Single-Cell Interpretable Tensor Decomposition

Single-cell Interpretable Tensor Decomposition (scITD) employs the Tucker tensor decomposition to extract multicell-type gene expression patterns that vary across donors/individuals. This tool is geared for use with single-cell RNA-sequencing datasets consisting of many source donors. The method has a wide range of potential applications, including the study of inter-individual variation at the population-level, patient sub-grouping/stratification, and the analysis of sample-level batch effects. Each "multicellular process" that is extracted consists of (A) a multi cell type gene loadings matrix and (B) a corresponding donor scores vector indicating the level at which the corresponding loadings matrix is expressed in each donor. Additional methods are implemented to aid in selecting an appropriate number of factors and to evaluate stability of the decomposition. Additional tools are provided for downstream analysis, including integration of gene set enrichment analysis and ligand-receptor analysis. Tucker, L.R. (1966) . Unkel, S., Hannachi, A., Trendafilov, N. T., & Jolliffe, I. T. (2011) . Zhou, G., & Cichocki, A. (2012) .


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install.packages("scITD")

1.0.0 by Jonathan Mitchel, 23 days ago


Browse source code at https://github.com/cran/scITD


Authors: Jonathan Mitchel [cre, aut] , Evan Biederstedt [aut] , Peter Kharchenko [aut]


Documentation:   PDF Manual  


GPL-3 license


Imports rTensor, ica, fgsea, circlize, reshape2, parallel, ComplexHeatmap, ggplot2, mgcv, utils, Rcpp, RColorBrewer, dplyr, edgeR, sva, stats, Rmisc, ggpubr, msigdbr, sccore, NMF

Depends on Matrix

Suggests methods, knitr, rmarkdown, testthat, coda.base, grid, ssvd, simplifyEnrichment, WGCNA, cowplot, matrixStats, stringr, zoo, rlang, AnnotationDbi, GO.db, conos, pagoda2, betareg

Linking to Rcpp, RcppArmadillo, RcppProgress


See at CRAN