gnu: Add r-multtest.

* gnu/packages/bioconductor.scm (r-multtest): New variable.
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Roel Janssen 2018-04-24 13:55:35 +02:00
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commit a6ae9ffd6a
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@ -147,3 +147,41 @@ a suite of bioinformatics tools integrated within this self-contained software
package comprehensively addressing issues ranging from post-alignments package comprehensively addressing issues ranging from post-alignments
processing to visualization and annotation.") processing to visualization and annotation.")
(license license:gpl2))) (license license:gpl2)))
(define-public r-multtest
(package
(name "r-multtest")
(version "2.34.0")
(source
(origin
(method url-fetch)
(uri (bioconductor-uri "multtest" version))
(sha256
(base32
"0n11rd49xl2vn3ldmfips7d3yb70l8npjcqsxyswr9ypjhgzkv9j"))))
(build-system r-build-system)
(propagated-inputs
`(("r-survival" ,r-survival)
("r-biocgenerics" ,r-biocgenerics)
("r-biobase" ,r-biobase)
("r-mass" ,r-mass)))
(home-page "http://bioconductor.org/packages/multtest")
(synopsis "Resampling-based multiple hypothesis testing")
(description
"This package can do non-parametric bootstrap and permutation
resampling-based multiple testing procedures (including empirical Bayes
methods) for controlling the family-wise error rate (FWER), generalized
family-wise error rate (gFWER), tail probability of the proportion of
false positives (TPPFP), and false discovery rate (FDR). Several choices
of bootstrap-based null distribution are implemented (centered, centered
and scaled, quantile-transformed). Single-step and step-wise methods are
available. Tests based on a variety of T- and F-statistics (including
T-statistics based on regression parameters from linear and survival models
as well as those based on correlation parameters) are included. When probing
hypotheses with T-statistics, users may also select a potentially faster null
distribution which is multivariate normal with mean zero and variance
covariance matrix derived from the vector influence function. Results are
reported in terms of adjusted P-values, confidence regions and test statistic
cutoffs. The procedures are directly applicable to identifying differentially
expressed genes in DNA microarray experiments.")
(license license:lgpl3)))