A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.

Abstract

Single-cell RNA sequencing (scRNA-seq) is widely used to profile the transcriptome of individual cells. This provides biological resolution that cannot be matched by bulk RNA sequencing, at the cost of increased technical noise and data complexity. The differences between scRNA-seq and bulk RNA-seq data mean that the analysis of the former cannot be performed by recycling bioinformatics pipelines for the latter. Rather, dedicated single-cell methods are required at various steps to exploit the cellular resolution while accounting for technical noise. This article describes a computational workflow for low-level analyses of scRNA-seq data, based primarily on software packages from the open-so..