PoissonUMIFeatureSelectionDropouts: 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

	PoissonUMIFeatureSelectionDropouts(fit)
	

Arguments

fit	output from NBumiFitModel or NBumiFitBasicModel.

Details

Calculates dropout probability for each observation using depth-adjusted negative binomial means and dispersions equal to the mean (Poisson). Total dropouts per gene are modelled using the normal approximation of the sum of bernoulli variables. And significance is evaluated using a Z-test.

Value

Sorted vector of p-values

Examples

library(M3DExampleData)
counts <- as.matrix(Mmus_example_list$data);
counts <- counts[rowSums(counts) > 0,];
fit <- NBumiFitModel(counts);
Dropout_features <- names(PoissonUMIFeatureSelectionDropouts(fit)[1:2000]);

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