Bioconductor packages

EDASeq

Numerical and graphical summaries of RNA-Seq read data. Within-sample normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization. Between-sample normalization procedures to adjust for distributional differences between samples (e.g., sequencing depth): global-scaling and full-quantile normalization.

RUVSeq

This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.

clusterExperiment

This package provides functions for running and comparing many different clusterings of single-cell sequencing data.

scone

SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses.

zinbwave

This package implements a zero-inflated negative binomial model for single-cell RNA-seq data, with latent factors.

slingshot

Tools for ordering single-cell sequencing and for inferring continuous, branching lineage structures in single-cell data.

SingleCellExperiment

S4 class for storing data from single-cell experiments.

DEScan2

Integrated peak and differential caller, specifically designed for broad epigenomic signals.

mbkmeans

K-means clustering for large single-cell datasets.

Data packages

yeastRNASeqRisso2011

Gene-level counts of RNA-Seq data from Risso et al. (2011).

zebrafishRNASeq

Gene-level counts of RNA-Seq data from Risso et al. (2011).

scRNAseq

Gene-level read counts of three public single-cell RNA-seq datasets.

TENxPBMCData

Single-cell RNA-seq data for on PBMC cells, generated by 10X Genomics.

fletcher2017data

Gene-level counts of single-cell RNA-Seq data from Fletcher et al. (2017).

Tutorials

RUVSeq tutorial

Tutorial to reproduce the analyses of Peixoto et al. (2015).

Single-cell RNA-seq tutorial

Normalization, clustering, and lineage analysis of single-cell RNA-seq data using the scone, clusterExperiment, and slingshot packages.