# This file created by conda-build 24.7.1
# meta.yaml template originally from:
# /opt/recipe, last modified Tue Dec 24 21:48:25 2024
# ------------------------------------------------

package:
  name: bioconductor-micsqtl
  version: 1.4.0
source:
  md5: 09eaf773ad8d3777ca46e06d1afcdab9
  url:
    - https://bioarchive.galaxyproject.org/MICSQTL_1.4.0.tar.gz
    - https://bioconductor.org/packages/3.20/bioc/src/contrib/Archive/MICSQTL/MICSQTL_1.4.0.tar.gz
    - https://bioconductor.org/packages/3.20/bioc/src/contrib/MICSQTL_1.4.0.tar.gz
    - https://depot.galaxyproject.org/software/bioconductor-micsqtl/bioconductor-micsqtl_1.4.0_src_all.tar.gz
build:
  noarch: generic
  number: '0'
  rpaths:
    - lib/
    - lib/R/lib/
  run_exports: bioconductor-micsqtl >=1.4.0,<1.5.0a0
  string: r44hdfd78af_0
requirements:
  host:
    - _libgcc_mutex 0.1 conda_forge
    - _openmp_mutex 4.5 2_gnu
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    - readline 8.2 h8228510_1
    - sed 4.8 he412f7d_0
    - setuptools 75.6.0 pyhff2d567_1
    - sysroot_linux-64 2.17 h4a8ded7_18
    - tk 8.6.13 noxft_h4845f30_101
    - tktable 2.10 h8bc8fbc_6
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    - tzdata 2024b hc8b5060_0
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    - xorg-libxau 1.0.12 hb9d3cd8_0
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    - xorg-libxext 1.3.6 hb9d3cd8_0
    - xorg-libxrender 0.9.12 hb9d3cd8_0
    - xorg-libxt 1.3.1 hb9d3cd8_0
    - yaml 0.2.5 h7f98852_2
    - yq 3.4.3 pyhd8ed1ab_1
    - zstd 1.5.6 ha6fb4c9_0
  run:
    - bioconductor-biocparallel >=1.40.0,<1.41.0
    - bioconductor-s4vectors >=0.44.0,<0.45.0
    - bioconductor-summarizedexperiment >=1.36.0,<1.37.0
    - bioconductor-toast >=1.20.0,<1.21.0
    - r-base >=4.4,<4.5.0a0
    - r-base >=4.4,<4.5.0a0
    - r-dirmult
    - r-ggplot2
    - r-ggpubr
    - r-ggridges
    - r-glue
    - r-magrittr
    - r-nnls
    - r-purrr
    - r-tca
test:
  commands:
    - $R -e "library('MICSQTL')"
about:
  description: Our pipeline, MICSQTL, utilizes scRNA-seq reference and bulk transcriptomes
    to estimate cellular composition in the matched bulk proteomes. The expression
    of genes and proteins at either bulk level or cell type level can be integrated
    by Angle-based Joint and Individual Variation Explained (AJIVE) framework. Meanwhile,
    MICSQTL can perform cell-type-specic quantitative trait loci (QTL) mapping to
    proteins or transcripts based on the input of bulk expression data and the estimated
    cellular composition per molecule type, without the need for single cell sequencing.
    We use matched transcriptome-proteome from human brain frontal cortex tissue samples
    to demonstrate the input and output of our tool.
  home: https://bioconductor.org/packages/3.20/bioc/html/MICSQTL.html
  license: GPL-3
  license_file: /opt/conda/conda-bld/bioconductor-micsqtl_1735087792198/_h_env_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_placehold_pl/lib/R/share/licenses/GPL-3
  summary: MICSQTL (Multi-omic deconvolution, Integration and Cell-type-specific Quantitative
    Trait Loci)
extra:
  copy_test_source_files: true
  final: true
