SIngle cell level Genotyping Using scRna Data (SIGURD)

MOTIVATION: By accounting for variants within measured transcripts, it is possible to evaluate the status of somatic variants using single-cell RNA-sequencing (scRNA-seq) and to characterize their clonality. However, the sparsity (very few reads per transcript) or bias in protocols (favoring 3' ends of the transcripts) makes the chance of capturing somatic variants very unlikely. This can be overcome by targeted sequencing or the use of mitochondrial variants as natural barcodes for clone identification. Currently, available computational tools focus on genotyping, but do not provide functionality for combined analysis of somatic and mitochondrial variants and functional analysis such as characterization of gene expression changes in detected clones. RESULTS: Here, we propose SIGURD (SIngle cell level Genotyping Using scRna Data) (SIGURD), which is an R-based pipeline for the clonal analysis of scRNA-seq data. This allows the quantification of clones by leveraging both somatic and mitochondrial variants. SIGURD also allows for functional analysis after clonal detection: association of clones with cell populations, detection of differentially expressed genes across clones, and association of somatic and mitochondrial variants. Here, we demonstrate the power of SIGURD by analyzing single-cell data of colony-forming cells derived from patients with myeloproliferative neoplasms.