outCallRank {OGSA} | R Documentation

## outCallRank

### Description

Counts outliers by the Ghosh method and generates list objects with all outliers noted

### Usage

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

### Arguments

`dataSet` | Set of matrices of molecular data
`phenotype` | A vector of 0s and 1s of length nSample, where 1 = case, 0 = control
`thres` | Alpha value
`tail` | A vector equal to the number of matrices with values left or right for where to find outliers
`corr` | Whether to correct for normal outliers
`offsets` | A vector equal to the number of matrices which sets the minimum value relative to normal to call outlier (corrected rank only)
`names` | A vector equal to the number of matrices to name molecular type of data (e.g., CNV)

### Value

A list with all specific outlier calls for each molecular type in each case sample

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

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

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

    outRankLRL <- outCallRank(dataSet, phenotype, names=c('Expr',
                                 'Meth', 'CNV'), tail=tailLRL)

[Package OGSA version 1.16.0 Index]