Our most recent research is focused on studying cell-level genetic variations from scRNA-seq data. At cell resolution, genetic variants can be linked to cellular phenotypes, including cell state, cell-specific gene expression, and cell fate. Importantly, genetically distinct cell populations can differ with respect to clinical features, including growth rate and sensitivity to drugs.
In the last year we have developed several tools for assessment of single cell-specific expressed Single Nucleotides Variants (sceSNVs). SCExecute executes a user-provided command on barcode-stratified, extracted on-the-fly individual cell alignments. We use scExecute in conjunction with variant callers to detect sceSNVs. For estimation of allele specific variant expression we apply SCReadCounts, which generates cell-SNV matrices with cell-level expressed variant allele frequency (VAFRNA). These matrices are compatible with the cell-GE matrices generated by tools such as UMItools, Seurat, STARsolo and CellRanger, and can be used in a variety of downstream analyses. We use the SCReadCounts outputs as inputs for our other tools scReQTL, scRsQTL, and scSNPair, which apply regression analysis to correlate variant expression to gene expression, splicing, and other SNV’s expression, respectively. These methods exploit the inherent ability of the scRNA-seq setting to preserve intercellular relationships and identify co-expressed features (i.e SNV-gene, SNV-splicing, SNV-SNV). The expression of SNVs of interest can be quantitatively visualized in two-dimensional projection space using scSNVis