CoExpNets

How-to generate and analyze co-expression aggregate networks

Guidance for RNA-seq co-expression network construction and analysis: safety in numbers S. Ballouz W. Verleyen J. Gillis Bioinformatics, Volume 31, Issue 13, 1 July 2015, Pages 2123–2130, https://doi.org/10.1093/bioinformatics/btv118

Basics

1. Get expression experiment

load("bin/run_GBA.Rdata")
load("data/GO.human.Rdata")
source("bin/helper_functions.r")
library(EGAD)

exprs_file = "data/GSE12946_expression_FPKM.parsed"
exprs = read.table(file=exprs_file, header=F)
geneids = exprs[,1]
exprs = exprs[,-1]
exprs = as.matrix(exprs) 
rownames(exprs) = geneids

2. Calculate network edges using correlations and rank standardize

Note, spearman correlations are recommended.

network = EGAD::build_coexp_network(exprs) 

3. Aggregation

a. Repeat on other expression experiments and aggregate

see agg_network.r

b. Other

If there aren''t enough datasets, you can hard threshold by taking the top 0.1% of connections:

network = threshold_network_top_genes(network, 0.001)

Or not ranking the matrix and then raising it to a power - also known as soft thresholding. See WGCNA: https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/faq.html

4. Test for network functionality

GBA = run_GBA(network, GO.labels)

As increasing the number of samples increases performance, aggregation is recommended.