sSeq (version 1.10.0)

sim: Generating Simulated Data

Description

This function is used to approximate the real experiment and to generate simulated counts table based on Negative Binomial distribution.

Usage

sim(ngenes, true_mean1, conds,  alpha = function(m) {rep(0.1, length(m))},  mean_DE = 0, sd_DE = 2, s0 = NULL, s0_mean = 2, s0_sd = 3,  true_isDE_proportion = 0.3)

Arguments

ngenes

The total number of genes or rows in the simulated counts table.

true_mean1

The expected gene expression ($\mu_{g}$) in a library or a sample. The length of this vector equals `ngenes'. It can generated from either random distributions or averages of counts table from a real experiment.

conds

A vector of characters representing the two conditions (or two groups). It must be matchable to the columns in countsTable, e.g., c("A", "A", "B", "B") matches to a countsTable that has four columns (or samples) in which the first two columns are samples under condition A and the last two columns are samples under condition B.

alpha

A function used to generate the true dispersion values. The default function generates a constant 0.1 for all the genes. It can also be a function specifying the dependence between dispersion and mean.

mean_DE

A true mean value of $\epsilon$ in $\mu_{gB}=\mu_{g}/exp(\epsilon)$ where $\epsilon$ follows a Normal distribution.

sd_DE

A true standard deviation of $\epsilon$ in $\mu_{gB}=\epsilon\mu_{g}$ where $\epsilon$ follows a Normal distribution.

s0

The true size factors for samples. The length of this vector equals to the length of the vector `conds'.

s0_mean

If the true size factors for samples are not defined for `s0', then the true size factors are assumed to follow a Normal distribution with mean as the value for `s0_mean'.

s0_sd

If the true size factors for samples are not defined for `s0', then the true size factors are assumed to follow a Normal distribution with standard deviation as the value for `s0_sd'.

true_isDE_proportion

The proportion of genes that are truly different. The default value is 0.3.

Value

The function outputs a list including the simulated counts table, a vector with TRUE of FALSE values indicating the truly differentiating genes, the true mean values, the true variance values, and the true dispersion values.

References

Yu, D., Huber, W. and Vitek O. (2013). Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size. Bioinformatics.

Examples

ng = 10000;
sim1 <- sim(ngenes=ng, conds=c("A","A","B","B"),
   true_mean1=round(rexp(ng, rate=1/200)), alpha=function(m){1/(m+100)},
   mean_DE=2, sd_DE=1, s0=runif(4, 0, 2) ); 
true_isDE <- sim1$true_isDE;    
countsTable <- sim1$countsTable;