Workflow Package: simpleSingleCell

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

Bioconductor version: 3.7

This workflow implements a low-level analysis pipeline for scRNA-seq data using scran, scater and other Bioconductor packages. It describes how to perform quality control on the libraries, normalization of cell-specific biases, basic data exploration and cell cycle phase identification. Procedures to detect highly variable genes, significantly correlated genes and subpopulation-specific marker genes are also shown. These analyses are demonstrated on a range of publicly available scRNA-seq data sets.

Author: Aaron Lun [aut, cre], Davis McCarthy [aut], John Marioni [aut]

Maintainer: Aaron Lun <alun at>

Citation (from within R, enter citation("simpleSingleCell")):


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HTML R Script Analyzing droplet-based scRNA-seq data
HTML R Script Analyzing scRNA-seq read count data
HTML R Script Analyzing scRNA-seq UMI count data
HTML R Script Correcting batch effects in scRNA-seq data
HTML R Script Further strategies for analyzing scRNA-seq data
HTML R Script Workflows for analyzing single-cell RNA-seq data with R/Bioconductor


biocViews SingleCellWorkflow, Workflow
Version 1.2.1
License Artistic-2.0
Depends R (>= 3.3.0), BiocStyle, knitr, BiocParallel, Rtsne, mvoutlier, destiny, readxl, gdata, SingleCellExperiment, scater,, scran, limma, pheatmap, dynamicTreeCut, cluster, edgeR, TxDb.Mmusculus.UCSC.mm10.ensGene, scRNAseq, DropletUtils
Suggests knitr, rmarkdown
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