recipe bioconductor-coexnet

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

Homepage:

https://bioconductor.org/packages/3.16/bioc/html/coexnet.html

License:

LGPL

Recipe:

/bioconductor-coexnet/meta.yaml

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

Versions:

1.19.1-0,  1.15.0-0,  1.14.0-0,  1.12.0-1,  1.12.0-0,  1.9.0-0,  1.8.0-0,  1.6.0-1,  1.4.0-0

Depends:

• bioconductor-acde >=1.28.0,<1.29.0

• bioconductor-affy >=1.76.0,<1.77.0

• bioconductor-biobase >=2.58.0,<2.59.0

• bioconductor-geoquery >=2.66.0,<2.67.0

• bioconductor-limma >=3.54.0,<3.55.0

• bioconductor-minet >=3.56.0,<3.57.0

• bioconductor-siggenes >=1.72.0,<1.73.0

• bioconductor-stringdb >=2.10.0,<2.11.0

• bioconductor-summarizedexperiment >=1.28.0,<1.29.0

• bioconductor-vsn >=3.66.0,<3.67.0

• r-base >=4.2,<4.3.0a0

• r-igraph

• r-rmarkdown

Required By:

Installation

With an activated Bioconda channel (see set-up-channels), install with:

conda install bioconductor-coexnet

and update with:

conda update bioconductor-coexnet

or use the docker container:

docker pull quay.io/biocontainers/bioconductor-coexnet:<tag>

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

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