In FAQ's of WGCNA package it's mentioned "We do not recommend attempting WGCNA on a data set consisting of fewer than 15 samples". What could be an alternate approach if one have let's say 6 samples? Can anybody explain if for example I want to analyze this gene expression data then from where will I start?
You can make an easy co-expression network by simply constructing the symmetric correlation matrix of all genes (adjacency matrix as its called in WGCNA) then filtering that matrix by a hard threshold such as r = 0.70.
Although, based on your newest comment I am not sure why differential expression analysis is not sufficient. Couldn't they be ranked on highest absolute fold change or lowest FDR adjusted p-value?
Aren't those attempting to answer two different questions?
With differential analysis, we're looking at how gene expression changes between conditions.
In making a correlation matrix, is this not a means of realizing relationships between genes, regardless of fold change across experimental conditions?
I'm asking as a novice.
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version 2.3.6
What is your actual goal?
Co-expression network analysis.