# This file created by conda-build 3.15.1
# meta.yaml template originally from:
# /Users/distiller/project/recipes/bioconductor-simlr, last modified Sun Dec  9 17:01:07 2018
# ------------------------------------------------

package:
    name: bioconductor-simlr
    version: 1.8.0
source:
    md5: a8d2108d7606877842718a0a26c6014e
    url:
        - https://bioarchive.galaxyproject.org/SIMLR_1.8.0.tar.gz
        - https://bioconductor.org/packages/3.8/bioc/src/contrib/SIMLR_1.8.0.tar.gz
        - https://depot.galaxyproject.org/software/bioconductor-simlr/bioconductor-simlr_1.8.0_src_all.tar.gz
build:
    number: '0'
    rpaths:
        - lib/
        - lib/R/lib/
    string: r351h0a44026_0
requirements:
    build:
        - cctools 895 h7512d6f_0
        - clang 4.0.1 h662ec87_0
        - clang_osx-64 4.0.1 h1ce6c1d_11
        - clangxx 4.0.1 hc9b4283_0
        - clangxx_osx-64 4.0.1 h22b1bf0_11
        - 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_2002
    host:
        - _r-mutex 1.0.0 anacondar_1
        - blas 1.0 mkl
        - bwidget 1.9.11 1
        - bzip2 1.0.6 h1de35cc_1002
        - ca-certificates 2018.10.15 ha4d7672_0
        - cairo 1.14.12 h9d4d9ac_1005
        - cctools 895 1
        - certifi 2018.8.24 py36_1001
        - clang 4.0.1 1
        - clang_osx-64 4.0.1 h1ce6c1d_11
        - clangxx 4.0.1 1
        - clangxx_osx-64 4.0.1 h22b1bf0_11
        - colormath 3.0.0 py_2
        - compiler-rt 4.0.1 hcfea43d_1
        - curl 7.62.0 heae2a1f_1000
        - decorator 4.3.0 py_0
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        - freetype 2.9.1 h5a5313f_1004
        - gettext 0.19.8.1 hcca000d_1001
        - gfortran_osx-64 4.8.5 h22b1bf0_5
        - glib 2.56.2 h67dad55_1001
        - graphite2 1.3.12 h2098e52_1001
        - harfbuzz 1.9.0 h9889186_1001
        - icu 58.2 h0a44026_1000
        - intel-openmp 2019.1 144
        - jpeg 9c h1de35cc_1001
        - krb5 1.16.2 h24a3359_1000
        - ld64 274.2 1
        - libcurl 7.62.0 h76de61e_1000
        - libcxx 4.0.1 hcfea43d_1
        - libcxxabi 4.0.1 hcfea43d_1
        - libedit 3.1.20170329 hcfe32e1_1001
        - libffi 3.2.1 h0a44026_1005
        - libgfortran 3.0.1 h93005f0_2
        - libiconv 1.15 h1de35cc_1003
        - libpng 1.6.36 ha441bb4_1000
        - libssh2 1.8.0 hf30b1f0_1003
        - libtiff 4.0.10 h79f4b77_1000
        - libxml2 2.9.8 hf14e9c8_1005
        - llvm 4.0.1 1
        - llvm-lto-tapi 4.0.1 1
        - llvm-openmp 4.0.1 hcfea43d_1
        - make 4.2.1 h1de35cc_2002
        - mkl 2018.0.3 1
        - mkl_fft 1.0.6 py36hb8a8100_0
        - mkl_random 1.0.1 py36h5d10147_1
        - ncurses 6.1 h0a44026_1001
        - networkx 2.2 py_1
        - numpy 1.15.4 py36h6a91979_0
        - numpy-base 1.15.4 py36h8a80b8c_0
        - openssl 1.0.2p h1de35cc_1001
        - pango 1.40.14 h1401c9d_1002
        - pcre 8.41 h0a44026_1003
        - pip 18.0 py36_1001
        - pixman 0.34.0 h1de35cc_1003
        - python 3.6.7 h4a56312_1001
        - r-base 3.5.1 he1ce5cd_1005
        - r-lattice 0.20_35 r351h46e59ec_1000
        - r-matrix 1.2_14 r351h46e59ec_1002
        - r-pracma 2.1.5 r351h6115d3f_1000
        - r-rcpp 1.0.0 r351h466af19_1000
        - r-rcppannoy 0.0.10 r351h466af19_1001
        - r-rcppeigen 0.3.3.4.0 r351h32998d9_0
        - r-rspectra 0.12_0 r351h0a44026_1002
        - readline 7.0 hcfe32e1_1001
        - setuptools 40.4.0 py36_1000
        - spectra 0.0.7 py36_0
        - sqlite 3.26.0 h1765d9f_1000
        - tk 8.6.9 ha441bb4_1000
        - tktable 2.10 h1de35cc_0
        - wheel 0.31.1 py36_1001
        - xz 5.2.4 h1de35cc_1001
        - zlib 1.2.11 h1de35cc_1003
    run:
        - libcxx >=4.0.1
        - r-base >=3.5.1,<3.5.2.0a0
        - r-matrix
        - r-pracma
        - r-rcpp
        - r-rcppannoy
        - r-rspectra
test:
    commands:
        - $R -e "library('SIMLR')"
about:
    home: https://bioconductor.org/packages/3.8/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
