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Algorithm for the inference of cell types and lineage trees from single-cell RNA-seq data. This is a novel R package of the RaceID3 and StemID2 method including novel functionalities and performance improvements compared to the previous RaceID3/StemID2 version in the RaceID3_StemID2 repository. The RaceID3_StemID2 repository will not be updated …
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README.md

RaceID 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.

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 analysis

Load package:

library(RaceID)

See vignette for details and examples:

vignette("RaceID")

Reference:

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.

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