Establishment of an Integrated Computational Workflow for Single Cell RNA-Seq Dataset

Abstract

Single cell RNA-sequencing (scRNA-Seq) has emerged as a popular transcriptomic profiling approach to address long-standing questions on developmental biology and cancer biology. With the advent of increasing single-cell computational methods, it is not easy to determine which profiler to use. Here, we provide an integrated pipeline for both gene expression and genomic architecture analysis in single cells. Our pipeline reveals the global expression profile of the populations, and also identifies the changes in transcriptome/genome including alternative splicing (AS), single-nucleotide polymorphisms (SNPs), RNA editing and gene fusion.