# This file created by conda-build 3.24.0
# meta.yaml template originally from:
# /opt/recipe, last modified Sun Aug  6 22:24:00 2023
# ------------------------------------------------

package:
  name: bioconductor-bayesspace
  version: 1.10.1
source:
  md5: 674dcaf64d6130d014c2c1fbdf7d15a1
  url:
    - https://bioarchive.galaxyproject.org/BayesSpace_1.10.1.tar.gz
    - https://bioconductor.org/packages/3.17/bioc/src/contrib/Archive/BayesSpace/BayesSpace_1.10.1.tar.gz
    - https://bioconductor.org/packages/3.17/bioc/src/contrib/BayesSpace_1.10.1.tar.gz
    - https://depot.galaxyproject.org/software/bioconductor-bayesspace/bioconductor-bayesspace_1.10.1_src_all.tar.gz
build:
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r43hf17093f_0
requirements:
  build:
    - _libgcc_mutex 0.1 conda_forge
    - _openmp_mutex 4.5 2_gnu
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    - gcc_linux-64 12.3.0 h76fc315_1
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    - gxx_linux-64 12.3.0 h8a814eb_1
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    - sysroot_linux-64 2.12 he073ed8_16
  host:
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    - yq 3.2.2 pyhd8ed1ab_0
    - zlib 1.2.13 hd590300_5
    - zstd 1.5.2 hfc55251_7
  run:
    - bioconductor-biocfilecache >=2.8.0,<2.9.0
    - bioconductor-biocsingular >=1.16.0,<1.17.0
    - bioconductor-rhdf5 >=2.44.0,<2.45.0
    - bioconductor-s4vectors >=0.38.0,<0.39.0
    - bioconductor-scater >=1.28.0,<1.29.0
    - bioconductor-scran >=1.28.0,<1.29.0
    - bioconductor-singlecellexperiment >=1.22.0,<1.23.0
    - bioconductor-summarizedexperiment >=1.30.0,<1.31.0
    - libblas >=3.9.0,<4.0a0
    - libgcc-ng >=12
    - liblapack >=3.9.0,<4.0a0
    - libstdcxx-ng >=12
    - r-assertthat
    - r-base >=4.3,<4.4.0a0
    - r-coda
    - r-dirichletreg
    - r-ggplot2
    - r-matrix
    - r-mclust
    - r-purrr
    - r-rcpp >=1.0.4.6
    - r-rcpparmadillo
    - r-rcppdist
    - r-rcppprogress
    - r-rcurl
    - r-scales
    - r-xgboost
test:
  commands:
    - $R -e "library('BayesSpace')"
about:
  description: Tools for clustering and enhancing the resolution of spatial gene expression
    experiments. BayesSpace clusters a low-dimensional representation of the gene
    expression matrix, incorporating a spatial prior to encourage neighboring spots
    to cluster together. The method can enhance the resolution of the low-dimensional
    representation into "sub-spots", for which features such as gene expression or
    cell type composition can be imputed.
  home: https://bioconductor.org/packages/3.17/bioc/html/BayesSpace.html
  license: MIT + file LICENSE
  license_file: LICENSE
  summary: Clustering and Resolution Enhancement of Spatial Transcriptomes
extra:
  copy_test_source_files: true
  final: true
