ArticleCell reports methods2024
Single-cell biclustering for cell-specific transcriptomic perturbation detection in AD progression.
Article in Cell reports methods, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 4 citations in OpenAlex.
- Single-cell disentangled representations for perturbation modeling and treatment effect estimation.bioRxiv : the preprint server for biology · 2026Article
- Outcome-guided spike-and-slab Lasso Biclustering: A Novel Approach for Enhancing Biclustering Techniques for Gene Expression Analysis.Statistics and computing · 2025Article
- Heterogeneity Analysis of Associations Involving the Large-Scale Online MindCrowd Survey Memory Test.Gerontology · 2025Article
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Authors and funding
7 authors at 1 institution in 1 country.
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Abstract
The pathogenesis of Alzheimer disease (AD) involves complex gene regulatory changes across different cell types. To help decipher this complexity, we introduce single-cell Bayesian biclustering (scBC), a framework for identifying cell-specific gene network biomarkers in scRNA and snRNA-seq data. Through biclustering, scBC enables the analysis of perturbations in functional gene modules at the single-cell level. Applying the scBC framework to AD snRNA-seq data reveals the perturbations within gene modules across distinct cell groups and sheds light on gene-cell correlations during AD progression. Notably, our method helps to overcome common challenges in single-cell data analysis, including batch effects and dropout events. Incorporating prior knowledge further enables the framework to yield more biologically interpretable results. Comparative analyses on simulated and real-world datasets demonstrate the precision and robustness of our approach compared to other state-of-the-art biclustering methods. scBC holds potential for unraveling the mechanisms underlying polygenic diseases characterized by intricate gene coexpression patterns.
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