fig. 4).
+- 2026-10-08, comparison rules (user decision; memory feedback_paper_comparison_rules):
+  - **Time.** Only per-cohort GPU work counts: no PBISC pretraining, no GPU queue waits. The time slide now shows the
+    5-donor draws (main) next to the 5-fold runs (separate). All runs: 7.7 GPU-days vs 5.7 h.
+  - **No bulk-broadcast baseline.** It is removed from the SLE benchmark, CAT, deconvolution and "Pairs needed"
+    figures.
+    - The "Bulk broadcast baseline" slide is gone. Its partial r panel is now on the SLE benchmark slide
+      (0.21 vs 0.17).
+    - Still to rebuild in their own phases: Fig. 2e (ImMicro only) and Fig. 6a.
+  - **Main comparison = 5 pairs** (LoRA-5 vs scSemiProfiler-5); 5-fold is reported separately. Partial r is
+    emphasised (bold panel titles).
+  - **New `tools/fig4_disease.py`**, which checks all 200 logFC r / DEG values against the section-9 tables:
+    - "Disease signal with 5 pairs: LoRA-5 vs others" (4b);
+    - "5-fold CV: disease signal" (4c);
+    - "Sepsis: fine-tuning variants" (details).
+  - The old "5 donors: LoRA vs scSemiProfiler — disease signal" slide is retired. `tools/sync_sec09_text.py` lists it
+    in RETIRED.
+  - The deconvolution slides are retitled "Cell-type deconvolution, same pairs: ...".
+- 2026-10-08, healthy-only Figs 2-3 (`tools/fig23_healthy.py`).
+  - Eight ImMicro-only slides, re-plotted from the deck's own data tables; nothing is re-scored.
+  - Fig. 2 (no disease metrics):
+    - cells, realism, full / LoRA / condition encoder, pairs needed, rank and K;
+    - 5 donors (main) shown next to 5-fold (separate).
+  - Fig. 3 (vs scSemiProfiler):
+    - LoRA-5 vs scSemiProfiler-5 on 6 cell metrics: LoRA ahead on 6 of 6;
+    - the same pairs, 5 to all;
+    - scSemiProfiler copies its representative;
+    - time.
+  - The earlier all-cohort slides moved to the Supplementary (note "all five cohorts").
+  - "5-fold CV: train on the other 4 folds" is retired (it carried the broadcast; its content is now in Fig. 4c and
+    the healthy slides). It is listed in RETIRED of `tools/sync_sec09_text.py`.
+- 2026-10-08, Fig. 5 rare cells (`tools/fig5_rare_states.py`).
+  - Source: new plan `plans/rare_states_paired_20261008` (README). It covers 4 paired disease cohorts, the 5-donor
+    draws and 5-fold, and the arms zero-shot, calibration, scSemiProfiler and LoRA. Each NPZ is sha-checked and the
+    benchmark's Poisson read-out and labels are reproduced cell by cell.
+  - Three slides:
+    - fraction (5a);
+    - donors and disease direction (5b);
+    - 5-fold, separate (5c).
+  - Results over 56 state × cohort pairs, 5 donors:
+    - LoRA-5 closer to the observed fraction than scSemiProfiler-5 in 51/56;
+    - within 2-fold: 19 vs 5;
+    - better donor ranking: 43/56;
+    - disease direction kept: 73% vs 61%.
+  - Fig. 6a no longer mentions the broadcast. No slide text mentions it now.
+- 2026-10-10, last broadcast mention removed from a figure (found while rewriting the manuscript from this deck).
+  - Fig. 2d "How we score generated cells" (`tools/sec04_metrics_overview.py`) still listed "broadcast; split-half
+    ceiling" as the comparator of logFC r inside the table image. It now reads "zero-shot; partial r (vs bulk)".
+  - Old tool, CSV and figure: `_archive/2026-10-10_metrics_overview_broadcast/`. The deck was rebuilt (76 slides); a
+    text search of every slide, the notes and the PDF twin of every figure a slide uses finds no "broadcast". Three
+    figure files of retired slides still contain it and are not used: `sec04_sle_broadcast`, `sec09_fewpair_disease`,
+    `sec09_fivefold`.
+  - The manuscript was rewritten from this deck on 2026-10-10 (`paper-release-20260928/manuscript/`,
+    `editorial/rewrite_20261010.md`); its figures are copies of this folder's figure PDFs.
+- 2026-10-10, LoRA rank figure key (`tools/sec07_rank_sweep.py`): "LoRA rank r = 16 (used in the deck)" now reads
+  "(used throughout)", because the same panel is Supplementary Fig. 12a of the manuscript. Old files:
+  `_archive/2026-10-10_rank_sweep_label/`. Deck rebuilt (76 slides).

paper-releases/pbisc-diffusion/updates/20261013T022035Z-pbisc-red-scsemi-blue/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/tools/build_results_review_20261013.py

@@ -0,0 +1,287 @@
+"""Build the reviewed 34-slide deck with the original 76-slide renderer/style.
+
+CPU only. Existing figures are reused intact; new plots read small saved CSVs.
+The original deck and its specification are never rewritten.
+"""
+from pathlib import Path
+from datetime import datetime, timezone, timedelta
+import copy
+import csv
+import hashlib
+import json
+import os
+import sys
+import tempfile
+
+if os.environ.get('CUDA_VISIBLE_DEVICES') != '':
+    raise SystemExit('Set CUDA_VISIBLE_DEVICES empty for this CPU-only builder')
+D = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(D))
+import generate_deck as original
+from pptx import Presentation
+from pptx.util import Inches
+from results_review_style_plots import build as build_plots
+from results_review_style_panels import build as build_panels
+from review_palette_pdf import recolor_pdf, render_pdf_to_png
+
+DATA = D/'data'
+ORIGINAL = json.loads((DATA/'slides_spec.json').read_text())['slides']
+PREVIOUS = json.loads((DATA/'style_review_20261013/before_results_review_20261013_provenance.json').read_text())
+STEM = D/'PBISC-Diffusion_results_review_20261013'
+SOURCES = {}
+
+
+def apply_method_palette(slides):
+    """Use figure-specific method roles; preserve biology and numerical geometry."""
+    policy_path=source(DATA/'method_palette_20261013/palette.json','user-approved method colour policy')
+    policy=json.loads(policy_path.read_text())
+    target=D/'figures/method_palette_20261013'
+    target.mkdir(exist_ok=True)
+    records=[]
+    converted={}
+    for slide_spec in slides:
+        originals=list(slide_spec.get('figures',[]))
+        if originals: slide_spec['figures_before_palette']=originals
+        replacements=[]
+        for original_asset in originals:
+            asset_path=D/original_asset
+            stem=asset_path.stem
+            rule=policy['rules'][stem]  # unknown assets require an explicit semantic policy
+            if not rule['mapping'] and not rule.get('rectangle_rules'):
+                replacements.append(original_asset)
+                records.append({'asset':original_asset,'action':'preserved','reason':rule['reason']})
+                continue
+            if stem not in converted:
+                input_pdf=source(rule.get('source_pdf',asset_path.with_suffix('.pdf')),'original vector figure for method recolouring')
+                input_png=source(asset_path,'original figure image')
+                output_pdf=target/(stem+'.pdf'); output_png=target/(stem+'.png')
+                with tempfile.TemporaryDirectory(prefix='.build-',dir=target) as tmp:
+                    candidate_pdf=Path(tmp)/(stem+'.pdf');candidate_png=Path(tmp)/(stem+'.png')
+                    proof=recolor_pdf(input_pdf,candidate_pdf,rule['mapping'],
+                        rectangle_rules=rule.get('rectangle_rules'),
+                        rgb_tolerance=5.1e-7 if stem=='fig1b_overview_nature' else 1e-8)
+                    render=render_pdf_to_png(candidate_pdf,candidate_png,dpi=300 if stem=='fig1b_overview_nature' else 200)
+                    candidate_pdf.replace(output_pdf);candidate_png.replace(output_png)
+                proof['destination']=str(output_pdf)
+                render['pdf']=str(output_pdf);render['png']=str(output_png)
+                proof.update({'render':render,'original_png':str(input_png),'original_png_sha256':sha(input_png),
+                              'semantic_reason':rule['reason'],'policy_sha256':sha(policy_path)})
+                proof_path=target/(stem+'.palette.json')
+                proof_path.write_text(json.dumps(proof,indent=2)+'\n')
+                for path in (output_pdf,output_png,proof_path):source(path,'method palette variant or preservation proof')
+                converted[stem]=relative(output_png)
+                records.append({'asset':original_asset,'action':'recolored','output':relative(output_png),
+                    'proof':relative(proof_path),'reason':rule['reason'],
+                    'preservation_pass':proof['preservation_pass']})
+            replacements.append(converted[stem])
+        if originals:slide_spec['figures']=replacements
+    catalog={'schema':'pbisc.deck-method-palette.v1','primary':policy['primary'],
+             'recolored_figures':len(converted),'records':records}
+    catalog_path=DATA/'method_palette_20261013/ASSET_MAP.json'
+    catalog_path.write_text(json.dumps(catalog,indent=2)+'\n')
+    source(catalog_path,'method palette coverage and per-figure proof locations')
+    return catalog
+
+
+def sha(path):
+    with Path(path).open('rb') as f:
+        return hashlib.file_digest(f,'sha256').hexdigest()
+
+
+def source(path, role):
+    path=Path(path).resolve()
+    if not path.is_file():
+        raise FileNotFoundError(path)
+    SOURCES[str(path)]={'sha256':sha(path),'role':role}
+    return path
+
+
+def relative(path):
+    return str(Path(path).resolve().relative_to(D))
+
+
+def from_original(number, title, section, protocol, keywords=None):
+    item=copy.deepcopy(ORIGINAL[number-1])
+    assert item['kind']=='content'
+    item.update(title=title,section_label=section,footnote=protocol,original_slide=number)
+    item['footnote_full']=protocol+'\nOriginal context: '+item.get('footnote_full',item.get('footnote',''))
+    if keywords is not None:
+        item['keywords']=keywords
+    # The original reference line remains visible when it fits. Full citations
+    # always stay in notes, unchanged from the original source specification.
+    if item.get('refs') and len(original.wrap(protocol,original.FOOT_PT,original.FOOT_W))>1:
+        item.pop('refs')
+    return item
+
+
+def fresh(title,section,protocol,asset,keywords=()):
+    return {'kind':'content','title':title,'section_label':section,'layout':'full',
+            'figures':[relative(Path(asset).with_suffix('.png'))],
+            'keywords':list(keywords),'footnote':protocol,'footnote_full':protocol,
+            'sources':str(asset)}
+
+
+def main():
+    plots=build_plots()
+    panels=build_panels()
+    for row in plots['inputs']:
+        source(row['path'],row.get('role','saved plot data'))
+    for path in plots['assets']:
+        source(path,'restyled saved-table plot')
+    for value in panels.values():
+        for key in ('png','pdf','svg','provenance'):
+            if value.get(key):
+                source(value[key],'restyled panel '+key)
+        proof=json.loads(Path(value['provenance']).read_text())
+        # Preserve the complete panel-specific evidence even if its schema differs.
+        for field in ('inputs','sources'):
+            rows=proof.get(field,[])
+            if isinstance(rows,dict): rows=[{'path':p} for p in rows]
+            for row in rows:
+                p=row.get('path',row.get('source')) if isinstance(row,dict) else row
+                if p: source(p,'saved panel data')
+    comp,rareobs,rarecompare=plots['figures']
+    slides=[
+        {'kind':'title','title':'PBISC-Diffusion','subtitle':'Results review · existing evidence and protocol updates · 13 October 2026'},
+        {'kind':'agenda','title':'Six figures: evidence available now','items':[
+            'Fig. 1  Pretraining, diffusion and bulk conditioning',
+            'Fig. 2  Model design and paired-data requirements',
+            'Fig. 3  Healthy reconstruction: quality, composition and time',
+            'Fig. 4  Disease signals and deconvolution benchmarks',
+            'Fig. 5  Rare cell states: corrected analysis of existing outputs',
+            'Fig. 6  Bulk-only reproduction: awaiting the new dataset']},
+    ]
+    slides += [
+        from_original(7,'PBISC-Diffusion: pretraining to generated cells','Fig. 1a',
+            'Schematic. Conditioning recipe size and the number of generated cells are distinct; no cell-type quotas.',
+            []),
+        from_original(8,'Bulk conditioning','Fig. 1b',
+            'Final 6k model. Recipe size K conditions the bulk; the number of generated cells is chosen separately.',
+            ['One shared bulk condition; independent cell draws', 'Expression, depth and measured-gene masks', 'No cell-type or disease labels as model input']),
+        from_original(9,'Pretraining corpus','Fig. 1c',
+            'Corpus labels describe the data; they are not model inputs. Dataset composition is not sampler exposure.'),
+        fresh('Native-pseudobulk model comparisons','Fig. 2a',
+            'Completed held-out rescoring. Architecture: 1% detection floor; gene-panel comparison: 0.1% floor.',panels['ablation']['pdf']),
+        from_original(12,'Gene panels: performance and coverage','Fig. 2b',
+            'Existing panel benchmark. 6k: five sampling seeds; other panels: one seed. Different axis from the architecture sweep.'),
+        fresh('Healthy bulk: full fine-tuning and LoRA rank','Fig. 2c',
+            'Original donor-level five-fold comparison. LoRA rank 16 is a quality/parameter compromise.',panels['rank']['pdf']),
+    ]
+    # Preserve the audited scope of every retained result. Figure assets and
+    # takeaways are reused from the original 76-slide deck, without cropping.
+    mapping=[
+        (31,'Historical paired-data learning curve','Fig. 2d'),
+        (26,'Additional pairs improve donor-level information','Fig. 2e'),
+        (17,'Measured bulk differs from pseudobulk','Fig. 3a'),
+        (30,'Healthy donors: exactly five paired samples','Fig. 3b'),
+    ]
+    for position,(old,title,label) in enumerate(mapping,start=8):
+        assert PREVIOUS['slides'][position]['old_deck_slide']==old
+        p=PREVIOUS['slides'][position]['protocol']
+        if old==31:
+            p='LoRA: n training + 7 validation. scSemi: n + 7 representatives, with CV-HVG reuse. Not the strict total-n protocol.'
+        slides.append(from_original(old,title,label,p))
+    slides.append(fresh('Composition: error and donor-level wins','Fig. 3c',
+        'Exactly five pairs per draw. Absolute errors averaged within each held-out donor; 107 donors, equal weight. Descriptive.',comp))
+    mapping=[
+        (32,'Representative-dependent composition','Fig. 3d'),
+        (34,'Adaptation and generation time','Fig. 3e'),
+        (37,'Paired cohorts and evaluation units','Fig. 4a'),
+        (38,'Disease signals: five-donor draws','Fig. 4b'),
+        (39,'Disease signals: five-fold validation','Fig. 4c'),
+        (40,'Disease-gene agreement across list lengths','Fig. 4d'),
+        (41,'Cell-type expression: deconvolution comparison','Fig. 4e'),
+        (46,'Composition: deconvolution comparison','Fig. 4f'),
+    ]
+    for position,(old,title,label) in enumerate(mapping,start=13):
+        assert PREVIOUS['slides'][position]['old_deck_slide']==old
+        p=PREVIOUS['slides'][position]['protocol']
+        kws=None
+        if old==32:
+            kws=['Composition varies with the selected representative',
+                 'Compare generated fractions with held-out observed fractions']
+        elif old==37:
+            kws=['403 paired samples, 275 donors',
+                 'Identity screening where available; donor-level splits',
+                 'Ji bulk: whole blood, not PBMC']
+        elif old==46:
+            kws=['PBISC closer than BayesPrism in 22/25 draws, than CIBERSORTx in 24/25',
+                 'Composition compared with held-out observed cells',
+                 'Five-fold: PBISC lowest error in 4 of 5 cohorts']
+        slides.append(from_original(old,title,label,p,kws))
+    slides += [
+        fresh('Rare-state frequencies in observed cells','Fig. 5a',
+            'Original five-donor outputs. Median across draws; state share within its parent population. Controls included.',rareobs),
+        fresh('Rare-state fidelity: zero-aware reanalysis','Fig. 5b',
+            'Original five-donor outputs; zero draws retained and actual donor IDs. 59 state/cohort combinations, including controls.',rarecompare),
+        fresh('Behcet: one-gene and multi-gene C1Q rules','Fig. 5c',
+            'Descriptive: unmatched reference includes training donors. Same three pairs; two identity-pass and one uncertain.',panels['behcet']['pdf']),
+        {'kind':'status','title':'Remaining work','section_label':'Figure status',
+         'rows':[
+            ['Protocol updates','Total n = 5/10/20/40 without extra validation; exact-five samples in multi-visit cohorts.'],
+            ['Analysis of existing outputs','Patient-aware contrasts, rare-state scoring and healthy cell visualizations.'],
+            ['Figure 6: new data','Reproduce a published finding in the user-prepared bulk-only cohort.']],
+         'footnote':'Existing results remain visible. Protocol updates and missing-data work are identified separately.'},
+    ]
+    mapping=[
+        (19,'Control-cell embedding diagnostic'),
+        (58,'Generated-cell quality across cohorts'),
+        (60,'Precision and coverage across neighbourhood sizes'),
+        (62,'Same training pairs: five-fold comparison'),
+        (68,'Cross-cohort pairs do not replace local pairs'),
+        (69,'Longer cross-cohort training leaves a gap'),
+        (70,'From-scratch control: evidence and limits'),
+        (74,'scSemiProfiler readout sensitivity'),
+        (42,'SLE pseudobulk development benchmark'),
+    ]
+    for position,(old,title) in enumerate(mapping,start=25):
+        assert PREVIOUS['slides'][position]['old_deck_slide']==old
+        p=PREVIOUS['slides'][position]['protocol']
+        slides.append(from_original(old,title,'Supporting',p))
+    assert len(slides)==34
+    palette=apply_method_palette(slides)
+    prs=Presentation();prs.slide_width=Inches(original.SLIDE_W);prs.slide_height=Inches(original.SLIDE_H)
+    for i,s in enumerate(slides,start=1):
+        kind=s['kind']
+        if kind=='title': original.build_title(prs,s)
+        elif kind=='agenda': original.build_agenda(prs,s,i)
+        elif kind=='status':
+            slide=prs.slides.add_slide(prs.slide_layouts[6]);original.white_bg(slide)
+            original.add_title(slide,s['title'],s['section_label'])
+            for row,(title,body) in enumerate(s['rows']):
+                y=1.35+row*1.55
+                original.add_text(slide,original.X0,y,11.6,.4,[title],20,bold=True)
+                original.add_text(slide,original.X0,y+.5,11.6,.8,[body],16,color=original.GREY)
+            original.add_footer(slide,s['footnote'],i)
+        else:
+            original.build_content(prs,s,i)
+        for f in s.get('figures',[]):
+            source(D/f,'figure reused intact' if s.get('original_slide') else 'restyled figure')
+            vector=(D/f).with_suffix('.pdf')
+            if vector.is_file(): source(vector,'figure vector companion')
+        for sh in prs.slides[-1].shapes:
+            assert sh.left>=0 and sh.top>=0 and sh.left+sh.width<=prs.slide_width+2 and sh.top+sh.height<=prs.slide_height+2,(i,sh.name)
+    prs.save(STEM.with_suffix('.pptx'))
+    for p in [D/'generate_deck.py',D/'tools/style.py',DATA/'slides_spec.json',
+              Path(__file__),D/'tools/results_review_style_plots.py',D/'tools/results_review_style_panels.py',
+              DATA/'style_review_20261013/before_results_review_20261013_provenance.json',
+              D/'tools/review_palette_pdf.py']:
+        source(p,'renderer, style or audited content source')
+    spec={'deck':{'output_pptx':STEM.name+'.pptx','style_source':'generate_deck.py + tools/style.py'},'slides':slides}
+    (DATA/'results_review_20261013_spec.json').write_text(json.dumps(spec,indent=2)+'\n')
+    manifest={'schema':'pbisc.results-review.reuse.v2','style_revision':4,
+        'as_of_kst':datetime.now(timezone(timedelta(hours=9))).isoformat(),
+        'slides':slides,'sources':SOURCES,'new_training':False,'new_generation':False,
+        'cpu_work':'Saved CSV plots; healthy composition now compares paired absolute errors and donor wins. No new model outputs.',
+        'plot_calculations':plots.get('calculations'), 'panel_outputs':panels,
+        'method_palette':palette,
+        'pptx_sha256':sha(STEM.with_suffix('.pptx')),'pdf_status':'awaiting PPTX export and visual verification'}
+    (DATA/'results_review_20261013_provenance.json').write_text(json.dumps(manifest,indent=2)+'\n')
+    with (DATA/'results_review_20261013_slide_map.csv').open('w') as f:
+        w=csv.DictWriter(f,fieldnames=['slide','section','title','protocol','old_deck_slide']);w.writeheader()
+        for i,s in enumerate(slides,1):w.writerow({'slide':i,'section':s.get('section_label',''),
+            'title':s['title'],'protocol':s.get('footnote',''),'old_deck_slide':s.get('original_slide','')})
+    print(json.dumps({'slides':len(slides),'sources':len(SOURCES),'pptx':str(STEM.with_suffix('.pptx'))}))
+
+
+if __name__=='__main__':main()