Other_FS: Other Feature Selection Methods
In M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Description Usage Arguments Details Value Examples

Other feature selection methods which only rank genes.

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

`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.

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.

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

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);