Archive 4 checkpoints (1-4)

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 paper-releases/pbisc-diffusion/updates/20261010T153319Z-manuscript-rewrite-from-deck/diffusion/dataset/server_1/model8/presentations/2026-09-30--paper-summary--r00/PBISC-Diffusion_paper_summary_20260930.pdf filter=lfs diff=lfs merge=lfs -text
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+paper-releases/pbisc-diffusion/updates/20261012T011725Z-manuscript-title-abstract/diffusion/dataset/server_1/manuscript/main.pdf filter=lfs diff=lfs merge=lfs -text

paper-releases/pbisc-diffusion/updates/20261012T011725Z-manuscript-title-abstract/diffusion/dataset/server_1/manuscript/main.pdf

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paper-releases/pbisc-diffusion/updates/20261012T011725Z-manuscript-title-abstract/diffusion/dataset/server_1/manuscript/main.tex

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+% Overleaf main document. Uses the official Springer Nature authoring class.
+% Select pdfLaTeX on Overleaf; all figures and citations are local files.
+% Rewritten from scratch on 2026-10-10 from the paper-summary deck (76 slides, 2026-10-08).
+% The previous version is kept in archive/pre_rewrite_20261010/.
+\documentclass[pdflatex,sn-nature]{sn-jnl}
+\usepackage{graphicx}
+\usepackage{amsmath,amssymb}
+\usepackage{booktabs}
+\usepackage{array}
+\usepackage{multirow}
+\usepackage{longtable}
+\usepackage{microtype}
+\usepackage{xcolor}
+\input{macros}
+\begin{document}
+\raggedbottom
+\title{Diffusion model with bulk expression conditioning can generate precise single cell dataset from bulk transcriptome with preserved disease specific gene expression signature}
+\input{authors}
+\abstract{\input{sections/abstract}}
+\keywords{bulk RNA sequencing, single-cell generation, diffusion model, low-rank adaptation, paired bulk and single-cell data, immune cells}
+\maketitle
+\raggedbottom
+\input{sections/introduction}
+\section*{Results}
+\input{sections/results_fig1}
+\input{sections/results_fig2}
+\input{sections/results_fig3}
+\input{sections/results_fig4}
+\input{sections/results_fig5}
+\input{sections/results_fig6}
+\input{sections/discussion}
+\input{sections/methods_model}
+\input{sections/methods_paired}
+\input{sections/methods_eval}
+\input{sections/declarations}
+\bibliography{references}
+% Figures with their legends, each on its own page(s). Panels are the deck's vector graphics, placed as-is;
+% the 180 mm journal layout is still to be drawn.
+\input{sections/fig1}
+\input{sections/fig2}
+\input{sections/fig3}
+\input{sections/fig4}
+\input{sections/fig5}
+\input{sections/fig6}
+\end{document}

paper-releases/pbisc-diffusion/updates/20261012T011725Z-manuscript-title-abstract/diffusion/dataset/server_1/manuscript/sections/abstract.tex

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+Bulk RNA sequencing offers cost-effective, sensitive transcriptome profiling but averages expression across cells, obscuring cellular heterogeneity. Deconvolution methods estimate cell-type composition and expression from the bulk dataset but do not resolve variation within cell types. Generative approaches infer individual cells using various machine learning methods but are constrained by small reference cohorts and there are mismatch between pseudobulk and measured bulk RNA-seq. Here we introduce PBISC, a diffusion model pretrained on over 33 million single cells for generating single-cell RNA profiles conditioned on a sample's bulk transcriptome. Using paired bulk and single-cell data from minimum five donors, the model adapted to the target bulk sequencing protocol and generates single-cell profiles for the remaining cohort. By repeated sampling from independent Gaussian noise, we were able to produce a synthetic cell population representing the sample's cellular heterogeneity. Pre-annotated cell types were not used for either training or generation, allowing PBISC to generate rare cell populations without defining them as separate categories in advance. Furthermore, PBISC showed improved preservation of disease-associated expression changes relative to comparator methods in most evaluated settings. Together, these results support extending analyses at single-cell resolution to large cohorts profiled by bulk RNA-seq.

paper-releases/pbisc-diffusion/updates/20261012T011725Z-manuscript-title-abstract/diffusion/dataset/server_1/manuscript/supplementary.tex

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+% Supplementary Information. Compile independently with pdfLaTeX; all dependencies are local.
+% Rewritten from scratch on 2026-10-10 from the paper-summary deck; previous version in archive/pre_rewrite_20261010/.
+\documentclass[pdflatex,sn-nature]{sn-jnl}
+\usepackage{graphicx}
+\usepackage{amsmath,amssymb}
+\usepackage{booktabs,array,longtable,multirow}
+\usepackage{microtype}
+\usepackage{xcolor}
+\usepackage{adjustbox}
+\input{macros}
+\renewcommand{\theequation}{S\arabic{equation}}
+\begin{document}
+\raggedbottom
+\renewcommand\tablename{Supplementary Table}
+\title{Supplementary Information for: Diffusion model with bulk expression conditioning can generate precise single cell dataset from bulk transcriptome with preserved disease specific gene expression signature}
+\input{authors}
+\maketitle
+\raggedbottom
+
+\section*{Supplementary Notes}
+\renewcommand{\thesection}{Supplementary Note \arabic{section}:}
+\input{sections/supp_notes}
+
+\section*{Supplementary Methods}
+\input{sections/supp_methods_model}
+\input{sections/supp_methods_eval}
+
+\section*{Supplementary Tables}
+\input{sections/supp_table_cohorts}
+\input{sections/supp_tables}
+
+\section*{Supplementary Figures}
+\input{sections/supp_figures}
+
+\clearpage
+\bibliography{references}
+\end{document}