This post collects a few links to WGCNA-related material posted elsewhere on the web. First and foremost, the WGCNA page maintained by me (PL) is the place to go for WGCNA downloads, the original set of tutorials and an FAQ. Steve Horvath wrote a comprehensive book on weighted network analysis called, appropriately, Weighted Network Analysis: … Continue reading WGCNA resources on the web

# Author: Peter Langfelder

# “Blockwise” network analysis of large data

A straightforward weighted correlation network analysis of large data (tens of thousands of nodes or more) is quite memory hungry. Because the analysis uses a correlation or similarity matrix of all nodes, for n network nodes the memory requirement scales as n2. In R, one has to multiply that by 8 bytes for each (double … Continue reading “Blockwise” network analysis of large data

# Signed and signed hybrid: what’s the difference?

In a previous post I gave my recommendation to use signed rather unsigned networks. This post will describe the two slightly different formulas that WGCNA offers for building signed networks from a correlation matrix. As a quick reminder, constructing a network really means calculating its adjacency matrix aij. Elements of this matrix encode the connection … Continue reading Signed and signed hybrid: what’s the difference?

# Signed or unsigned: which network type is preferable?

How should pairs of nodes with strong negative correlations be treated in a correlation network analysis? One option is to consider them connected, just as if the correlation were positive. A network constructed in this way is an unsigned network, because the sign of the correlation does not matter. On the other hand, strongly negatively … Continue reading Signed or unsigned: which network type is preferable?