recipe bioconductor-pigengene

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.



GPL (>=2)



package bioconductor-pigengene

(downloads) docker_bioconductor-pigengene



Depends bioconductor-go.db


Depends bioconductor-graph


Depends bioconductor-impute


Depends bioconductor-preprocesscore


Depends bioconductor-rgraphviz


Depends r-base


Depends r-bnlearn

Depends r-c50


Depends r-mass

Depends r-matrixstats

Depends r-partykit

Depends r-pheatmap


Depends r-wgcna



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

conda install bioconductor-pigengene

and update with:

conda update bioconductor-pigengene

or use the docker container:

docker pull<tag>

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