NBumiFeatureSelectionCombinedDrop {M3Drop} | R Documentation

## Dropout-based Feature Selection

### Description

Ranks genes by significance of increase in dropouts compared to expectation.

### Usage

    	NBumiFeatureSelectionCombinedDrop(fit, ntop=NULL, method="fdr", qval.thresh=2, suppress.plot=TRUE)

### Arguments

`fit` | output from NBumiFitModel or NBumiFitBasicModel.
`ntop` | number of top ranked genes to return
`method` | correction method for multiple comparisons (check ?p.adjust.methods for more details)
`qval.thresh` | significant threshold
`suppress.plot` | logical, whether to plot the fitted curve and highlight selected features

### Details

Calculates dropout probability for each observation using depth-adjusted negative binomial means and dispersions calculated from a fitted power-law relationship between mean and dispersion. Total dropouts per gene are modelled using the normal approximation of the sum of bernoulli variables. And significance is evaluated using a Z-test.

If provided, ntop will overrule the significance threshold.

### Value

dataframe with columns: Gene effect_size (difference between observed and expected dropout rate) p.value q.value (corrected by adjustment method specifed by the method argument)

### Examples

    	library(M3DExampleData)
    	counts <- NBumiConvertData(Mmus_example_list$data)
    	fit <- NBumiFitModel(counts);
    	Drop_features <- names(NBumiFeatureSelectionCombinedDrop(fit, qval.thresh=0.05));

[Package M3Drop version 1.16.0 Index]