recipe bioconductor-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.

package bioconductor-coexnet

(downloads) docker_bioconductor-coexnet


1.9.0-0, 1.8.0-0, 1.6.0-1, 1.4.0-0

Required By


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install bioconductor-coexnet

and update with:

conda update bioconductor-coexnet

or use the docker container:

docker pull<tag>

(see bioconductor-coexnet/tags for valid values for <tag>)