scISR: Single-Cell Imputation using Subspace Regression

Provides an imputation pipeline for single-cell RNA sequencing data. The 'scISR' method uses a hypothesis-testing technique to identify zero-valued entries that are most likely affected by dropout events and estimates the dropout values using a subspace regression model (Tran (2022) <doi:10.1038/s41598-022-06500-4>).

Version: 0.1.1
Depends: R (≥ 3.4)
Imports: cluster, entropy, stats, utils, parallel, irlba, PINSPlus, matrixStats, markdown
Suggests: testthat, knitr, mclust
Published: 2022-06-30
DOI: 10.32614/CRAN.package.scISR
Author: Duc Tran [aut, cre], Bang Tran [aut], Hung Nguyen [aut], Tin Nguyen [fnd]
Maintainer: Duc Tran <duct at>
License: LGPL-2 | LGPL-2.1 | LGPL-3 [expanded from: LGPL]
NeedsCompilation: no
Citation: scISR citation info
Materials: README
CRAN checks: scISR results


Reference manual: scISR.pdf
Vignettes: scISR package manual


Package source: scISR_0.1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): scISR_0.1.1.tgz, r-oldrel (arm64): scISR_0.1.1.tgz, r-release (x86_64): scISR_0.1.1.tgz, r-oldrel (x86_64): scISR_0.1.1.tgz


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