split.MADOutlier: Outliers using left and right MAD
In discordant: The Discordant Method: A Novel Approach for Differential Correlation

Description Usage Arguments Details Value References Examples

Identify features with outliers using left and right median absolute deviation (MAD).

1	splitMADOutlier(mat, filter0 = TRUE, threshold = 2)

`mat`	mxn matrix of -omics data, where rows are features and columns samples.
`filter0`	Option to filter out features if they have at least one 0 value. Default is TRUE.
`threshold`	Threshold of how many MADs outside the left or right median is used to determine features with outliers.

The purpose of this function is to determine outliers in non-symmetric distributions. The distribution is split by the median. Outliers are identifed by being however many median absolute deviations (MAD) from either split distribution.

`mat.filtered`	Input matrix where features with outliers filtered out.
`index`	Index of features that have no outliers.

Leys C, Klein O, Bernard P and Licata L. "Detecting Outliers: Do Not Use Standard Deviation Around the Mean, Use Absolute Deivation Around the Median." Journal of Experimental Social Psychology, 2013. 49(4), 764-766. Magwene, PM, Willis JH, Kelly JK and Siepel A. "The Statistics of Bulk Segregant Analysis Using Next Generation Sequencing." PLoS Computational Biology, 2011. 7(11), e1002255.

## Simulate matrix of continuous -omics data.
data(TCGA_Breast_miRNASeq)

## Filter matrix based on outliers.
mat.filtered <- splitMADOutlier(TCGA_Breast_miRNASeq)$mat.filtered