Workflow Package: simpleSingleCell

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

Bioconductor version: Release (3.8)

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 <infinite.monkeys.with.keyboards at>

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


To install this package, start R (version "3.5") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
BiocManager::install("simpleSingleCell", version = "3.8")


To view available workflows for the version of this package installed in your system, start R and enter:



HTML R Script 01. Introduction
HTML R Script 02. Read count data
HTML R Script 03. UMI count data
HTML R Script 04. Droplet-based data
HTML R Script 05. Correcting batch effects
HTML R Script 06. Quality control details
HTML R Script 07. Spike-in normalization
HTML R Script 08. Detecting doublets
HTML R Script 09. Advanced variance modelling
HTML R Script 10. Scalability for big data
HTML R Script 11. Further analysis strategies


biocViews SingleCellWorkflow, Workflow
Version 1.4.0
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, BiocFileCache, BiocNeighbors, TENxBrainData
Suggests knitr, rmarkdown
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