Pigengene: Infers biological signatures from gene expression data
Version 1.2.0

Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes.

Package details

AuthorHabil Zare, Amir Foroushani, and Rupesh Agrahari
Bioconductor views BiomedicalInformatics Classification Clustering DecisionTree DimensionReduction GeneExpression GraphAndNetwork Microarray Network NetworkInference Normalization PrincipalComponent RNASeq SystemsBiology Transcriptomics
MaintainerHabil Zare <zare@txstate.edu>
LicenseGPL (>=2)
Version1.2.0
Package repositoryView on Bioconductor
Installation Install the latest version of this package by entering the following in R:
source("https://bioconductor.org/biocLite.R")
biocLite("Pigengene")

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Pigengene documentation built on May 31, 2017, 12:29 p.m.