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
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?
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?