{% set version = "1.0.0" %}
{% set name = "ILoReg" %}
{% set bioc = "3.12" %}

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: f5b976e7d38d18e6133f4482998fd840
build:
  number: 1
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: knitr, rmarkdown
requirements:
  host:
    - 'bioconductor-s4vectors >=0.28.0,<0.29.0'
    - 'bioconductor-singlecellexperiment >=1.12.0,<1.13.0'
    - 'bioconductor-summarizedexperiment >=1.20.0,<1.21.0'
    - r-aricode
    - r-base
    - r-cluster
    - r-cowplot
    - r-dendextend
    - r-desctools
    - r-dorng
    - r-dosnow
    - r-dplyr
    - r-fastcluster
    - r-foreach
    - r-ggplot2
    - r-liblinear
    - r-matrix
    - r-paralleldist
    - r-pheatmap
    - r-plyr
    - r-reshape2
    - r-rspectra
    - r-rtsne
    - r-scales
    - r-sparsem
    - r-umap
  run:
    - 'bioconductor-s4vectors >=0.28.0,<0.29.0'
    - 'bioconductor-singlecellexperiment >=1.12.0,<1.13.0'
    - 'bioconductor-summarizedexperiment >=1.20.0,<1.21.0'
    - r-aricode
    - r-base
    - r-cluster
    - r-cowplot
    - r-dendextend
    - r-desctools
    - r-dorng
    - r-dosnow
    - r-dplyr
    - r-fastcluster
    - r-foreach
    - r-ggplot2
    - r-liblinear
    - r-matrix
    - r-paralleldist
    - r-pheatmap
    - r-plyr
    - r-reshape2
    - r-rspectra
    - r-rtsne
    - r-scales
    - r-sparsem
    - r-umap
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: GPL-3
  summary: 'ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data'
  description: 'ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided.'
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-3'

