{% set version = "1.20.1" %}
{% set name = "AUCell" %}
{% set bioc = "3.16" %}

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|lower }}/bioconductor-{{ name|lower }}_{{ version }}_src_all.tar.gz'
  md5: 1da1c84f54ae28d244fdcf6df5de12b1
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, rbokeh, Rtsne, testthat, zoo
requirements:
  host:
    - 'bioconductor-biocgenerics >=0.44.0,<0.45.0'
    - 'bioconductor-delayedarray >=0.24.0,<0.25.0'
    - 'bioconductor-delayedmatrixstats >=1.20.0,<1.21.0'
    - 'bioconductor-gseabase >=1.60.0,<1.61.0'
    - 'bioconductor-summarizedexperiment >=1.28.0,<1.29.0'
    - r-base
    - r-data.table
    - r-mixtools
    - r-r.utils
    - r-shiny
  run:
    - 'bioconductor-biocgenerics >=0.44.0,<0.45.0'
    - 'bioconductor-delayedarray >=0.24.0,<0.25.0'
    - 'bioconductor-delayedmatrixstats >=1.20.0,<1.21.0'
    - 'bioconductor-gseabase >=1.60.0,<1.61.0'
    - 'bioconductor-summarizedexperiment >=1.28.0,<1.29.0'
    - r-base
    - r-data.table
    - r-mixtools
    - r-r.utils
    - r-shiny
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: GPL-3
  summary: 'AUCell: Analysis of ''gene set'' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures)'
  description: 'AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed.'
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'

