RaceID/StemID/VarID algorithm

RaceID is a clustering algorithm for the identification of cell types from single-cell RNA-sequencing data. It was specifically designed for the detection of rare cells which correspond to outliers in conventional clustering methods. The package contains RaceID3, the most recently published version of this algorithm, and StemID2, an algorithm for the identification of lineage trees based on RaceID3 analysis. RaceID3 utilizes single cell expression data, and was designed to work well with quantitative single-cell RNA-seq data incorporating unique molecular identifiers. It requires a gene-by-cell expression matrix as input and produces a clustering partition representing cell types. StemID2 assembles these cell types into a lineage tree. The RaceID package (>= v0.1.4) also contains functions for a VarID analysis. VarID comprises a sensitive clustering method utilizing pruned k-nearest neighbor networks, connecting only cells with links supported by a background model of gene expression. These pruned k-nearest neighbor networks further enable the definition of homogenous neighborhoods for the quantification of local gene expression variability in cell state space.

Installing

After downloading and unzipping

unzip RaceID3_StemID2_package-master.zip 

it can be installed from the command line by

R CMD INSTALL RaceID3_StemID2_package-master

or directly in R from source by

install.packages("RaceID3_StemID2_package-master",repos = NULL, type="source")

(if R is started from the directory where RaceID3_StemID2_package-master.zip has been downloaded to; otherwise specify the full path)

Alternatively, install in R directly from github using devtools:

install.packages("devtools")
library(devtools)
install_github("dgrun/RaceID3_StemID2_package")

Running a RaceID/StemID/VarID analysis

Load package:

library(RaceID)

See vignette for details and examples:

vignette("RaceID")

Reference:

Rosales-Alvarez RE, Rettkowski J, Herman JS, Dumbovic G, Cabezas-Wallscheid N, Grün D (2022) VarID2 quantifies gene expression noise dynamics and unveils functional heterogeneity of ageing hematopoietic stem cells. Genome Biology 24(1):148. doi: 10.1186/s13059-023-02974-1.

Grün D (2020) Revealing Dynamics of Gene Expression Variability in Cell State Space. Nature Methods 17(1):45-49. doi: 10.1038/s41592-019-0632-3

Herman JS, Sagar, Grün D. (2018) FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nature Methods. 2018 May;15(5):379-386. doi: 10.1038/nmeth.4662.