R: Simulate dropout expression data

simulate_dropout2 {Corbi} | R Documentation

## Simulate dropout expression data

### Description

Generate the expression data with desired dropout rate range

### Usage

    simulate_dropout2(counts, min.rate = 0, max.rate = 0.8)

### Arguments

`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.

### Details

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.

### Value

This function will return a list with the following components:

`counts` | The modified expression matrix with the same dimension as input `counts`.
`original.counts` | The original input expression matrix.
`dropout` | The binary matrix indicating where the dropout events happen.

### References

Peter V. Kharchenko, Lev Silberstein, and David T. Scadden. Bayesian approach to single-cell differential expression analysis. Nature Methods, 11(7):740–742, 2014.

[Package Corbi version 0.6-1 Index]