Other Feature Selection Methods {M3Drop} | R Documentation

## Other Feature Selection Methods

### Description

Other feature selection methods which only rank genes.

### Usage

    	irlbaPcaFS(expr_mat, pcs=c(2,3))
    	giniFS(expr_mat, suppress.plot=TRUE)
    	corFS(expr_mat, dir=c("both", "pos", "neg"), fdr=NULL)

### Arguments

`expr_mat` | normalized but not log-transformed gene expression matrix, rows=genes, cols=cells.
`pcs` | which principle components to use to score genes.
`suppress.plot` | whether to plot the gene expression vs Gini score.
`dir` | direction of correlation to consider.
`fdr` | apply an fdr threshold for significant features.

### Details

`irlbaPcaFS` Features are ranked by the sum of the magnitude of the loadings for the specified principle components. PCA is performed using the irlba package using sparse matricies for speed. `giniFS` Fits a loess curve between the maximum expression value and gini-index of each gene. Genes are ranked by p-value from a normal distribution fit to the residuals of the curve. As proposed by GiniClust [1]. `corFS` Calculates all gene-gene correlations then ranks genes by the magnitude of the most positive or negative correlation. "both" will rank by the average of the magnitudes of the most positive & negative correlation.

### Value

Sorted vector of scores for each gene from best features to worst features.

### Examples

    library(M3DExampleData)
    norm <- M3DropConvertData(Mmus_example_list$data[1:500,]);
    Features_gini <- giniFS(norm, suppress.plot=FALSE);
    Features_cor <- corFS(norm);
    Features_pca <- irlbaPcaFS(norm);

[Package M3Drop version 1.20.0 Index]