CellID take as input a matrix of expression level generated by SCRNA-Seq and performs dimensionality reduction using Multiple Corespondance Analysis. This enables a representation of both genes and cell in the same euclidean space with genes close to a particular cell or group of cells being specificly expressed in those. This property allows to get a ranking of specificity of genes for each cells giving the opportunity to extract meaningful geneset or perform geneset enrichment analysis without any need of clustering.
CellID contains dependencies with Biocondutor packages. Please use first
setRepositories() and type 1 2, to enable the download of bioconducor package.
install.packages("devtools") library(devtools) install_github("cbl-imagine/CellID")
Alternatively you can download all bioconductor dependencies beforehand.
if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager") BiocManager::install("scater", version = "3.8") BiocManager::install("fgsea", version = "3.8")
python umap-learn package can be handy for fast umap computation but is not mandatory.
pip install umap-learn
CellID is used with single cell specific S4objects. Curreltly supported are SingleCellExperiment from Bioconductor and Seurat Version 3 from CRAN.
You can find a vignette for the basic functionalities and usage of the package here
This project is licensed under the GNU General Public License - see the LICENSE file for details
Rausell, A., Juan, D., Pazos, F., & Valencia, A. (2010). Protein interactions and ligand binding: from protein subfamilies to functional specificity. Proceedings of the National Academy of Sciences of the United States of America, 107(5), 1995–2000. url
Aan, Z., & Greenacre, M. (2011). Biplots of fuzzy coded data. Fuzzy Sets and Systems, 183(1), 57–71. url
Alexey Sergushichev. An algorithm for fast preranked gene set enrichment analysis using cumulative statistic calculation. bioRxiv (2016), url
Stuart and Butler et al. Comprehensive integration of single cell data. bioRxiv (2018). url
Aaron Lun and Davide Risso (2019). SingleCellExperiment: S4 Classes for Single Cell Data. R package version 1.4.1. url
A big thanks to the fast growing SingleCell community that keeps giving and delivering amazing data and software.
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