test.class.genes: Test for differential connectivity of a class of genes
In dna: Differential Network Analysis

Description Usage Arguments Value Author(s) References Examples

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

1 2	test.class.genes(X1,X2,genelist=NULL,scores="PLS",distance="abs", num.permutations=1000,check.networks=TRUE,...)

`X1`	network 1 with genes in columns and samples in rows.
`X2`	network 2 with genes in columns and samples in rows.
`genelist`	a list of the names of the subset of genes to be considered for testing. Alternately, a numerical vector of the indices for the genes can be supplied.
`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.

results

result of test (of class resultsClassTest).

The authors are Ryan Gill, Somnath Datta, and Susmita Datta. The software is maintained by Ryan Gill rsgill01@louisville.edu.

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

# 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 all genes 
# with PLS connectivity scores and squared distances
## Not run: tcg=test.class.genes(X1,X2)
## Not run: results.tcg=get.results(tcg)
## Not run: results.tcg

# perform a test for differential connectivity of a selected group of 
# 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: our.genes=c(1,3)
## Not run: tcg2=test.class.genes(X1,X2,genelist=our.genes,scores="PLS",
## distance="sqr",num.permutations=10000,rescale.data=FALSE,
## symmetrize.scores=FALSE,rescale.scores=FALSE)
## Not run: tcg2
## 
## or, equivalently
##
## Not run: our.genes=c("G2","G4")
## Not run: tcg2=test.class.genes(X1,X2,genelist=our.genes,scores="PLS",
## distance="abs",num.permutations=10000,rescale.data=FALSE,
## symmetrize.scores=FALSE,rescale.scores=FALSE)
## Not run: tcg2

# perform a test for differential connectivity of all genes
# with PLS connectivity scores and with custom distances
## Not run: our.dist=function(score1,score2){pmin(abs(score1-score2),1)}
## Not run: tcg3=test.class.genes(X1,X2,scores=PLSnet,distance=our.dist)
## Not run: tcg3

# perform a test for differential connectivity of all genes 
# with correlation connectivity scores
## Not run: tcg4=test.class.genes(X1,X2,scores="cor")
## Not run: tcg4

# perform a test for differential connectivity of all genes 
# with principal components regression connectivity scores
## Not run: tcg5=test.class.genes(X1,X2,scores="PC")
## Not run: tcg5

# 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: tcg6=test.class.genes(X1,X2,scores="RR",rescale.scores=TRUE,
## lambda=3)
## Not run: tcg6

# 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: tcg7=test.class.genes(X1,X2,scores=ourRRnet)
## Not run: tcg7