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The recent introduction of single-cell RNA-sequencing (scRNA-seq) has revolutionized biomedical sciences by revealing genome-wide gene expression levels within an individual cell. It for the first time enables the identification of differentially expressed (DE) genes between conditions at the single-cell level, bringing RNA-seq analyses to a new stage. In contrast to bulk RNA-seq data with homogeneous samples, the well-known heterogeneity in scRNA-seq data brings new challenges to DE analysis.
Here I will introduce scClust, a computational framework that discovers previously unknown cell type marker genes and matches cells of the same cell type under more than one conditions. Researchers can utilize scClust to first identify matched cells, and then perform DE analysis within each cell type. Compared to common practices that simply treat cells under the same condition as replicates, our recommended practice based on scClust exploits the extensive heterogeneity in scRNA-seq data and uncovers cell type specific DE genes that may have been missed otherwise.
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