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gnu: Add python-ikarus.
* gnu/packages/bioinformatics.scm (python-ikarus): New variable.
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@ -13520,6 +13520,53 @@ (define-public pyscenic
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single-cell RNA-seq data.")
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(license license:gpl3+)))
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(define-public python-ikarus
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(package
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(name "python-ikarus")
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(version "0.0.2")
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(source
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(origin
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(method url-fetch)
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(uri (pypi-uri "ikarus" version))
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(sha256
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(base32
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"086czpvj4yafz4vrq5rx2gy0bj2l8nzwnkk0gw8qvy4w133xjysy"))))
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(build-system python-build-system)
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(arguments
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`(#:tests? #false
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#:phases
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(modify-phases %standard-phases
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;; See https://github.com/BIMSBbioinfo/ikarus/issues/12
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(add-after 'unpack 'fix-issue-12
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(lambda _
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(substitute* "ikarus/classifier.py"
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(("pyscenic.genesig") "ctxcore.genesig"))))
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;; Numba needs a writable dir to cache functions.
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(add-before 'check 'set-numba-cache-dir
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(lambda _
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(setenv "NUMBA_CACHE_DIR" "/tmp"))))))
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(propagated-inputs
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(list python-numpy
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python-pandas
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python-scipy
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python-scanpy
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python-anndata
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python-ctxcore ;because of issue 12
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pyscenic))
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(home-page "https://github.com/BIMSBbioinfo/ikarus")
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(synopsis "Machine learning classifier of tumor cells")
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(description
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"ikarus is a stepwise machine learning pipeline that tries to cope with a task
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of distinguishing tumor cells from normal cells. Leveraging multiple
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annotated single cell datasets it can be used to define a gene set specific to
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tumor cells. First, the latter gene set is used to rank cells and then to
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train a logistic classifier for the robust classification of tumor and normal
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cells. Finally, sensitivity is increased by propagating the cell labels based
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on a custom cell-cell network. ikarus is tested on multiple single cell
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datasets to ascertain that it achieves high sensitivity and specificity in
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multiple experimental contexts.")
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(license license:expat)))
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(define-public vbz-compression
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(package
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(name "vbz-compression")
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