Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261011T120708Z-manuscript-introduction-docx/diffusion/dataset/server_1/manuscript/README.md

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+# PBISC-Diffusion — Nature Communications 초안
+
+**2026-10-10 전면 재작성.** 이 원고는 paper-summary 덱을 기준으로 처음부터 다시 쓴 영문 초안입니다.
+
+**2026-10-11 Introduction 교체.** `PBISC-Diffusion_Abstract_Introduction.docx`의 Introduction 5개 문단을 그대로 반영했습니다. 인용 번호는 BibTeX 키로 변환했고, 새로 인용한 문헌을 추가했습니다. 원문 해시와 인용 대응표는 [editorial/introduction_import_20261011.json](editorial/introduction_import_20261011.json)에 있습니다.
+
+- 기준 덱: `immun-pbmc-fm/model_8_sparse_data_diffusion/presentations/2026-09-30--paper-summary--r00/`, 76장, 2026-10-10 재빌드.
+- 구성: 본문, Methods, Supplementary Information, 참고문헌, 그림.
+- 그림 구성은 덱의 `data/figure_plan.json`을 따라 본문 그림 6개입니다.
+- **Figure 6(bulk-only 데이터에서의 발견)은 "to be continued" 자리표시입니다.** 생성 현황만 적었고 발견은 쓰지 않았습니다.
+- 저자 정보, 공개 저장소 링크 등 미완 항목은 본문에 빨간 `[TODO: …]`로 표시했습니다. 따라서 이 패키지는 제출 최종본이 아닙니다.
+- 작업 과정, 적용한 규칙, 근거 충돌의 해소, 남은 항목은 [editorial/rewrite_20261010.md](editorial/rewrite_20261010.md)에 있습니다.
+- 집필 지침서는 [editorial/rewrite_20261010_brief.md](editorial/rewrite_20261010_brief.md)입니다.
+- 재작성 전 원고(10-06의 커밋하지 않은 Figure 1 수정 포함)는 `archive/pre_rewrite_20261010/`에 그대로 보존했습니다. `archive/`는 git에서 제외된 로컬 사본입니다.
+
+## 연결된 GitHub와 Overleaf 업데이트
+
+이 원고의 GitHub 저장소는 [junidude/PBISC-diffusion-overleaf](https://github.com/junidude/PBISC-diffusion-overleaf)입니다. Overleaf에서 내보낸 `Initial Overleaf Import` 이력을 그대로 유지합니다. 서버의 현재 원고 폴더가 이 저장소의 Git 작업 폴더입니다.
+
+```bash
+cd /home/sj_server_1/PBISC/paper-release-20260928/manuscript
+git switch main
+git pull --ff-only origin main
+```
+
+서버에서 수정한 내용은 커밋하여 GitHub의 `main`에 반영합니다. 공동 작업은 변경 브랜치에서 검토한 뒤 PR로 합칩니다.
+
+이후 웹 Overleaf에서 **Integrations → GitHub → Pull GitHub changes into Overleaf**를 실행해야 갱신됩니다. **GitHub에 push하는 것만으로 Overleaf가 자동 갱신되지는 않습니다.** [공식 동기화 안내](https://docs.overleaf.com/integrations-and-add-ons/git-integration-and-github-synchronization/github-synchronization)
+
+반대로 웹 Overleaf에서 수정했다면 다음 순서로 가져옵니다.
+1. 같은 메뉴의 **Push Overleaf changes to GitHub**를 실행합니다.
+2. 서버에서 `git pull --ff-only`로 가져옵니다.
+3. `pull`이 충돌이나 이력 분기를 알리면 강제로 덮어쓰지 말고, 두 변경을 확인해 병합합니다.
+
+이 연결에는 GitHub 인증을 사용하며, 별도의 Overleaf Git 토큰은 필요하지 않습니다.
+
+2026-10-11 갱신은 전체 재작성본(본문·그림·보충자료)과 새 Introduction을 함께 포함합니다.
+
+## Overleaf에서 열기
+
+- Main document는 `main.tex`, Compiler는 pdfLaTeX로 지정합니다.
+- 보충자료는 Main document를 `supplementary.tex`로 바꿔 따로 컴파일합니다.
+- `overleaf_upload.zip`은 `scripts/build_upload_zip.py`로 다시 만들 수 있습니다.
+
+공식 Springer Nature `sn-jnl.cls`와 Nature 인용 스타일 `sn-nature.bst`가 함께 들어 있습니다. 컴파일에는 shell escape, 서버 절대경로, 모델 체크포인트, GPU가 필요하지 않습니다.
+
+## 파일 안내
+
+| 파일/폴더 | 내용 |
+|---|---|
+| [main.tex](main.tex), [main.pdf](main.pdf) | 본문(Abstract, Introduction, Results, Discussion, Methods, 선언문, 참고문헌). 참고문헌 뒤에 Figure 1–6과 범례가 이어집니다. |
+| [supplementary.tex](supplementary.tex), [supplementary.pdf](supplementary.pdf) | Supplementary Notes 1–5, Supplementary Methods, Supplementary Tables 1–4, Supplementary Figs 1–17 |
+| [macros.tex](macros.tex) | 두 문서가 함께 쓰는 매크로: `\method{}`, `\todo{}`, 그림 배치(`\mainfigure`, `\figpanel` 등) |
+| [sections/](sections/) | 본문 조각(`abstract`, `introduction`, `results_fig1`–`results_fig6`, `discussion`, `methods_model`, `methods_paired`, `methods_eval`, `declarations`), 그림 블록(`fig1`–`fig6`), 보충 조각(`supp_*`) |
+| [references.bib](references.bib) | 인용한 문헌의 BibTeX. 새 항목은 Crossref 또는 reference-papers PDF로 확인했습니다. 학회 논문은 `sn-nature.bst`의 표시 문제 때문에 `@misc`로 넣었습니다. |
+| [figures/deck_20261010/](figures/deck_20261010/) | 덱의 벡터 그림 패널(그대로 배치). 출처와 sha256은 `PROVENANCE.json`에 있습니다. 180 mm 저널 레이아웃은 아직 그리지 않았습니다. |
+| [figures/figS_corpus.pdf](figures/figS_corpus.pdf) | Supplementary Fig. 1(사전학습 코퍼스) |
+| [source_data/corpus/](source_data/corpus/) | 코퍼스 수치 표(Supplementary Note 1) |
+| [editorial/](editorial/) | 공동저자 검토용 기록. 재작성 기록은 `rewrite_20261010*.md`입니다. |
+
+`figures/`의 `fig02_*`–`fig06_*`, `figS_calibration_*`, `tables/` 등은 재작성 전 원고의 자료입니다. 새 원고는 이 파일들을 쓰지 않습니다.
+
+## 로컬에서 다시 빌드하기
+
+```bash
+tectonic --keep-logs main.tex
+tectonic --keep-logs supplementary.tex
+# 또는: latexmk -pdf -interaction=nonstopmode main.tex; latexmk -pdf -interaction=nonstopmode supplementary.tex
+```
+
+그림 패널은 덱 폴더의 `figures/*.pdf`를 복사한 것입니다. 덱 그림이 바뀌면 다음 순서로 반영합니다.
+1. 덱의 해당 `tools/*.py`를 다시 실행합니다.
+2. 결과 PDF를 `figures/deck_20261010/`에 복사합니다.
+3. `PROVENANCE.json`의 sha256을 갱신합니다.
+
+원고 형식은 [Nature Communications Article 안내](https://www.nature.com/ncomms/submit/article)를 목표로 삼았습니다. 기준은 제목 15단어 이하, 초록 200단어 이하, 본문 약 5,000단어, 범례 350단어 이하입니다. 공식 템플릿 사용과 컴파일 성공이 투고 요건 충족을 뜻하지는 않습니다.

paper-releases/pbisc-diffusion/updates/20261011T120708Z-manuscript-introduction-docx/diffusion/dataset/server_1/manuscript/editorial/introduction_import_20261011.json

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+{
+  "source_file": "PBISC-Diffusion_Abstract_Introduction.docx",
+  "source_sha256": "7bc00dc09a6b93b5add8d9a0f281daef854fd1ecbf89348b2cd151468ee863e6",
+  "section": "Introduction",
+  "paragraph_count": 5,
+  "citation_mapping": {
+    "1": "newman2019cibersortx",
+    "2": "wang2019music",
+    "3": "jew2020bisque",
+    "4": "tsoucas2019dwls",
+    "5": "menden2020scaden",
+    "6": "chen2022tape",
+    "7": "chu2022bayesprism",
+    "8": "barbosa2021blade",
+    "9": "wang2021bmind",
+    "10": "avilacobos2020benchmarking",
+    "11": "sturm2019evaluation",
+    "12": "dietrich2026omnideconv",
+    "13": "community2024assessment",
+    "15": "liao2022bulk2space",
+    "16": "zeng2024omicverse",
+    "17": "wang2024scsemiprofiler",
+    "18": "luo2024scdiffusion",
+    "19": "scdiffusionx2026",
+    "20": "ostheimer2025sparse",
+    "21": "song2021ddim",
+    "22": "delafuente2025sweetwater"
+  },
+  "source_paragraphs": [
+    "Bulk RNA sequencing of blood and tissue remains the most widely used transcriptomic assay in clinical research, including large clinical trials, biobanks and disease cohorts. Each bulk profile, however, is a mixture: changes in cell-type abundance are confounded with changes in cell-state-specific expression, and disease signals carried by minority populations are diluted. Single-cell RNA sequencing resolves these components, but cost and sample logistics still limit it to tens or hundreds of donors per study.",
+    "Computational deconvolution has been the principal bridge between the two. Digital cytometry methods such as CIBERSORTx1, MuSiC2, Bisque3, DWLS4, Scaden5 and TAPE6 estimate cell-type proportions from bulk using single-cell-derived signatures, and BayesPrism7, BLADE8 and bMIND9 additionally infer cell-type-specific expression. Despite of the highly advanced calculation algorithms, the accuracy of each model’s output highly depends on data transformation, reference choice and annotation granularity, degrades for closely related and rare cell types, and changes rank when the reference differs from the bulk cohort10,11,12,13. These methods also return proportions or cell-type averages, which cannot represent heterogeneity within a cell type, thus masking many disease-associated states that could be inferred by transcriptomics.",
+    "Due to the advance in machine learning and neural networking, generative models now offer a route to single-cell resolution. Bulk2Space15 and BulkTrajBlend16 estimate proportions and then draw cells from a matched reference, and scSemiProfiler17 adapts real single cells from actively selected cohort representatives toward each target’s bulk profile. These designs either constrain generated populations to a proportion-weighted sum or anchor every generated sample to a representative donor profiled within the same cohort, and their evaluation has relied largely on pseudobulk aggregated from single-cell data17. In parallel, diffusion models now generate realistic single-cell transcriptomes18,19, and sparsity-aware formulations that diffuse expression values and their zero pattern jointly preserve the exact zeros that dominate single-cell data20. These models are conditioned on labels, not on a donor’s bulk transcriptome.",
+    "A largely untested assumption shared by these approaches is that measured bulk RNA-seq and single-cell pseudobulk are interchangeable. The two assays differ in library chemistry, transcript coverage, capture efficiency and gene-length sensitivity, and distribution shifts between pseudobulk used for training and real bulk encountered at inference are currently unaccounted for22. If this gap is large and systematic, a model trained on pseudobulk is evaluated in one domain and deployed in another, and the consequences for biological conclusions drawn from generated or deconvolved cells have not been quantified.",
+    "Here we present PBISC-Diffusion, a bulk-conditioned generative model that combines joint value–support diffusion in gene space, adapted from Sparse Data Diffusion20, with deterministic sampling21 and pretraining on peripheral blood mononuclear cells from 22 independent bulk to single cell paired datasets."
+  ],
+  "target": "sections/introduction.tex",
+  "target_sha256": "a00c56582dabb347b03c29f5c330cf7b6ae1a031b0024c65f233efbb17a2097b",
+  "scope": "Introduction only from the attachment; the existing full manuscript rewrite is included in the GitHub update as explicitly confirmed by the user.",
+  "verification": {
+    "docx_paragraphs_match": 5,
+    "isolated_git_index_build": true,
+    "documents": {
+      "main": {
+        "build_exit_code": 0,
+        "unresolved_citations_or_references": 0,
+        "pdf_sha256": "ba7bf086cd64bcf7b4269f4ee5e6f0c3eb01132f69fa258484c24ed0a03adfc9",
+        "pages": 49
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+      "supplementary": {
+        "build_exit_code": 0,
+        "unresolved_citations_or_references": 0,
+        "pdf_sha256": "a02ed4b4ce1f452edae524bfd2e899bc9cc600bf32935fc53ee00fdec893f787",
+        "pages": 46
+      }
+    },
+    "whitespace_check": "Passed for authored files; unchanged generated SVG path whitespace preserved to retain provenance hashes."
+  },
+  "content_note": "The supplied final sentence says \"22 independent bulk to single cell paired datasets\". It is preserved verbatim; existing Methods describes pretraining with constructed pseudobulk. This import does not independently validate that claim."
+}

paper-releases/pbisc-diffusion/updates/20261011T120708Z-manuscript-introduction-docx/diffusion/dataset/server_1/manuscript/editorial/reference_metadata/barbosa2021blade.introduction_20261011.crossref.json

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