# This file created by conda-build 24.7.1
# meta.yaml template originally from:
# /opt/recipe, last modified Tue Dec 24 15:24:41 2024
# ------------------------------------------------

package:
  name: bioconductor-scmet
  version: 1.8.0
source:
  md5: 454268faca57a0ade6352837704f5b15
  url:
    - https://bioarchive.galaxyproject.org/scMET_1.8.0.tar.gz
    - https://bioconductor.org/packages/3.20/bioc/src/contrib/Archive/scMET/scMET_1.8.0.tar.gz
    - https://bioconductor.org/packages/3.20/bioc/src/contrib/scMET_1.8.0.tar.gz
    - https://depot.galaxyproject.org/software/bioconductor-scmet/bioconductor-scmet_1.8.0_src_all.tar.gz
build:
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  run_exports: bioconductor-scmet >=1.8.0,<1.9.0a0
  string: r44he5774e6_0
requirements:
  build:
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    - sysroot_linux-64 2.17 h4a8ded7_18
    - tzdata 2024b hc8b5060_0
  host:
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  run:
    - bioconductor-biocstyle >=2.34.0,<2.35.0
    - bioconductor-biocstyle >=2.34.0,<2.35.0a0
    - bioconductor-s4vectors >=0.44.0,<0.45.0
    - bioconductor-s4vectors >=0.44.0,<0.45.0a0
    - bioconductor-singlecellexperiment >=1.28.0,<1.29.0
    - bioconductor-singlecellexperiment >=1.28.0,<1.29.0a0
    - bioconductor-summarizedexperiment >=1.36.0,<1.37.0
    - bioconductor-summarizedexperiment >=1.36.0,<1.37.0a0
    - libblas >=3.9.0,<4.0a0
    - libgcc >=13
    - liblapack >=3.9.0,<4.0a0
    - libstdcxx >=13
    - r-assertthat
    - r-base >=4.4,<4.5.0a0
    - r-base >=4.4,<4.5.0a0
    - r-bh >=1.66.0
    - r-coda
    - r-cowplot
    - r-data.table
    - r-dplyr
    - r-ggplot2
    - r-logitnorm
    - r-mass
    - r-matrix
    - r-matrixstats
    - r-rcpp >=1.0.0
    - r-rcppeigen >=0.3.3.3.0
    - r-rcppparallel >=5.0.1
    - r-rstan >=2.21.3
    - r-rstantools >=2.1.0
    - r-stanheaders >=2.21.0.7
    - r-vgam
    - r-viridis
test:
  commands:
    - $R -e "library('scMET')"
about:
  description: High-throughput single-cell measurements of DNA methylomes can quantify
    methylation heterogeneity and uncover its role in gene regulation. However, technical
    limitations and sparse coverage can preclude this task. scMET is a hierarchical
    Bayesian model which overcomes sparsity, sharing information across cells and
    genomic features to robustly quantify genuine biological heterogeneity. scMET
    can identify highly variable features that drive epigenetic heterogeneity, and
    perform differential methylation and variability analyses. We illustrate how scMET
    facilitates the characterization of epigenetically distinct cell populations and
    how it enables the formulation of novel hypotheses on the epigenetic regulation
    of gene expression.
  home: https://bioconductor.org/packages/3.20/bioc/html/scMET.html
  license: GPL-3
  license_file: /opt/conda/conda-bld/bioconductor-scmet_1735072757844/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_plac/lib/R/share/licenses/GPL-3
  summary: Bayesian modelling of cell-to-cell DNA methylation heterogeneity
extra:
  copy_test_source_files: true
  final: true
