# This file created by conda-build 3.21.4
# meta.yaml template originally from:
# /Users/runner/work/bioconda-recipes/bioconda-recipes/recipes/bioconductor-pcatools, last modified Thu Nov  4 08:33:00 2021
# ------------------------------------------------

package:
  name: bioconductor-pcatools
  version: 2.6.0
source:
  md5: 8707552e1bbf1b6b001141d53e9ea568
  url:
    - https://bioarchive.galaxyproject.org/PCAtools_2.6.0.tar.gz
    - https://bioconductor.org/packages/3.14/bioc/src/contrib/PCAtools_2.6.0.tar.gz
    - https://depot.galaxyproject.org/software/bioconductor-pcatools/bioconductor-pcatools_2.6.0_src_all.tar.gz
build:
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  string: r41h832b226_0
requirements:
  build:
    - cctools_osx-64 949.0.1 h6407bdd_23
    - clang 11.1.0 h694c41f_1
    - clang-11 11.1.0 default_he082bbe_1
    - clang_osx-64 11.1.0 hb91bd55_4
    - clangxx 11.1.0 default_he082bbe_1
    - clangxx_osx-64 11.1.0 h7e1b574_4
    - compiler-rt 11.1.0 h654b07c_0
    - compiler-rt_osx-64 11.1.0 h8c5fa43_0
    - ld64_osx-64 530 he8994da_21
    - ldid 2.1.2 h6a69015_3
    - libclang-cpp11.1 11.1.0 default_he082bbe_1
    - libcxx 12.0.1 habf9029_0
    - libllvm11 11.1.0 hd011deb_2
    - libllvm12 12.0.1 hd011deb_2
    - libzlib 1.2.11 h9173be1_1013
    - llvm-tools 11.1.0 hd011deb_2
    - make 4.3 h22f3db7_1
    - tapi 1100.0.11 h9ce4665_0
    - zlib 1.2.11 h9173be1_1013
  host:
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    - bioconductor-matrixgenerics 1.6.0 r41hdfd78af_0
    - bioconductor-s4vectors 0.32.0 r41h68a2ddb_0
    - bioconductor-scaledmatrix 1.2.0 r41hdfd78af_0
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    - bwidget 1.9.14 h694c41f_1
    - bzip2 1.0.8 h0d85af4_4
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    - cairo 1.16.0 he43a7df_1008
    - cctools_osx-64 973.0.1 h3e07e27_2
    - clang 12.0.1 h694c41f_4
    - clang-12 12.0.1 default_he082bbe_4
    - clang_osx-64 12.0.1 hb91bd55_1
    - clangxx 12.0.1 default_he082bbe_4
    - clangxx_osx-64 12.0.1 h7e1b574_1
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    - gmp 6.2.1 h2e338ed_0
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    - r-cli 3.1.0 r41h9951f98_0
    - r-colorspace 2.0_2 r41h28b5c78_0
    - r-cowplot 1.1.1 r41hc72bb7e_0
    - r-crayon 1.4.2 r41hc72bb7e_0
    - r-desc 1.4.0 r41hc72bb7e_0
    - r-diffobj 0.3.5 r41h28b5c78_0
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    - r-evaluate 0.14 r41hc72bb7e_2
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    - r-ggplot2 3.3.5 r41hc72bb7e_0
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    - r-lambda.r 1.2.4 r41hc72bb7e_1
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    - r-lifecycle 1.0.1 r41hc72bb7e_0
    - r-magrittr 2.0.1 r41h28b5c78_1
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    - xz 5.2.5 haf1e3a3_1
    - zlib 1.2.11 h9173be1_1013
    - zstd 1.5.0 h582d3a0_0
  run:
    - bioconductor-beachmat >=2.10.0,<2.11.0
    - bioconductor-biocparallel >=1.28.0,<1.29.0
    - bioconductor-biocsingular >=1.10.0,<1.11.0
    - bioconductor-delayedarray >=0.20.0,<0.21.0
    - bioconductor-delayedmatrixstats >=1.16.0,<1.17.0
    - libblas >=3.8.0,<4.0a0
    - libcxx >=11.1.0
    - liblapack >=3.8.0,<4.0a0
    - r-base >=4.1,<4.2.0a0
    - r-bh
    - r-cowplot
    - r-dqrng
    - r-ggplot2
    - r-ggrepel
    - r-lattice
    - r-matrix
    - r-rcpp
    - r-reshape2
test:
  commands:
    - $R -e "library('PCAtools')"
about:
  description: Principal Component Analysis (PCA) is a very powerful technique that
    has wide applicability in data science, bioinformatics, and further afield. It
    was initially developed to analyse large volumes of data in order to tease out
    the differences/relationships between the logical entities being analysed. It
    extracts the fundamental structure of the data without the need to build any model
    to represent it. This 'summary' of the data is arrived at through a process of
    reduction that can transform the large number of variables into a lesser number
    that are uncorrelated (i.e. the 'principal components'), while at the same time
    being capable of easy interpretation on the original data. PCAtools provides functions
    for data exploration via PCA, and allows the user to generate publication-ready
    figures. PCA is performed via BiocSingular - users can also identify optimal number
    of principal components via different metrics, such as elbow method and Horn's
    parallel analysis, which has relevance for data reduction in single-cell RNA-seq
    (scRNA-seq) and high dimensional mass cytometry data.
  home: https://bioconductor.org/packages/3.14/bioc/html/PCAtools.html
  license: GPL-3
  license_file: /Users/runner/bioconda/conda-bld/bioconductor-pcatools_1636016524890/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold/lib/R/share/licenses/GPL-3
  summary: 'PCAtools: Everything Principal Components Analysis'
extra:
  copy_test_source_files: true
  final: true
