NBumi_FSDrop: Dropout-based Feature Selection

In tallulandrews/M3Drop: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq

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));

tallulandrews/M3Drop documentation built on March 6, 2024, 1:49 a.m.