ArticleJournal of the American Statistical Association2024
Estimating cell-type-specific gene co-expression networks from bulk gene expression data with an application to Alzheimer's disease.
Article in Journal of the American Statistical Association, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Cell-type-agnostic differential gene expression uncovers conserved principles of cellular regulation.bioRxiv : the preprint server for biology · 2025Article
- Cell type heterogeneity in gene co-expression networks: implications for toxicological research.Briefings in bioinformatics · 2025Review
- Development and Validation of Early Alert Model for Diabetes Mellitus-Tuberculosis Comorbidity.Microorganisms · 2025Article
- Statistical Inference of Cell-type Proportions Estimated from Bulk Expression Data.Journal of the American Statistical Association · 2024Article
- eQTL studies: from bulk tissues to single cells.Journal of genetics and genomics = Yi chuan xue bao · 2023Review
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.Nature communications · 2023Article
- eQTL Studies: from Bulk Tissues to Single Cells.ArXiv · 2023Article
- Cell-type-specific co-expression inference from single cell RNA-sequencing data.bioRxiv : the preprint server for biology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
Inferring and characterizing gene co-expression networks has led to important insights on the molecular mechanisms of complex diseases. Most co-expression analyses to date have been performed on gene expression data collected from bulk tissues with different cell type compositions across samples. As a result, the co-expression estimates only offer an aggregated view of the underlying gene regulations and can be confounded by heterogeneity in cell type compositions, failing to reveal gene coordination that may be distinct across different cell types. In this paper, we introduce a flexible framework for estimating cell-type-specific gene co-expression networks from bulk sample data, without making specific assumptions on the distributions of gene expression profiles in different cell types. We develop a novel sparse least squares estimator, referred to as CSNet, that is efficient to implement and has good theoretical properties. Using CSNet, we analyzed the bulk gene expression data from a cohort study on Alzheimer's disease and identified previously unknown cell-type-specific co-expressions among Alzheimer's disease risk genes, suggesting cell-type-specific disease mechanisms.
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