scINSIGHT: Interpretation of Heterogeneous Single-Cell Gene Expression Data

We develop a novel matrix factorization tool named 'scINSIGHT' to jointly analyze multiple single-cell gene expression samples from biologically heterogeneous sources, such as different disease phases, treatment groups, or developmental stages. Given multiple gene expression samples from different biological conditions, 'scINSIGHT' simultaneously identifies common and condition-specific gene modules and quantify their expression levels in each sample in a lower-dimensional space. With the factorized results, the inferred expression levels and memberships of common gene modules can be used to cluster cells and detect cell identities, and the condition-specific gene modules can help compare functional differences in transcriptomes from distinct conditions. Please also see Qian K, Fu SW, Li HW, Li WV (2022) <doi:10.1186/s13059-022-02649-3>.

Version: 0.1.4
Depends: methods
Imports: Rcpp, RANN, igraph, parallel, stats, stringr
LinkingTo: Rcpp, RcppArmadillo
Published: 2022-05-29
DOI: 10.32614/CRAN.package.scINSIGHT
Author: Kun Qian ORCID iD [aut, ctb, cre], Wei Vivian Li ORCID iD [aut, ctb]
Maintainer: Kun Qian <Kun_Qian at>
License: GPL-3
NeedsCompilation: yes
In views: Omics
CRAN checks: scINSIGHT results


Reference manual: scINSIGHT.pdf


Package source: scINSIGHT_0.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): scINSIGHT_0.1.4.tgz, r-oldrel (arm64): scINSIGHT_0.1.4.tgz, r-release (x86_64): scINSIGHT_0.1.4.tgz, r-oldrel (x86_64): scINSIGHT_0.1.4.tgz
Old sources: scINSIGHT archive


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