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.
This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples.
This package provides functions for running and comparing many different clusterings of single-cell sequencing data.
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.
This package implements a zero-inflated negative binomial model for single-cell RNA-seq data, with latent factors.
Tools for ordering single-cell sequencing and for inferring continuous, branching lineage structures in single-cell data.
S4 class for storing data from single-cell experiments.
Integrated peak and differential caller, specifically designed for broad epigenomic signals.
K-means clustering for large single-cell datasets.
Gene-level counts of RNA-Seq data from Risso et al. (2011).
Gene-level counts of RNA-Seq data from Risso et al. (2011).
Gene-level read counts of three public single-cell RNA-seq datasets.
Single-cell RNA-seq data for on PBMC cells, generated by 10X Genomics.
Gene-level counts of single-cell RNA-Seq data from Fletcher et al. (2017).
Tutorial to reproduce the analyses of Peixoto et al. (2015).
Normalization, clustering, and lineage analysis of single-cell RNA-seq data using the scone, clusterExperiment, and slingshot packages.