{% set version = "1.10.0" %}
{% set name = "SIMLR" %}
{% set bioc = "3.9" %}

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: e4b3f7ebe8ef92491f009efc463ccd2c
build:
  number: 1
  rpaths:
    - lib/R/lib/
    - lib/
# Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph
requirements:
  host:
    - r-base
    - r-matrix
    - r-pracma
    - r-rcpp
    - r-rcppannoy
    - r-rspectra
  run:
    - r-base
    - r-matrix
    - r-pracma
    - r-rcpp
    - r-rcppannoy
    - r-rspectra
  build:
    - {{ compiler('c') }}
    - {{ compiler('cxx') }}
    - make
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: 'file LICENSE'
  summary: 'Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization.'
extra:
  identifiers:
    - biotools:simlr
    - doi:10.1101/118901
  parent_recipe:
    name: bioconductor-simlr
    path: recipes/bioconductor-simlr
    version: 1.8.0

