Speaker
Description
Single-cell RNA sequencing has given researchers unparalleled insight into biological systems. It enables the identification of distinct cellular subpopulations, the characterization of differences between them, and the assessment of overall tissue heterogeneity. Conventional analysis pipelines first cluster individual cells into similar groups and then test for differentially expressed genes between these groups to identify cell types. Using the same data for clustering and testing, however, poses a selective inference problem and can result in overconfidence in differences that may not truly exist. We introduce StabCell, a novel stability-selection framework which integrates clustering and detection of differentially expressed marker genes. By performing clustering and detection of differentially expressed marker genes in randomly selected subsamples of the full dataset, StabCell provides an assessment of clustering stability. Furthermore, it provides finite sample error control over the expected number of falsely selected marker genes per cell subpopulation. In simulation studies, we show that StabCell outperforms conventional analysis pipelines with fewer false positive findings, especially in cases with low signal-to-noise ratio and low sequencing depth. Applying the method to a cell differentiation dataset from induced pluripotent stem cells (iPSCs) to cardiomyocytes and cardiac fibroblasts reveals that meaningful marker genes are consistently in the set of top-ranked genes. This demonstrates the potential of StabCell to enhance the interpretability and robustness of scRNA-seq analyses, supporting more reliable biological discovery and greater confidence in downstream insights.
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