OGSA (version 1.2.0)

outRank: outRank

Description

Counts outliers by the Ghosh method.

Usage

outRank (dataSet, phenotype, thres= 0.05, tail='right', corr=FALSE,
offsets=NULL)

Arguments

dataSet

Set of matrices of molecular data

phenotype

Vector of 1 for case, 0 for control

thres

Alpha value

tail

Vector equal to number of matrices with values 'left' or 'right' for where to find outliers

corr

Whether to correct for normal outliers

offsets

Vector equal to number of matrices which sets minimum value relative to normal to call outlier (corrected rank only)

Value

A vector with outlier counts by gene

References

Ochs, M. F., Farrar, J. E., Considine, M., Wei, Y., Meshinchi, S., & Arceci, R. J. (n.d.). Outlier Analysis and Top Scoring Pair for Integrated Data Analysis and Biomarker Discovery. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 1-1. doi:10.1109/tcbb.2013.153

D. Ghosh. (2010). Discrete Nonparametric Algorithms for Outlier Detection with Genomic Data. J. Biopharmaceutical Statistics, 20(2), 193-208.

Examples

data(ExampleData)

# Set up Phenotype
phenotype <- pheno
names(phenotype) <- colnames(cnv)

#set up dataSet
dataSet <- list(expr, meth, cnv)

# set up values for expr-meth-cnv in that order
tailLRL <- c('left', 'right', 'left')

outRankLRL <- outRank(dataSet, phenotype, thres= 0.05, tail=tailLRL,
                             corr=FALSE, offsets=NULL)