outCount: outCount

In OGSA: Outlier Gene Set Analysis

Description

Counts outliers by the Tibshirani and Hastie method. Adds the ability to subtract for outliers in the normals using corr = TRUE

Usage

outCount (data, phenotype, tail='right', corr=FALSE)

Arguments

data	A matrix of nGene by nSample
phenotype	A vector of 0s and 1s of length nSample, where 1 = case, 0 = control
tail	Indicates whether outliers are up (right) or down (left) outliers
corr	Whether to correct for normal outliers

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

Examples

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

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

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

outTibLRL <- outCallTib(dataSet, phenotype=pheno,
                         names=c('Expr', 'Meth', 'CNV'), tail=tailLRL)

OGSA documentation built on April 28, 2020, 6:58 p.m.