DaMiR.FReduct {DaMiRseq} | R Documentation

## Remove highly correlated features, based on feature-per-feature correlation.

### Description

This function allows the user to remove highly correlated features.

### Usage

    DaMiR.FReduct(data, th.corr = 0.85, type = c("spearman", "pearson"))

### Arguments

`data` | A transposed data frame or matrix of normalized expression data. Rows and Cols should be, respectively, observations and features
`th.corr` | Feature-per-feature correlation threshold; default is 0.85
`type` | Type of correlation metric to be applied; default is "spearman"

### Details

This function produces an absolute correlation matrix that it is then used to reduce pair-wise correlations. When two features present a correlation higher than that defined by the user in `th.corr` argument, the function, first, calculates the mean absolute correlation of each feature and, then, removes the feature with the largest mean absolute correlation.

### Value

An expression matrix without highly correlated features.

### Author(s)

Mattia Chiesa, Luca Piacentini

### See Also

`rcorr`, `findCorrelation`

### Examples

    # use example data:
    data(data_reduced)
    # reduce the number of features:
    data_Reduced <- DaMiR.FReduct(data_reduced,
    th.corr = 0.75, type = "pearson")

[Package DaMiRseq version 2.2.0 Index]