outCount {OGSA} | R Documentation

## outCount

### 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)

[Package OGSA version 1.16.0 Index]