getReadMatrix.NB: Simulate a count matrix from negative binomial mixed effect...

In KechrisLab/HeritSeq: Heritability of Gene Expression for Next-Generation Sequencing

Description

Simulate a (possibly unbalanced) count matrix from NBMM. Under NBMM, an observed number of reads aligned to feature/gene g, Y_{gsr}, follows a negative binomial (NB) distribution with mean μ_{gs} and variance μ_{gs}+φ_{g} μ_{gs}^2, where φ_g is the dispersion parameter, shared across strains. The generalized linear model uses a \log-link:
\log(μ_{gs}) = α_g+ b_{gs}, \;\;b_{gs}\sim N(0, σ^2_g).

Usage

getReadMatrix.NB(vec.num.rep, alphas, sigma2s, phis)

Arguments

vec.num.rep	A vector of replicate numbers for each strain.
alphas	Intercept vector α_g's, 1 x num.features.
sigma2s	Random effect variance vector σ^2_g's, 1 x num.features.
phis	Dispersion parameter in NB models, φ_g's, a 1 x num.features vector.

Value

A G x N matrix with NB reads. N is the total number of samples; G is the number of features. Column names are sample names of the form "Ss_r", where S stands for sample, s is the strain number, r is the replicate number within the strain. Row names are the feature names of the form "Gene g", where g is the feature index.

Examples

## Generate a sequencing dataset with 5 features and 6 strains. 
## Assign parameter values.
rep.num <- c(3, 5, 2, 3, 4, 2)
a0s <- c(-1, 1, 2, 5, 10)
sig2s <- c(10, 0.2, 0.1, 0.03, 0.01)
phis <- c(0.5, 1, 0.05, 0.01, 0.1)

set.seed(1234)
## Generate reads:
nbData <- getReadMatrix.NB(rep.num, a0s, sig2s, phis)

KechrisLab/HeritSeq documentation built on May 8, 2019, 4:48 p.m.