R: Simulate differentially expressed gene data (Gaussian)

make_DEG_data {Corbi} | R Documentation

## Simulate differentially expressed gene data (Gaussian)

### Description

Generate differentially expressed gene (DEG) data from Gaussian distribution.

### Usage

    make_DEG_data(
      n.genes,
      n.samples.A,
      n.samples.B,
      exp.mean = 8,
      exp.sd = 2,
      alpha = 0.2,
      size.factor.sd = 0.1,
      ...
    )

### Arguments

`n.genes` | The total number of genes in the simulated data.
`n.samples.A` | The number of samples in the group A.
`n.samples.B` | The number of samples in the group B.
`exp.mean` | The mean of log-normal distribution that determines gene-specific expression mean.
`exp.sd` | The standard deviation of log-normal distribution that determines gene-specific expression means.
`alpha` | The dispersion ratio of gene-specific expression standard deviation to mean.
`size.factor.sd` | The standard deviation of size factors for samples.
`...` | The parameters passed to function `make_DEG_pattern`.

### Details

The expression values of each gene are assumed following a Gaussian distribution with gene-specific mean, which follows a log-normal distribution. The size factor for each sample follows a Gaussian distribution with zero mean and specific standard deviation. The heterogeneity of gene expression data is simulated by using the function `make_DEG_pattern`.

### Value

This function will return a list with the following components:

`DEG` | The matrix of simulated DEG pattern, which is generated by `make_DEG_pattern`.
`countsA` | The expression matrix of group A. Each row represents a gene and each column represents a sample.
`countsB` | The expression matrix of group B. Each row represents a gene and each column represents a sample.

[Package Corbi version 0.6-1 Index]