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gnu: Add r-rcistarget.

* gnu/packages/bioconductor.scm (r-rcistarget): New variable.
master
Ricardo Wurmus 2019-08-15 17:39:11 +02:00
parent 19f1aac0e7
commit bb88417fb7
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@ -5089,6 +5089,41 @@ by a sparse number of variables, this method can reduce the complexity of
data, to only emphasize the data that actually matters.")
(license license:expat)))
(define-public r-rcistarget
(package
(name "r-rcistarget")
(version "1.4.0")
(source
(origin
(method url-fetch)
(uri (bioconductor-uri "RcisTarget" version))
(sha256
(base32
"133x2vr86ifbk82q08x1c8q19zsk5za7b6qrzz77dhsyf4bhcvpd"))))
(properties `((upstream-name . "RcisTarget")))
(build-system r-build-system)
(propagated-inputs
`(("r-aucell" ,r-aucell)
("r-biocgenerics" ,r-biocgenerics)
("r-data-table" ,r-data-table)
("r-feather" ,r-feather)
("r-gseabase" ,r-gseabase)
("r-r-utils" ,r-r-utils)
("r-summarizedexperiment" ,r-summarizedexperiment)))
(home-page "https://aertslab.org/#scenic")
(synopsis "Identify transcription factor binding motifs enriched on a gene list")
(description
"RcisTarget identifies @dfn{transcription factor binding motifs} (TFBS)
over-represented on a gene list. In a first step, RcisTarget selects DNA
motifs that are significantly over-represented in the surroundings of the
@dfn{transcription start site} (TSS) of the genes in the gene-set. This is
achieved by using a database that contains genome-wide cross-species rankings
for each motif. The motifs that are then annotated to TFs and those that have
a high @dfn{Normalized Enrichment Score} (NES) are retained. Finally, for
each motif and gene-set, RcisTarget predicts the candidate target genes (i.e.
genes in the gene-set that are ranked above the leading edge).")
(license license:gpl3)))
(define-public r-cicero
(package
(name "r-cicero")