Archive 4 checkpoints (1-4)

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@@ -126,3 +126,4 @@ paper-releases/pbisc-diffusion/updates/20261012T090327Z-uniform-manuscript-margi
 paper-releases/pbisc-diffusion/updates/20261012T091446Z-frontmatter-word-export/diffusion/dataset/server_1/manuscript/archive/word_exports/20261012T091054Z/preview/PBISC-Diffusion_Title_Abstract_Introduction_20261012.pdf filter=lfs diff=lfs merge=lfs -text
 paper-releases/pbisc-diffusion/updates/20261012T104812Z-jh-introduction-edit/diffusion/dataset/server_1/manuscript/main.pdf filter=lfs diff=lfs merge=lfs -text
 paper-releases/pbisc-diffusion/updates/20261012T121201Z-abstract-pretraining-lora/diffusion/dataset/server_1/manuscript/main.pdf filter=lfs diff=lfs merge=lfs -text
+paper-releases/pbisc-diffusion/updates/20261012T122929Z-introduction-transitions-lora/diffusion/dataset/server_1/manuscript/main.pdf filter=lfs diff=lfs merge=lfs -text

paper-releases/pbisc-diffusion/updates/20261012T122929Z-introduction-transitions-lora/diffusion/dataset/server_1/manuscript/editorial/introduction_transitions_lora_20261012.json

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+{
+  "date": "2026-10-12",
+  "source": "User-approved four sentence replacements in Introduction paragraphs 2,3,6.",
+  "changes": [
+    {
+      "before": "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.",
+      "after": "Also these methods' outputs are limited to cell-type proportions and expression averages, thus cannot represent heterogeneity within a cell type."
+    },
+    {
+      "before": "Methods that organize generation around cell types or clusters also depend on how these populations are defined\\cite{liao2022bulk2space,zeng2024omicverse}, motivating approaches that do not require cell identities or proportions to be specified in advance.",
+      "after": "Moreover, methods that organize single cell generation around pre-defined cell-types or clusters also depend on how these populations are defined\\cite{liao2022bulk2space,zeng2024omicverse}, which motivate approaches that do not require cell-type identities or proportions to be specified in advance."
+    },
+    {
+      "before": "Here we introduce PBMC Bulk Sample to Immune Single Cells (PBISC), a bulk-conditioned generative model pretrained on pseudobulk--single-cell examples constructed from over 33 million cells across 22 scRNA-seq datasets.",
+      "after": "Here we introduce PBISC, a bulk-conditioned diffusion model that combines pretraining on over 33 million single cells across 22 scRNA-seq datasets with low-rank adaptation (LoRA)\\cite{hu2022lora} using paired bulk and single-cell data from just five donors."
+    },
+    {
+      "before": "This pretraining establishes a shared model of cellular expression across cohorts, reducing reliance on selecting a separate single-cell reference for each target.",
+      "after": "The pretraining step establishes a shared model of cellular expression across cohorts, reducing reliance on selecting a separate single-cell reference for each target."
+    }
+  ],
+  "citation_note": "Preserved Bulk2Space/BulkTrajBlend citations and reused existing hu2022lora at the newly explicit LoRA mention. No new bibliography entry.",
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+  "scope": "Four requested sentence replacements only; all other prose unchanged.",
+  "verification": {
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+    "build_exit_code": 0,
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+    "pages": 49,
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+  }
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+% Introduction updated from JH Word edits on 2026-10-12; tracked insertions accepted and deletions omitted.
+% Minimal grammar and mechanism wording corrections plus citations are recorded in editorial/jh_edit_integration_20261012.json.
+\section*{Introduction}
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+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.
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+Computational deconvolution has been the principal bridge between the two. Digital cytometry methods such as CIBERSORTx\cite{newman2019cibersortx}, MuSiC\cite{wang2019music}, Bisque\cite{jew2020bisque}, DWLS\cite{tsoucas2019dwls}, Scaden\cite{menden2020scaden} and TAPE\cite{chen2022tape} estimate cell-type proportions from bulk using single-cell-derived signatures, and BayesPrism\cite{chu2022bayesprism}, BLADE\cite{barbosa2021blade} and bMIND\cite{wang2021bmind} 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, single cell profile reference choice and annotation granularity, degrades for closely related and rare cell types, and changes rank when the reference differs from the bulk cohort\cite{avilacobos2020benchmarking,sturm2019evaluation,dietrich2026omnideconv,community2024assessment}. Also these methods' outputs are limited to cell-type proportions and expression averages, thus cannot represent heterogeneity within a cell type.
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+Advances in machine learning have enabled generative approaches to infer single-cell expression profiles from bulk transcriptomes. Bulk2Space\cite{liao2022bulk2space} and BulkTrajBlend\cite{zeng2024omicverse} use estimated cell-type composition to guide generation from single-cell reference data, whereas scSemiProfiler\cite{wang2024scsemiprofiler} adapts profiles from selected cohort representatives using bulk-derived constraints on aggregate expression. However, these approaches remain dependent on selected references, whose coverage and representativeness can influence the inferred cellular landscape\cite{liao2022bulk2space,zeng2024omicverse,wang2024scsemiprofiler}. Moreover, methods that organize single cell generation around pre-defined cell-types or clusters also depend on how these populations are defined\cite{liao2022bulk2space,zeng2024omicverse}, which motivate approaches that do not require cell-type identities or proportions to be specified in advance.
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+Diffusion-based methods, including scDiffusion\cite{luo2024scdiffusion}, have expanded the possibilities for generating cellular profiles. However, realistic single-cell generation still requires faithful reproduction of the sparse patterns of zero and nonzero expression observed in experimental data. Meanwhile, Sparse Data Diffusion introduced ``sparsity bits'' to represent these patterns explicitly alongside expression magnitudes, improving the fidelity of sparse data generation in biological and physical datasets\cite{ostheimer2025sparse}. This approach provides a basis for jointly modeling which genes are detected and their expression levels when reconstructing cellular heterogeneity.
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+A further obstacle for such bulk transcriptome deconvolution is the scarcity of experimentally paired bulk and single-cell measurements. Large single-cell datasets can provide abundant bulk-to-single-cell training sets by aggregating cells into pseudobulk profiles, but these simulated profiles do not fully reproduce the expression distribution of measured bulk RNA-seq due to technical and sample-preparation differences, such as differences in sequencing depth\cite{wang2024scsemiprofiler} and cell loss during dissociation of solid tissues\cite{denisenko2020systematic}. Systematic differences between pseudobulk and measured bulk can therefore limit the transfer of models to experimental data\cite{delafuente2025sweetwater}. Learning broadly from available single-cell resources and subsequently adapting to real bulk measurements with a small amount of paired data offers a practical strategy for addressing this limitation.
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+Here we introduce PBISC, a bulk-conditioned diffusion model that combines pretraining on over 33 million single cells across 22 scRNA-seq datasets with low-rank adaptation (LoRA)\cite{hu2022lora} using paired bulk and single-cell data from just five donors. The pretraining step establishes a shared model of cellular expression across cohorts, reducing reliance on selecting a separate single-cell reference for each target. We incorporated the sparsity-bit concept from Sparse Data Diffusion\cite{ostheimer2025sparse} to jointly model gene-detection patterns and expression levels. With PBISC, we were able to generate cells from independent random initializations under a shared bulk condition, without requiring cell-type annotations or predicted cell-type proportions during training or generation.
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+To adapt PBISC to measured bulk RNA-seq, we fine-tuned the pretrained model using experimentally paired bulk and single-cell profiles, with as few as five paired samples in the evaluated settings. During adaptation, measured bulk profiles served as conditioning inputs, while observed single-cell profiles from the corresponding biological samples provided the training targets. We then applied the adapted model to generate single-cell profiles for the remaining cohort using bulk measurements alone.
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+In various evaluation standards, we were able to reproduce the distributions of paired experimental single-cell profiles, as assessed by precision\cite{kynkaanniemi2019improved} and coverage\cite{naeem2020reliable}. PBISC-generated deconvoluted dataset preserved disease-associated expression changes more accurately than comparator methods in most evaluated settings. Furthermore, PBISC reproduced literature-defined rare cell populations\cite{reyes2020immune,nehar2020mapping} observed in the single-cell data of the evaluated cohorts, without specifying these populations as separate generation categories. Together, these results support extending analyses of cellular heterogeneity and disease-associated transcriptional programs to large cohorts primarily profiled by bulk RNA-seq.