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

1 2 |

`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) |

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

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
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)
``` |

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