ArticleNature genetics2025
Dissecting tumor transcriptional heterogeneity from single-cell RNA-seq data by generalized binary covariance decomposition.
Article in Nature genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- Clonal lineage tracing and parallel multiomics profiling reveal transcriptional heterogeneity induced by ARID1A deficiency.Science advances · 2026Article
- A Gene-Program Architecture of Mouse T cells.bioRxiv : the preprint server for biology · 2026Article
- Prognostic RNA-splicing archetypes in breast cancer identified by extended pre-training of histopathology foundation models.Nature communications · 2026Article
- Empirical Bayes Covariance Decomposition, and a Solution to the Multiple Tuning Problem in Sparse PCA.Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America · 2026Article
- Article
- Connecting polygenic disease risk to cell states and regulatory programs through single-cell chromatin accessibility.bioRxiv : the preprint server for biology · 2026Article
- The CD8 immgenT framework as a universal reference of mouse CD8αβ T cell differentiation states.bioRxiv : the preprint server for biology · 2026Article
- Remodeling the tumor dormancy ecosystem to prevent recurrence and metastasis.Signal transduction and targeted therapy · 2026Review
- Integrative single-cell and bulk transcriptomics define polyamine-associated cell states in acute myeloid leukemia and implicate CCT6A in polyamine homeostasis.Frontiers in immunology · 2026Article
- Article
- Genetic contributions to epigenetic-defined endotypes of allergic phenotypes in children.American journal of human genetics · 2025Article
- Variation in bulk RNA-seq and estimated cell type proportion using deconvolution when comparing pancreatic cancer samples within the same individual.medRxiv : the preprint server for health sciences · 2025Article
- Single-cell sequencing reveals the mechanisms of multiple myeloma progression: clarity or confusion?Annals of hematology · 2025Review
- Covariate-moderated Empirical Bayes Matrix Factorization.Advances in neural information processing systems · 2025Article
- Bulk RNA-seq deconvolution heterogeneity across paired pancreatic cancer human samples.Frontiers in genetics · 2025Article
- ebnm: An R Package for Solving the Empirical Bayes Normal Means Problem Using a Variety of Prior Families.Journal of statistical software · 2025Article
- The Integrated Stress Response in Pancreatic Development, Tissue Homeostasis, and Cancer.Gastroenterology · 2024Review
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Abstract
Profiling tumors with single-cell RNA sequencing has the potential to identify recurrent patterns of transcription variation related to cancer progression, and to produce therapeutically relevant insights. However, strong intertumor heterogeneity can obscure more subtle patterns that are shared across tumors. Here we introduce a statistical method, generalized binary covariance decomposition (GBCD), to address this problem. We show that GBCD can decompose transcriptional heterogeneity into interpretable components-including patient-specific, dataset-specific and shared components relevant to disease subtypes-and that, in the presence of strong intertumor heterogeneity, it can produce more interpretable results than existing methods. Applied to data on pancreatic ductal adenocarcinoma, GBCD produced a refined characterization of existing tumor subtypes, and identified a gene expression program prognostic of poor survival independent of tumor stage and subtype. The gene expression program is enriched for genes involved in stress responses, and suggests a role for the integrated stress response in pancreatic ductal adenocarcinoma.
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