# This file created by conda-build 3.18.7
# meta.yaml template originally from:
# /Users/distiller/project/recipes/bioconductor-simlr, last modified Fri Jul 19 05:08:35 2019
# ------------------------------------------------

package:
    name: bioconductor-simlr
    version: 1.10.0
source:
    md5: e4b3f7ebe8ef92491f009efc463ccd2c
    url:
        - https://bioarchive.galaxyproject.org/SIMLR_1.10.0.tar.gz
        - https://bioconductor.org/packages/3.9/bioc/src/contrib/SIMLR_1.10.0.tar.gz
        - https://depot.galaxyproject.org/software/bioconductor-simlr/bioconductor-simlr_1.10.0_src_all.tar.gz
build:
    number: '1'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r36h6de7cb9_1
requirements:
    build:
        - cctools 895 h7512d6f_0
        - clang 4.0.1 h662ec87_0
        - clang_osx-64 4.0.1 h1ce6c1d_16
        - clangxx 4.0.1 hc9b4283_0
        - clangxx_osx-64 4.0.1 h22b1bf0_16
        - compiler-rt 4.0.1 h5487866_0
        - ld64 274.2 h7c2db76_0
        - libcxx 4.0.1 h579ed51_0
        - libcxxabi 4.0.1 hebd6815_0
        - llvm 4.0.1 hc748206_0
        - llvm-lto-tapi 4.0.1 h6701bc3_0
        - make 4.2.1 h1de35cc_2004
    host:
        - _r-mutex 1.0.1 anacondar_1
        - bwidget 1.9.11 0
        - bzip2 1.0.8 h01d97ff_0
        - ca-certificates 2019.6.16 hecc5488_0
        - cairo 1.16.0 h0ab9d94_1001
        - cctools 895 1
        - certifi 2019.6.16 py37_1
        - clang 4.0.1 1
        - clang_osx-64 4.0.1 h1ce6c1d_16
        - clangxx 4.0.1 1
        - clangxx_osx-64 4.0.1 h22b1bf0_16
        - colormath 3.0.0 py_2
        - compiler-rt 4.0.1 hcfea43d_1
        - curl 7.65.2 h22ea746_0
        - decorator 4.4.0 py_0
        - fontconfig 2.13.1 h1027ab8_1000
        - freetype 2.10.0 h24853df_0
        - gettext 0.19.8.1 h46ab8bc_1002
        - gfortran_osx-64 4.8.5 h22b1bf0_8
        - glib 2.58.3 h9d45998_1002
        - graphite2 1.3.13 h2098e52_1000
        - gsl 2.5 ha2d443c_0
        - harfbuzz 2.4.0 h92b87b8_1
        - icu 58.2 h0a44026_1000
        - jpeg 9c h1de35cc_1001
        - krb5 1.16.3 hcfa6398_1001
        - ld64 274.2 1
        - libblas 3.8.0 10_openblas
        - libcblas 3.8.0 10_openblas
        - libcurl 7.65.2 h16faf7d_0
        - libcxx 4.0.1 hcfea43d_1
        - libcxxabi 4.0.1 hcfea43d_1
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        - libffi 3.2.1 h6de7cb9_1006
        - libgfortran 3.0.1 0
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        - liblapack 3.8.0 10_openblas
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        - libtiff 4.0.10 hd08fb8f_1003
        - libxml2 2.9.9 hd80cff7_2
        - llvm 4.0.1 1
        - llvm-lto-tapi 4.0.1 1
        - llvm-openmp 8.0.0 h770b8ee_0
        - lz4-c 1.8.3 h6de7cb9_1001
        - make 4.2.1 h1de35cc_2004
        - ncurses 6.1 h0a44026_1002
        - networkx 2.3 py_0
        - numpy 1.16.4 py37h6b0580a_0
        - openblas 0.3.6 hd44dcd8_4
        - openssl 1.1.1c h01d97ff_0
        - pango 1.40.14 h0da9b22_1005
        - pcre 8.41 h0a44026_1003
        - pip 19.1.1 py37_0
        - pixman 0.38.0 h01d97ff_1003
        - python 3.7.3 h93065d6_1
        - r-base 3.6.1 h577800e_2
        - r-lattice 0.20_38 r36h159158b_1001
        - r-matrix 1.2_17 r36h159158b_1
        - r-pracma 2.2.5 r36h6115d3f_2
        - r-rcpp 1.0.1 r36hf99fc2c_1
        - r-rcppannoy 0.0.12 r36hf99fc2c_1
        - r-rcppeigen 0.3.3.5.0 r36hf99fc2c_1
        - r-rspectra 0.15_0 r36hf99fc2c_1
        - readline 8.0 hcfe32e1_0
        - setuptools 41.0.1 py37_0
        - spectra 0.0.11 py_1
        - sqlite 3.29.0 hb7d70f7_0
        - tk 8.6.9 h2573ce8_1002
        - tktable 2.10 hfa822fa_1
        - wheel 0.33.4 py37_0
        - xz 5.2.4 h1de35cc_1001
        - zlib 1.2.11 h01d97ff_1005
        - zstd 1.4.0 ha9f0a20_0
    run:
        - libcxx >=4.0.1
        - r-base >=3.6,<3.7.0a0
        - r-matrix
        - r-pracma
        - r-rcpp
        - r-rcppannoy
        - r-rspectra
test:
    commands:
        - $R -e "library('SIMLR')"
about:
    home: https://bioconductor.org/packages/3.9/bioc/html/SIMLR.html
    license: file LICENSE
    summary: Single-cell RNA-seq technologies enable high throughput gene expression
        measurement of individual cells, and allow the discovery of heterogeneity
        within cell populations. Measurement of cell-to-cell gene expression similarity
        is critical for the identification, visualization and analysis of cell populations.
        However, single-cell data introduce challenges to conventional measures of
        gene expression similarity because of the high level of noise, outliers and
        dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell
        Interpretation via Multi-kernel LeaRning), which learns an appropriate distance
        metric from the data for dimension reduction, clustering and visualization.
extra:
    copy_test_source_files: true
    final: true
    identifiers:
        - biotools:simlr
        - doi:10.1101/118901
