NBumi_FSDrop: Dropout-based Feature Selection
In M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

Description Usage Arguments Details Value Examples

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

1 2	NBumiFeatureSelectionCombinedDrop(fit, ntop=NULL, method="fdr", qval.thresh=2, suppress.plot=TRUE)

`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

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.

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)

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