dna (version 2.1-2)

test.individual.genes: Test for differential connectivity of individual genes

Description

Tests for differential connectivity of individual genes between two networks using PLS scores.

Usage

test.individual.genes(X1,X2,scores,distance="abs",
num.permutations=1000,check.networks=TRUE,...)

Arguments

X1

network 1 with genes in columns and samples in rows.

X2

network 2 with genes in columns and samples in rows.

scores

type of connectivity score to be used. Either one of the built-in methods ("PLS", "PC", "RR", or "cor") can be used or a user-defined method can be supplied.

distance

distance function to be used. Either one of the built-in functions ("abs" or "sqr") can be used or a user-defined distance function can be supplied.

num.permutations

the number of random permutations.

check.networks

indicates whether get.common.networks is used to preprocess the networks before the test is performed.

...

additional arguments for scores or distance.

Value

results

result of test (of class resultsIndTest).

References

Gill, R., Datta, S., and Datta, S. (2010) A statistical framework for differential network analysis from microarray data. BMC Bioinformatics, 11, 95.

Examples

# NOT RUN {
# small example illustrating test procedures
set.seed(12345)
X1=rbind(
c(2.5,6.7,4.5,2.3,8.4,3.1),
c(1.2,0.7,4.0,9.1,6.6,7.1),
c(4.3,-1.2,7.5,3.8,1.0,9.3),
c(9.5,7.6,5.4,2.3,1.1,0.2))
colnames(X1)=paste("G",1:6,sep="")

X2=rbind(
c(4.5,2.4,6.8,5.6,4.5,1.2,4.5),
c(7.6,9.0,0.1,3.4,5.6,5.5,1.2),
c(8.3,4.5,7.0,1.2,4.3,3.7,6.8),
c(3.4,1.1,6.9,7.2,3.1,0.9,6.6),
c(3.4,2.2,1.3,5.5,9.8,6.7,0.6))
colnames(X2)=paste("G",8:2,sep="")

# perform a test for differential connectivity of individual genes 
# with PLS connectivity scores and squared distances
## Not run: tig=test.individual.genes(X1,X2)
## Not run: summary(tig)

# extract results for a test for differential connectivity of individual genes
## Not run: results.tig=get.results(tig)
## Not run: results.tig

# perform a test for differential connectivity of individual genes 
# with PLS connectivity scores without rescaling the data,
# symmetrizing the scores, or rescaling the scores and with squared distances
# based on 10000 permutations
## Not run: tig2=test.individual.genes(X1,X2,scores="PLS",distance="sqr",
## num.permutations=10000,rescale.data=FALSE,symmetrize.scores=FALSE,
## rescale.scores=FALSE)
## Not run: summary(tig2)

# perform a test for differential connectivity of individual genes 
# with PLS connectivity scores and with custom distances
## Not run: our.dist=function(score1,score2){pmin(abs(score1-score2),1)}
## Not run: tig3=test.individual.genes(X1,X2,scores=PLSnet,distance=our.dist)
## Not run: summary(tig3)

# perform a test for differential connectivity of individual genes 
# with correlation connectivity scores
## Not run: tig4=test.individual.genes(X1,X2,scores="cor")
## Not run: summary(tig4)

# perform a test for differential connectivity of individual genes 
# with principal components regression connectivity scores
## Not run: tig5=test.individual.genes(X1,X2,scores="PC")
## Not run: summary(tig5)

# perform a test for differential connectivity of individual genes 
# with ridge regression connectivity scores with rescaled data,
# symmetrized and rescaled scores and a penalty parameter equal to 3
## Not run: tig6=test.individual.genes(X1,X2,scores="RR",
## rescale.scores=TRUE,lambda=3)
## Not run: summary(tig6)

# perform a test for differential connectivity of individual genes  
# with custom ridge regression connectivity scores with 
# centered and rescaled data and symmetrized and rescaled scores
## Not run: ourRR=function(X,y,lambda=3){
## solve(t(X)%*%X+lambda*diag(ncol(X)))%*%t(X)%*%y}
## Not run: ourRRnet=function(X){gennet(X,f=ourRR,recenter.data=TRUE,
## rescale.data=TRUE,symmetrize.scores=TRUE,rescale.scores=TRUE)}
## Not run: tig7=test.individual.genes(X1,X2,scores=ourRRnet)
## Not run: summary(tig7)
# }