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messina
Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems.
Bioconductor version: 3.0
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 garvan.org.au>
Citation (from within R,
enter citation("messina")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("messina")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("messina")
| R Script | Using Messina | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | BiomedicalInformatics, Classification, DifferentialExpression, GeneExpression, Software, Survival |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | EPL (>= 1.0) |
| Depends | R (>= 3.1.0), survival (>= 2.37-4), methods |
| Imports | Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics |
| LinkingTo | Rcpp |
| Suggests | knitr (>= 1.5), antiProfilesData(>= 0.99.2), Biobase(>= 2.22.0), BiocStyle |
| SystemRequirements | |
| Enhances | doMC (>= 1.3.3) |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | messina_1.2.0.tar.gz |
| Windows Binary | messina_1.2.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | messina_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | messina_1.2.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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