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Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems.

Bioconductor version: Release (3.1)

Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression.

Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph]

Maintainer: Mark Pinese <m.pinese at>

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biocViews BiomedicalInformatics, Classification, DifferentialExpression, GeneExpression, Software, Survival
Version 1.4.0
In Bioconductor since BioC 2.14 (R-3.1) (1.5 years)
License EPL (>= 1.0)
Depends R (>= 3.1.0), survival (>= 2.37-4), methods
Imports Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>=, grid (>= 3.1.0), foreach (>= 1.4.1), graphics
LinkingTo Rcpp
Suggests knitr (>= 1.5), antiProfilesData(>= 0.99.2), Biobase(>= 2.22.0), BiocStyle
Enhances doMC (>= 1.3.3)
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