{% set version = "1.8.0" %}
{% set name = "POWSC" %}
{% set bioc = "3.17" %}

package:
  name: 'bioconductor-{{ name|lower }}'
  version: '{{ version }}'
source:
  url:
    - 'https://bioconductor.org/packages/{{ bioc }}/bioc/src/contrib/{{ name }}_{{ version }}.tar.gz'
    - 'https://bioconductor.org/packages/{{ bioc }}/bioc/src/contrib/Archive/{{ name }}/{{ 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: 68f052aeb186749e50a9f2bbb5cbe3f6
build:
  number: 0
  rpaths:
    - lib/R/lib/
    - lib/
  noarch: generic
# Suggests: rmarkdown, knitr, testthat (>= 3.0.0), BiocStyle
requirements:
  host:
    - 'bioconductor-biobase >=2.60.0,<2.61.0'
    - 'bioconductor-limma >=3.56.0,<3.57.0'
    - 'bioconductor-mast >=1.26.0,<1.27.0'
    - 'bioconductor-singlecellexperiment >=1.22.0,<1.23.0'
    - 'bioconductor-summarizedexperiment >=1.30.0,<1.31.0'
    - r-base
    - r-ggplot2
    - r-pheatmap
    - r-rcolorbrewer
  run:
    - 'bioconductor-biobase >=2.60.0,<2.61.0'
    - 'bioconductor-limma >=3.56.0,<3.57.0'
    - 'bioconductor-mast >=1.26.0,<1.27.0'
    - 'bioconductor-singlecellexperiment >=1.22.0,<1.23.0'
    - 'bioconductor-summarizedexperiment >=1.30.0,<1.31.0'
    - r-base
    - r-ggplot2
    - r-pheatmap
    - r-rcolorbrewer
test:
  commands:
    - '$R -e "library(''{{ name }}'')"'
about:
  home: 'https://bioconductor.org/packages/{{ bioc }}/bioc/html/{{ name }}.html'
  license: GPL-2
  summary: 'Simulation, power evaluation, and sample size recommendation for single cell RNA-seq'
  description: 'Determining the sample size for adequate power to detect statistical significance is a crucial step at the design stage for high-throughput experiments. Even though a number of methods and tools are available for sample size calculation for microarray and RNA-seq in the context of differential expression (DE), this topic in the field of single-cell RNA sequencing is understudied. Moreover, the unique data characteristics present in scRNA-seq such as sparsity and heterogeneity increase the challenge. We propose POWSC, a simulation-based method, to provide power evaluation and sample size recommendation for single-cell RNA sequencing DE analysis. POWSC consists of a data simulator that creates realistic expression data, and a power assessor that provides a comprehensive evaluation and visualization of the power and sample size relationship.'
  license_file: '{{ environ["PREFIX"] }}/lib/R/share/licenses/GPL-2'

