A toolkit that allows scientists to work with data from single cell sequencing technologies such as scRNA-seq, scVDJ-seq, scATAC-seq, CITE-Seq and Spatial Transcriptomics (ST). Single (i) Cell R package ('iCellR') provides unprecedented flexibility at every step of the analysis pipeline, including normalization, clustering, dimensionality reduction, imputation, visualization, and so on. Users can design both unsupervised and supervised models to best suit their research. In addition, the toolkit provides 2D and 3D interactive visualizations, differential expression analysis, filters based on cells, genes and clusters, data merging, normalizing for dropouts, data imputation methods, correcting for batch differences, pathway analysis, tools to find marker genes for clusters and conditions, predict cell types and pseudotime analysis. See Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.05.05.078550> and Khodadadi-Jamayran, et al (2020) <doi:10.1101/2020.03.31.019109> for more details.
Version: 1.6.7 Depends: R (≥ 3.3.0), ggplot2, plotly Imports: Matrix, Rtsne, gridExtra, ggrepel, ggpubr, scatterplot3d, RColorBrewer, knitr, NbClust, shiny, pheatmap, ape, ggdendro, plyr, reshape, Hmisc, htmlwidgets, methods, uwot, hdf5r, progress, igraph, data.table, Rcpp, RANN, jsonlite, png LinkingTo: Rcpp Published: 2024-01-29 DOI: 10.32614/CRAN.package.iCellR Author: Alireza Khodadadi-Jamayran [aut, cre], Joseph Pucella [aut, ctb], Hua Zhou [aut, ctb], Nicole Doudican [aut, ctb], John Carucci [aut, ctb], Adriana Heguy [aut, ctb], Boris Reizis [aut, ctb], Aristotelis Tsirigos [aut, ctb] Maintainer: Alireza Khodadadi-Jamayran <alireza.khodadadi.j at gmail.com> BugReports: https://github.com/rezakj/iCellR/issues License: GPL-2 URL: https://github.com/rezakj/iCellR NeedsCompilation: yes Citation: iCellR citation info In views: MissingData, Omics CRAN checks: iCellR results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=iCellR to link to this page.
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