{% set version = "1.8.0" %}
{% set name = "coexnet" %}
{% set bioc = "3.10" %}

package:
  name: 'bioconductor-{{ name|lower }}'
  version: '{{ version }}'
source:
  url:
    - 'https://bioconductor.org/packages/{{ bioc }}/bioc/src/contrib/{{ name }}_{{ version }}.tar.gz'
    - 'https://bioarchive.galaxyproject.org/{{ name }}_{{ version }}.tar.gz'
    - 'https://depot.galaxyproject.org/software/bioconductor-{{ name }}/bioconductor-{{ name }}_{{ version }}_src_all.tar.gz'
  md5: f7a3d7940240da58417abb30d63af455
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: RUnit, BiocGenerics, knitr
requirements:
  host:
    - 'bioconductor-acde >=1.16.0,<1.17.0'
    - 'bioconductor-affy >=1.64.0,<1.65.0'
    - 'bioconductor-biobase >=2.46.0,<2.47.0'
    - 'bioconductor-geoquery >=2.54.0,<2.55.0'
    - 'bioconductor-limma >=3.42.0,<3.43.0'
    - 'bioconductor-minet >=3.44.0,<3.45.0'
    - 'bioconductor-siggenes >=1.60.0,<1.61.0'
    - 'bioconductor-stringdb >=1.26.0,<1.27.0'
    - 'bioconductor-summarizedexperiment >=1.16.0,<1.17.0'
    - 'bioconductor-vsn >=3.54.0,<3.55.0'
    - r-base
    - r-igraph
    - r-rmarkdown
  run:
    - 'bioconductor-acde >=1.16.0,<1.17.0'
    - 'bioconductor-affy >=1.64.0,<1.65.0'
    - 'bioconductor-biobase >=2.46.0,<2.47.0'
    - 'bioconductor-geoquery >=2.54.0,<2.55.0'
    - 'bioconductor-limma >=3.42.0,<3.43.0'
    - 'bioconductor-minet >=3.44.0,<3.45.0'
    - 'bioconductor-siggenes >=1.60.0,<1.61.0'
    - 'bioconductor-stringdb >=1.26.0,<1.27.0'
    - 'bioconductor-summarizedexperiment >=1.16.0,<1.17.0'
    - 'bioconductor-vsn >=3.54.0,<3.55.0'
    - r-base
    - r-igraph
    - r-rmarkdown
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: LGPL
  summary: 'coexnet: An R package to build CO-EXpression NETworks from Microarray Data'
  description: '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.'
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/LGPL-3'

