simData {optBiomarker}R Documentation

Simulation of microarray data

Description

The function simulates microarray data for two-group comparison with user supplied parameters such as number of biomarkers (genes or proteins), sample size, biological and experimental (technical) variation, replication, differential expression, and correlation between biomarkers.

Usage

simData(nTrain=100,
        nGr1=floor(nTrain/2),
        nBiom=50,nRep=3,
        sdW=1.0,
        sdB=1.0,rho=0,
        sigma=1,diffExpr=TRUE,
        foldMin=2,
        orderBiom=TRUE,
        baseExpr=NULL)

Arguments

nTrain Training set size,.i.e., the total number of biological samples in group 1 (nGr1) and group 2.
nGr1 Size of group 1. Defaults to floor(nTrain/2).
nBiom Number of biomarkers (genes, probes or proteins).
nRep Number of technical replications.
sdW Experimental (technical) variation (σ_e) of data in log (base 2) scale.
sdB Biological variation (σ_b) of data in log (base 2) scale.
rho Common Pearson correlation between biomarkers. To ensure positive definiteness, allowed values of rho are restricted between 0 and 0.95 inclusive.
sigma Standard deviation of the normal distribution (before truncation) where fold changes are generated from. See details.
diffExpr Logical. Should systematic difference be introduced between the data of the two groups?
foldMin Minimum value of fold changes. See details.
orderBiom Logical. Should columns (biomarkers) be arranged in order of differential expression?
baseExpr A vector of length nBiom to be used as base expressions μ. See realBiomarker for details.

Details

Differential expressions are introduced by adding zdelta to the data of group 2 where delta values are generated from a truncated normal distribution and z is randomly selected from (-1,1) to characterise up- or down-regulation.

Assuming that Y ~is~ N(μ, σ^2), and A=[a_1,a_2], a subset of -Inf <y < Inf, the conditional distribution of Y given A is called truncated normal distribution:

f(y, μ, σ)= (1/σ) phi((y-μ)/σ) / (Phi((a2-μ)/σ) - Phi((a_1-μ)/σ))

for a_1 <= y <= a_2, and 0 otherwise,

where μ is the mean of the original Normal distribution before truncation, σ is the corresponding standard deviation,a_2 is the upper truncation point, a_1 is the lower truncation point, phi(x) is the density of the standard normal distribution, and Phi(x) is the distribution function of the standard normal distribution. For simData function, we consider a_1=log_2(foldMin) and a_2=Inf. This ensures that the biomarkers are differentially expressed by a fold change of foldMin or more.

Value

A dataframe of dimension nTrain by nBiom+1. The first column is a factor (class) representing the group memberships of the samples.

Author(s)

Mizanur Khondoker, Till Bachmann, Peter Ghazal
Maintainer: Mizanur Khondoker mizanur.khondoker@googlemail.com.

See Also

classificationError

Examples


simData(nTrain=10,nBiom=3)

[Package optBiomarker version 1.0-20 Index]