DCGL (version 2.1.2)

WGCNA: Identify DCGs (Differential Coexpressed Genes) based on the 'Weighted Gene Coexpression Network Analysis'

Description

A method to pick out DCGs from microarray data based on 'Weighted Gene Coexpression Network Analysis' (WGCNA) (Mason, MJ. Et al. 2009; van Nas et al. 2009 ).

Usage

WGCNA(exprs.1, exprs.2, power = 12, variant = "WGCNA")

Arguments

exprs.1

a data frame or matrix for condition A, with rows as variables (genes) and columns as samples.

exprs.2

a data frame or matrix for condition B, with rows as variables (genes) and columns as samples.

power

the thresholding parameter, an integer >1.

variant

if the variant is 'WGCNA' the original version is evoked; if it is 'DCp', the length-normalized Euclidean distance is adopted to replace the connectivity difference measure.

Value

WGCNA

score of 'WGCNA' to identify DCGs

Details

The 'weighted gene coexpression network analysis' (WGCNA) weights links with correlation coefficients and compares the sums of the correlation coefficients of a gene (Mason, et al., 2009; van Nas, et al., 2009). Correlation coefficients are firstly softly thresholded by a 'power'.

References

Mason, M.J., et al. (2009) Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells, BMC Genomics, 10, 327.

van Nas, A., Guhathakurta, D., Wang, S.S., Yehya, N., Horvath, S., Zhang, B., Ingram-Drake, L., Chaudhuri, G., Schadt, E.E., Drake, T.A., Arnold, A.P. and Lusis, A.J. (2009) Elucidating the role of gonadal hormones in sexually dimorphic gene coexpression networks, Endocrinology, 150, 1235-1249.

Examples

data(exprs)
WGCNA(exprs[1:100,1:16],exprs[1:100,17:63],power=12,variant='WGCNA')