gnu: Add r-rcistarget.
* gnu/packages/bioconductor.scm (r-rcistarget): New variable.master
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@ -5089,6 +5089,41 @@ by a sparse number of variables, this method can reduce the complexity of
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data, to only emphasize the data that actually matters.")
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(license license:expat)))
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(define-public r-rcistarget
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(package
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(name "r-rcistarget")
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(version "1.4.0")
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(source
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(origin
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(method url-fetch)
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(uri (bioconductor-uri "RcisTarget" version))
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(sha256
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(base32
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"133x2vr86ifbk82q08x1c8q19zsk5za7b6qrzz77dhsyf4bhcvpd"))))
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(properties `((upstream-name . "RcisTarget")))
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(build-system r-build-system)
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(propagated-inputs
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`(("r-aucell" ,r-aucell)
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("r-biocgenerics" ,r-biocgenerics)
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("r-data-table" ,r-data-table)
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("r-feather" ,r-feather)
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("r-gseabase" ,r-gseabase)
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("r-r-utils" ,r-r-utils)
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("r-summarizedexperiment" ,r-summarizedexperiment)))
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(home-page "https://aertslab.org/#scenic")
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(synopsis "Identify transcription factor binding motifs enriched on a gene list")
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(description
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"RcisTarget identifies @dfn{transcription factor binding motifs} (TFBS)
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over-represented on a gene list. In a first step, RcisTarget selects DNA
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motifs that are significantly over-represented in the surroundings of the
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@dfn{transcription start site} (TSS) of the genes in the gene-set. This is
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achieved by using a database that contains genome-wide cross-species rankings
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for each motif. The motifs that are then annotated to TFs and those that have
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a high @dfn{Normalized Enrichment Score} (NES) are retained. Finally, for
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each motif and gene-set, RcisTarget predicts the candidate target genes (i.e.
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genes in the gene-set that are ranked above the leading edge).")
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(license license:gpl3)))
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(define-public r-cicero
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(package
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(name "r-cicero")
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