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 pathways 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 pathways can be used to cluster cells and detect cell identities, and the condition-specific gene pathways can help compare functional differences in transcriptomes from distinct conditions.

Version: 0.1.1
Depends: methods
Imports: Rcpp, RANN, igraph, parallel, stats, stringr
LinkingTo: Rcpp, RcppArmadillo
Published: 2021-09-06
Author: Kun Qian ORCID iD [aut, ctb, cre], Wei Vivian Li ORCID iD [aut, ctb]
Maintainer: Kun Qian <Kun_Qian at foxmail.com>
License: GPL-3
NeedsCompilation: yes
CRAN checks: scINSIGHT results

Downloads:

Reference manual: scINSIGHT.pdf
Package source: scINSIGHT_0.1.1.tar.gz
Windows binaries: r-devel: scINSIGHT_0.1.1.zip, r-devel-UCRT: scINSIGHT_0.1.1.zip, r-release: scINSIGHT_0.1.1.zip, r-oldrel: scINSIGHT_0.1.1.zip
macOS binaries: r-release (arm64): scINSIGHT_0.1.1.tgz, r-release (x86_64): scINSIGHT_0.1.1.tgz, r-oldrel: scINSIGHT_0.1.1.tgz
Old sources: scINSIGHT archive

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