Exploiting single-cell RNA sequencing data to link alternative splicing and cancer heterogeneity: A computational approach

Cell heterogeneity studies using single-cell sequencing are gaining great significance in the era of personalized medicine. In particular, characterization of tumor heterogeneity is an emergent issue to improve clinical oncology, since both inter- and intra-tumor level heterogeneity influence the utility and application of molecular classifications through specific biomarkers. Majority of studies have exploited gene expression to discriminate cell types. However, to provide a more nuanced view of the underlying differences, isoform expression and alternative splicing events have to be analyzed in detail.