SignacX: Cell Type Identification and Discovery from Single Cell Gene Expression Data (original) (raw)
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:
2.2.5
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:
2021-11-18
DOI:
Author:
Mathew Chamberlain [aut, cre], Virginia Savova [aut], Richa Hanamsagar [aut], Frank Nestle [aut], Emanuele de Rinaldis [aut], Sanofi US [fnd]
Maintainer:
Mathew Chamberlain
BugReports:
https://github.com/mathewchamberlain/SignacX/issues
License:
URL:
https://github.com/mathewchamberlain/SignacX
NeedsCompilation:
no
Citation:
Materials:
In views:
CRAN checks: