This package is for version 3.8 of Bioconductor; for the stable, up-to-date release version, see simpleSingleCell.

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

Bioconductor version: 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>

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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 ImmunoOncologyWorkflow, SingleCellWorkflow, Workflow
Version 1.4.1
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, BiocFileCache, BiocNeighbors, TENxBrainData
Suggests knitr, rmarkdown
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