test.modular.structure: Test for differential modular structures

In dna: Differential Network Analysis

Description

Tests for differential modular structures between two networks using PLS scores.

Usage

test.modular.structure(X1,X2,scores="PLS",min.module.size=5,epsilon=.5,
num.permutations=1000,check.networks=TRUE,...)

Arguments

X1	network 1 with genes in columns and samples in rows.
X2	network 2 with genes as 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.
min.module.size	minimum module size parameter.
epsilon	threshold parameter.
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.

Value

results

result of test (of class resultsModTest).

Author(s)

The authors are Ryan Gill, Somnath Datta, and Susmita Datta. The software is maintained by Ryan Gill [e-mail].

References

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

Examples

# 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 modular structure using a minimum module size of 2
# and threshold of .5 with PLS connectivity scores
## Not run: tms=test.modular.structure(X1,X2,min.module.size=2)
## Not run: summary(tms)

# extract results for a test of modular structure
## Not run: results.tms=get.results(tms)
## Not run: results.tms

# perform a test for modular structure using a minimum module size of 2
# and threshold of .5 with PLS connectivity scores without rescaling the data,
# symmetrizing the scores, or rescaling the scores based on 10000 permutations
## Not run: tms2=test.modular.structure(X1,X2,scores="PLS",min.module.size=2,
## num.permutations=10000,rescale.data=FALSE,symmetrize.scores=FALSE,
## rescale.scores=FALSE)
## Not run: summary(tms2)

# perform a test for modular structure using a minimum module size of 2
# and threshold of .5 with correlation connectivity scores
## Not run: tms3=test.modular.structure(X1,X2,scores="cor",min.module.size=2)
## Not run: summary(tms3)

# perform a test for modular structure using a minimum module size of 3
# and threshold of .7 with principal components regression connectivity scores
## Not run: tms4=test.modular.structure(X1,X2,scores="PC",min.module.size=3,
## epsilon=.7)
## Not run: summary(tms4)

# perform a test for modular structure using a minimum module size of 2
# and threshold of .9 with ridge regression connectivity scores with 
# rescaled data, symmetrized and rescaled scores and a penalty parameter 
# equal to 3
## Not run: tms5=test.modular.structure(X1,X2,scores="RR",min.module.size=2,
## epsilon=.5,rescale.scores=TRUE,lambda=3)
## Not run: summary(tms5)

# perform a test for modular structure using a minimum module size of 2 and 
# threshold of .9 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: tms6=test.modular.structure(X1,X2,scores=ourRRnet,
## min.module.size=2,epsilon=.9)
## Not run: summary(tms6)

dna documentation built on July 8, 2020, 7:26 p.m.