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.
After downloading and unzipping
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 analysis
See vignette for details and examples:
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. Nat Methods. 2018 May;15(5):379-386. doi: 10.1038/nmeth.4662.