simulate_dropout2 {Corbi} | R Documentation |
Generate the expression data with desired dropout rate range
simulate_dropout2(counts, min.rate = 0, max.rate = 0.8)
counts |
expression matrix where each row is a gene and each column is a sample. |
min.rate |
the minimum dropout rate of all samples. |
max.rate |
the maximum dropout rate of all samples. |
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.
This function will return a list with the following components:
counts |
The modified expression matrix with the same dimension as input |
original.counts |
The original input expression matrix. |
dropout |
The binary matrix indicating where the dropout events happen. |
Peter V. Kharchenko, Lev Silberstein, and David T. Scadden. Bayesian approach to single-cell differential expression analysis. Nature Methods, 11(7):740–742, 2014.