{% set version = "1.6.2" %}
{% set name = "scPCA" %}
{% set bioc = "3.13" %}

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: cbb8f818a16a60ed343937b6cc016853
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark
requirements:
  host:
    - 'bioconductor-biocparallel >=1.26.0,<1.27.0'
    - 'bioconductor-delayedarray >=0.18.0,<0.19.0'
    - 'bioconductor-matrixgenerics >=1.4.0,<1.5.0'
    - 'bioconductor-scaledmatrix >=1.0.0,<1.1.0'
    - r-assertthat
    - r-base
    - r-cluster
    - r-coop
    - r-dplyr
    - r-elasticnet
    - r-kernlab
    - r-matrix
    - r-matrixstats
    - r-origami
    - r-purrr
    - r-rdpack
    - r-rspectra
    - r-sparsepca
    - r-stringr
    - r-tibble
  run:
    - 'bioconductor-biocparallel >=1.26.0,<1.27.0'
    - 'bioconductor-delayedarray >=0.18.0,<0.19.0'
    - 'bioconductor-matrixgenerics >=1.4.0,<1.5.0'
    - 'bioconductor-scaledmatrix >=1.0.0,<1.1.0'
    - r-assertthat
    - r-base
    - r-cluster
    - r-coop
    - r-dplyr
    - r-elasticnet
    - r-kernlab
    - r-matrix
    - r-matrixstats
    - r-origami
    - r-purrr
    - r-rdpack
    - r-rspectra
    - r-sparsepca
    - r-stringr
    - r-tibble
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: 'MIT + file LICENSE'
  summary: 'Sparse Contrastive Principal Component Analysis'
  description: 'A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA''s ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA.'
  license_file: LICENSE

