# 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 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
# }
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