Abstract:
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With the wide-adoption of single-cell RNA-seq (scRNA-seq) technologies, designs of scRNA-seq experiments are becoming more complicated with multiple treatments or biological conditions. However, despite the active research on batch effects correction, cell type clustering, and missing data imputation for scRNA-seq data, rigorous statistical methods to compare scRNA-seq experiments under different conditions are still lacking. Here, we propose a Bayesian hierarchical model to rigorously quantify the treatment effects on both cellular compositions and cell-type-specific gene expression levels for scRNA-seq data. We implement a highly scalable algorithm to handle the large number of cells. Application of our proposed method to a pancreatic study demonstrates that considering the biological conditions of samples in the analysis further boosts the clustering accuracy as compared to traditional analysis pipelines for scRNA-seq data and identifies cell-type-specific and condition-specific differentially expressed genes.
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