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:
2.2.5
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
Published:
2021年11月18日
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>
NeedsCompilation:
no
Documentation:
Downloads:
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