SignacX: Cell Type Identification and Discovery from Single Cell Gene Expression Data

An implementation of neural networks trained with flow-sorted gene expression data to classify cellular phenotypes in single cell RNA-sequencing data. See Chamberlain M et al. (2021) <doi:10.1101/2021.02.01.429207> for more details.

Version: 2.2.4
Depends: R (≥ 3.5.0)
Imports: neuralnet, lme4, methods, Matrix, pbmcapply, Seurat (≥ 3.2.0), RJSONIO, igraph (≥ 1.2.1), jsonlite (≥ 1.5), RColorBrewer (≥ 1.1.2), stats
Suggests: hdf5r, rhdf5, knitr, rmarkdown, formatR
Published: 2021-07-22
Author: Mathew Chamberlain [aut, cre], Virginia Savova [aut], Richa Hanamsagar [aut], Frank Nestle [aut], Emanuele de Rinaldis [aut], Sanofi US [fnd]
Maintainer: Mathew Chamberlain <chamberlainphd at gmail.com>
BugReports: https://github.com/mathewchamberlain/SignacX/issues
License: GPL-3
URL: https://github.com/mathewchamberlain/SignacX
NeedsCompilation: no
Citation: SignacX citation info
Materials: README NEWS
CRAN checks: SignacX results

Downloads:

Reference manual: SignacX.pdf
Vignettes: Mapping homologous gene symbols
Benchmarking SignacX and SingleR with flow-sorted data
Analysis of Kidney lupus data from AMP
Analysis of CITE-seq PBMCs from 10X Genomics
Analysis of PBMCs from 10X Genomics
Mapping cells from CITE-seq PBMCs from 10X Genomics to another data set
Benchmarking SignacFast with flow-sorted data
Package source: SignacX_2.2.4.tar.gz
Windows binaries: r-devel: SignacX_2.2.4.zip, r-release: SignacX_2.2.4.zip, r-oldrel: SignacX_2.2.4.zip
macOS binaries: r-release (arm64): SignacX_2.2.4.tgz, r-release (x86_64): SignacX_2.2.4.tgz, r-oldrel: SignacX_2.2.4.tgz
Old sources: SignacX archive

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