SIBERG: Systematic Identification of Bimodally Expressed Genes Using RNAseq Data

Provides models to identify bimodally expressed genes from RNAseq data based on the Bimodality Index. SIBERG models the RNAseq data in the finite mixture modeling framework and incorporates mechanisms for dealing with RNAseq normalization. Three types of mixture models are implemented, namely, the mixture of log normal, negative binomial, or generalized Poisson distribution. See Tong et al. (2013) <doi:10.1093/bioinformatics/bts713>.

Version:	2.0.3
Depends:	R (≥ 2.10)
Imports:	mclust
Suggests:	edgeR, doParallel
Published:	2022-05-03
Author:	Pan Tong, Kevin R. Coombes
Maintainer:	Kevin R. Coombes <krc at silicovore.com>
License:	Apache License (== 2.0)
URL:	http://oompa.r-forge.r-project.org/
NeedsCompilation:	no
Materials:	NEWS
CRAN checks:	SIBERG results

Documentation:

Reference manual:	SIBERG.pdf
Vignettes:	SIBER Vignette

Downloads:

Package source:	SIBERG_2.0.3.tar.gz
Windows binaries:	r-devel: SIBERG_2.0.3.zip, r-release: SIBERG_2.0.3.zip, r-oldrel: SIBERG_2.0.3.zip
macOS binaries:	r-release (arm64): SIBERG_2.0.3.tgz, r-oldrel (arm64): SIBERG_2.0.3.tgz, r-release (x86_64): SIBERG_2.0.3.tgz, r-oldrel (x86_64): SIBERG_2.0.3.tgz
Old sources:	SIBERG archive

Linking:

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