outRank {OGSA} | R Documentation

## 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)

[Package OGSA version 1.10.0 Index]