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

TLDR

scRNA‑seq provides single‑cell resolution but introduces higher technical noise and data complexity than bulk RNA‑seq, necessitating distinct analytical approaches. The article presents a dedicated computational workflow to perform low‑level scRNA‑seq analysis that addresses these challenges. Built with Bioconductor packages, the workflow covers quality control, data exploration, normalization, cell‑cycle assignment, variable‑gene detection, clustering, and marker‑gene identification. Demonstrations on public datasets of hematopoietic, brain, T‑helper, and embryonic stem cells show the workflow’s applicability and offer templates for users to construct their own pipelines.

Abstract

<ns4:p>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.</ns4:p>