PoissonUMIFeatureSelectionDropouts: Dropout-based Feature Selection
In tallulandrews/M3D: 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	PoissonUMIFeatureSelectionDropouts(fit)

fit

output from NBumiFitModel or NBumiFitBasicModel.

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.

Sorted vector of p-values

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