cornet: Correlation network

In dna: Differential Network Analysis

Description

Computes the connectivity scores for a network based on correlation.

Usage

cornet(data,rescale.scores=FALSE)

Arguments

data	microarray dataset with genes in columns and samples in rows.
rescale.scores	indicates whether PLS scores should be rescaled so that the largest score for each gene should be 1 in magnitude.

Value

cornet

a correlation matrix measuring the interactions between gene pairs.

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 using cornet without rescaled scores
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))
s=cornet(X1)
print(round(s,4))

# small example using cornet with rescaled scores
s2=cornet(X1,rescale.scores=TRUE)
print(round(s2,4))

Example output

dna 1.1-1 loaded
        Gene 1  Gene 2  Gene 3  Gene 4  Gene 5  Gene 6
Gene 1  1.0000  0.5089  0.3534 -0.6147 -0.7502 -0.6307
Gene 2  0.5089  1.0000 -0.4374 -0.6083  0.1827 -0.9764
Gene 3  0.3534 -0.4374  1.0000 -0.3977 -0.7864  0.4131
Gene 4 -0.6147 -0.6083 -0.3977  1.0000  0.2964  0.5426
Gene 5 -0.7502  0.1827 -0.7864  0.2964  1.0000 -0.0388
Gene 6 -0.6307 -0.9764  0.4131  0.5426 -0.0388  1.0000
        Gene 1  Gene 2  Gene 3  Gene 4  Gene 5  Gene 6
Gene 1  1.0000  0.5212  0.3619 -0.6295 -0.7683 -0.6460
Gene 2  0.5212  1.0000 -0.4480 -0.6230  0.1871 -1.0000
Gene 3  0.3619 -0.4480  1.0000 -0.4073 -0.8054  0.4231
Gene 4 -0.6295 -0.6230 -0.4073  1.0000  0.3036  0.5557
Gene 5 -0.7683  0.1871 -0.8054  0.3036  1.0000 -0.0398
Gene 6 -0.6460 -1.0000  0.4231  0.5557 -0.0398  1.0000

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