Generate the expression data with desired dropout rate range
simulate_dropout2(counts, min.rate = 0, max.rate = 0.8)expression matrix where each row is a gene and each column is a sample.
the minimum dropout rate of all samples.
the maximum dropout rate of all samples.
This function will return a list with the following components:
The modified expression matrix with the same dimension as input counts.
The original input expression matrix.
The binary matrix indicating where the dropout events happen.
The dropout event is modelled by a logistic distribution such that the low expression genes have higher probability of dropout. The expression value of genes in a sample are randomly set to zero with probabilities associated with their true expression values until the desired dropout rate for that sample is meet.
Peter V. Kharchenko, Lev Silberstein, and David T. Scadden. Bayesian approach to single-cell differential expression analysis. Nature Methods, 11(7):740<U+2013>742, 2014.