gibbslab/coexnet: coexnet: An R package to build CO-EXpression NETworks from Microarray Data

Extracts the gene expression matrix from GEO DataSets (.CEL files) as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analisys using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach.

Getting started

Package details

AuthorJuan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres Pinzon-Velasco [aut]
Bioconductor views DifferentialExpression GeneExpression GraphAndNetwork Microarray Network NetworkInference Normalization SystemsBiology
MaintainerJuan David Henao <>
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
gibbslab/coexnet documentation built on May 17, 2019, 4:19 a.m.