selOutliers: select outliers based on PCA analysis
In mariavica/mircomb: An R package for analyzing miRNA-mRNA interactions

Description Usage Arguments Details Value See Also Examples

select outliers from miRNA or mRNA samples based on PCA analysis

1	selOutliers(obj, subset, method = "aq.plot", delete = FALSE, add.pheno = TRUE, n.dim = 2)

`obj`	a `corObject`
`subset`	`"miRNA"` or `"mRNA"`
`method`	method used to select the outliers `"aq.plot"`
`delete`	TRUE or FALSE. If TRUE, outlier samples are removed.
`add.pheno`	TRUE or FALSE. If TRUE, `"is.outlier"` column is added to pheno slot and then a PCA plot highlighting outlier samples is produced.
`n.dim`	number of components of the PCA to use.

This is an implementation of mvoutlier package. Check the origninal source for more information.

If delete=FALSE and add.pheno=FALSE, character vector with the names of the outlier samples.
If delete=FALSE and add.pheno=TRUE, a corObject with a column called is.outlier added to the pheno slot indicating if a sample is outlier or not.
If delete=TRUE, a corObject without the outlier samples in the data and pheno slot.

mvoutlier, plotPca

data(data.obj)
#selOutliers(data.obj, "miRNA", add.pheno=FALSE)
#data.obj.out <- selOutliers(data.obj, "miRNA", add.pheno=TRUE)
#head(data.obj@pheno.miRNA)
#head(data.obj.out@pheno.miRNA)