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-source Bioconductor project. It covers basic steps including quality control, data exploration and normalization, as well as more complex procedures such as cell cycle phase assignment, identification of highly variable and correlated genes, clustering into subpopulations and marker gene detection. Analyses were demonstrated on gene-level count data from several publicly available datasets involving haematopoietic stem cells, brain-derived cells, T-helper cells and mouse embryonic stem cells. This will provide a range of usage scenarios from which readers can construct their own analysis pipelines.
Highlights
Single-cell RNA sequencing is widely used to measure the genome-wide expression profile of individual cells
We identify the rows corresponding to External RNA Controls Consortium (ERCC) spike-ins and mitochondrial genes
Classification of cell cycle phase We use the prediction method described by Scialdone et al (2015) to classify cells into cell cycle phases based on the gene expression data
Summary
This article is included in the Bioconductor gateway. This article is included in the EMBL-EBI collection. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This version of the workflow contains a number of improvements based on the referees’ comments. We have re-compiled the workflow using the latest packages from Bioconductor release 3.4, and stated more explicitly the dependence on these package versions. Some minor rewording and elaborations have been performed in various parts of the article
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