OCplus (version 1.46.0)

MAsim.smyth: Simulate two-sample microarray data

Description

These functions simulate two-sample microarray data from various different models.

Usage

MAsim(ng = 10000, n = 10, n1 = n, n2 = n, D = 1, p0 = 0.9, sigma = 1)

MAsim.var(ng = 10000, n = 10, n1 = n, n2 = n, D = 1, p0 = 0.9) 
MAsim.smyth(ng = 10000, n = 10, n1 = n, n2 = n, p0 = 0.9, d0 = 4,  s2_0 = 4, v0 = 2)
MAsim.real(xdat, grp, n, n1, n2, D = 1, p0 = 0.9, replace = TRUE)

Arguments

ng

number of genes

n, n1, n2

number of samples per group; by default balanced, except for MAsim.real.

p0

proportion of differentially expressed genes

D

effect size for differentially expressed genes, in units of the gene-specific standard deviation (sigma in MAsim).

sigma

standard deviation, constant for all genes

d0, s2_0, v0

prior parameters for effect size and variability across genes in Smyth's model, see Details.

xdat, grp

expression data and grouping variable for an existing microarray data set, as specified in EOC.

replace

logical switch indicating whether to sub-sample (replace=FALSE) or bootstrap (replace=TRUE) from the existing data. Note that the specified group-sizes have to be smaller than the real group sizes in case of sub-sampling.

Value

The functions all return a matrix with ng rows and n1+n2 columns, except for MAsim.real, where the default is to return a matrix of the same dimensions as xdat. The group membership of each column is given by its column name. The matrix has additionally the attribute DE, which is a logical vector specifying for each gene whether or not it was assumed to be differentially expressed in the simulation.

Details

MAsim simulates normal data with constant standard deviation sigma across genes and fixed effect size D; the sign of the effect is equally and randomly split between up- and down-regulation, and effects are added to the second group. MAsim.var does the same, but instead of relying on a fixed variance across genes, it simulates gene-specific variances from a standard exponential distribution.

MAsim.smyth simulates from the model suggested in Smyth (2004), using a normal error distribution. The variances are assumed to follow an inverse chisquared distribution with d0 degrees of freedom and are scaled by s2_0; consequently, large values of d0 lead to similar gene-wise variances across genes, whereas small values lead to very different variances between genes. The effect sizes for differentially expressed genes are assumed to follow a normal distribution with mean zero and variance v0 times the previously simulated gene-specific variance; consequently, large values of v0 lead to large effects in the model.

MAsim.real finally uses existing real or simulated existing data sets to generate simulated data with fixed effect sizes: for each group, the specified number of samples is sampled either with or without replacement from the columns of xdat; for each gene, the group means are subtracted from the resampled data, so that the groupwise and overall mean for each gene is zero. Then, noise from an appropriate t-distribution is added to each group to break the sum-to-zero constraint in a consistent manner. The specified effect (evenly split between up- and down-regulation) for the differentially expressed genes is again added to the second group.

References

Smyth G (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3, No. 1, Article 3

See Also

EOC

Examples

# Small examples only
sim1 = MAsim(ng=1000, n=10, p0=0.8)
sim2 = MAsim.var(ng=1000, n1=15, n2=5, p0=0.8)
sim3 = MAsim.smyth(ng=1000, n=10, p0=0.8)

# Assess FDR
eoc1 = EOC(sim1, colnames(sim1), plot=FALSE)
eoc2 = EOC(sim2, colnames(sim2), plot=FALSE)
eoc3 = EOC(sim3, colnames(sim3), plot=FALSE)

# Show
par(mfrow=c(2,2))
plot(eoc1)
plot(eoc2)
plot(eoc3)
OCshow(eoc1, eoc2, eoc3)

# The truth will make you fret
table(eoc1$FDR<0.1, attr(sim1, "DE"))