simulation.indep {sdef}R Documentation

Simulate p-values for two indipendent experiments

Description

The function simulate two vectors of p-values using the procedure described in Hwang et al. for independent experiments

Usage

simulation.indep(n, GammaA = 2, GammaB = 2, epsilonM = 0,
epsilonSD = 1, r1, r2, DEfirst, DEsecond)

Arguments

n Number of features to be simulated
GammaA Parameter of the Gamma distribution
GammaB Parameter of the Gamma distribution
epsilonM Parameter of the Gaussian noise
epsilonSD Parameter of the Gaussian noise
r1 Additional experiment-specific noise
r2 Additional experiment-specific noise
DEfirst Number of DE features in the first experiment
DEsecond Number of DE features in in the second experiment

Details

Considering two experiments (k=1,2), each of them with two classes, and n genes, for each gene we simulate a true difference between the classes delta(g), drawn from a Gamma distribution with random sign. The true difference delta(g) is 0 if the gene is not differentially expressed. We then add two normal random noise components, r[k] that act as experiment specific components and epsilon(gk), that is the gene-experiment components. The former is assigned deterministically, whilst the latter is drawn from a standard Gaussian distribution. So the log fold change (FC(gk)) is the sum of all these components for each gene and experiment. We divide the n genes in three groups: genes differentially expressed only in the first experiment, genes differentially expressed only in the second experiment and genes differentially expressed in neither experiment. There are not true positive genes (i.e. truly DE in both experiments), so we should find no genes in common using our method.

Then, as described in Hwang et al., a two tails T-test is performed for each FC(gk) and a p-value is generated as: P(gk) = 2 Normal cdf(-absolute value (FC(gk)/r(k))) where FC(gk) is the t statistic that evaluates the differential expression between the two classes for the g gene and k experiment.

Value

names Which group each simulated gene expression value belongs to
FC1 T statistic for the first experiment
FC2 T statistic for the second experiment
Pval p-values for the experiment to be compared

Author(s)

Alberto Cassese, Marta Blangiardo

References

Hwang D, Rust A, Ramsey S, Smith J, Leslie D, Weston A, de Atauri P, Aitchison J, Hood L, Siegel A, Bolouri H (2005): A data integration methodology for system biology. PNAS 2005.

M.Blangiardo and S.Richardson (2007) Statistical tools for synthesizing lists of differentially expressed features in related experiments, Genome Biology, 8, R54.

Examples

data.indep = simulation.indep(n=500,GammaA=1,
GammaB=1,r1=0.5,r2=0.8,DEfirst=300,DEsecond=200)


[Package sdef version 1.5 Index]