simData {optBiomarker} | R Documentation |
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.
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)
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. |
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.
A dataframe of dimension nTrain
by nBiom+1
. The first
column is a factor (class
) representing the group memberships of
the samples.
Mizanur Khondoker, Till Bachmann, Peter Ghazal
Maintainer: Mizanur Khondoker mizanur.khondoker@googlemail.com.
simData(nTrain=10,nBiom=3)