Abstract
Single-cell RNA sequencing (scRNA-seq) analysis has significantly advanced our knowledge of functional states of cells. By analyzing scRNA-seq data, we can deconvolve individual cell states into thousands of gene expression profiles, allowing us to perform cell clustering, and identify significant genes for each cluster. However, interpreting these results remains challenging. Here, we present a novel scRNA-seq analysis pipeline named ASURAT, which simultaneously performs unsupervised cell clustering and biological interpretation in semi-automatic manner, in terms of cell type and various biological functions. We validate the reliable clustering performance of ASURAT by comparing it with existing methods, using six published scRNA-seq datasets from healthy donors and cancer patients. Furthermore, we applied ASURAT to patient-derived scRNA-seq datasets including small cell lung cancers, finding some putative cancer subpopulations showing different resistance mechanisms. ASURAT is expected to open new means of scRNA-seq analysis, focusing more on “biological meaning” than conventional gene-based analyses.
Competing Interest Statement
The authors have declared no competing interest.
