simulate.data: Simulation of microarray data

In penalizedSVM: Feature Selection SVM using Penalty Functions

Simulation of microarray data

Description

Simulation of 'n' samples. Each sample has 'sg' genes, only 'nsg' of them are called significant and have influence on class labels. All other '(ng - nsg)' genes are called ballanced. All gene ratios are drawn from a multivariate normal distribution. There is a posibility to create blocks of highly correlated genes.

Usage

sim.data(n = 256, ng = 1000, nsg = 100,
		 p.n.ratio = 0.5, 
		 sg.pos.factor= 1, sg.neg.factor= -1,
		 # correlation info:
		 corr = FALSE, corr.factor = 0.8,
		 # block info:
		 blocks = FALSE, n.blocks = 6, nsg.block = 1, ng.block = 5, 
		 seed = 123, ...)

Arguments

n	number of samples, logistic regression works well if n>200!
ng	number of genes
nsg	number of significant genes
p.n.ratio	ratio between positive and negative significant genes (default 0.5)
sg.pos.factor	impact factor of positive significant genes on the classifaction, default: 1
sg.neg.factor	impact factor of negative significant genes on the classifaction,default: -1
corr	are the genes correalted to each other? (default FALSE). see Details
corr.factor	correlation factorfor genes, between 0 and 1 (default 0.8)
blocks	are blocks of highly correlated genes are allowed? (default FALSE)
n.blocks	number of blocks
nsg.block	number of significant genes per block
ng.block	number of genes per block
seed	seed
...	additional argument(s)

Details

If no blockes (n.blocks=0 or blocks=FALSE) are defined and corr=TRUE create covarance matrix for all genes! with decrease of correlation : cov(i,j)=cov(j,i)= corr.factor^(i-j)

Value

x	matrix of simulated data. Genes in rows and samples in columns
y	named vector of class labels
seed	seed

Author(s)

Wiebke Werft, Natalia Becker

See Also

mvrnorm

Examples

my.seed<-123

# 1. simulate 20 samples, with 100 genes in each. Only the first two genes
# have an impact on the class labels.
# All genes are assumed to be i.i.d. 
train<-sim.data(n = 20, ng = 100, nsg = 3, corr=FALSE, seed=my.seed )
print(str(train)) 

# 2. change the proportion between positive and negative significant genes 
#(from 0.5 to 0.8)
train<-sim.data(n = 20, ng = 100, nsg = 10, p.n.ratio = 0.8,  seed=my.seed )
rownames(train$x)[1:15]
# [1] "pos1" "pos2" "pos3" "pos4" "pos5" "pos6" "pos7" "pos8" 
# [2] "neg1" "neg2" "bal1" "bal2" "bal3" "bal4" "bal5"

# 3. assume to have correlation for positive significant genes, 
# negative significant genes and 'balanced' genes separatly. 
train<-sim.data(n = 20, ng = 100, nsg = 10, corr=TRUE, seed=my.seed )
#cor(t(train$x[1:15,]))

# 4. add 6 blocks of 5 genes each and only one significant gene per block.
# all genes in the block are correlated with constant correlation factor
#  corr.factor=0.8 		
train<-sim.data(n = 20, ng = 100, nsg = 6, corr=TRUE, corr.factor=0.8,
			 blocks=TRUE, n.blocks=6, nsg.block=1, ng.block=5, seed=my.seed )
print(str(train)) 
# first block
#cor(t(train$x[1:5,]))
# second block
#cor(t(train$x[6:10,]))

penalizedSVM documentation built on March 31, 2023, 7:51 p.m.