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meghapadi / ALPACA

a method to compare two networks and find changes in modular structure
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How to find the Pearson correlation between genes? #1

Open victortoledo opened 2 years ago

victortoledo commented 2 years ago

Hello fellow researchers,

I am currently using the ALPACA and CONDOR functions in R and they return me the following objects: the DWBM values, row/col names, the membership of all TFs and genes and their scores. However, I am failing to find a matrix of correlation between the genes, which would be important for me to plot the ALPACA modules. Since you reported in the original ALPACA article that it is possible to know the correlation between each pair of genes, I would like to ask for your help in this matter. How can I extract the Pearson correlation between each pair of genes in the ALPACA modules?

Thank you very much in advance. Victor

meghapadi commented 2 years ago

Victor,

We haven't included a function to provide Pearson correlations between genes. The best way to represent the ALPACA modules is the DWBM values, which you can treat as edge weights between TFs and genes. These weights represent how much stronger the regulatory interaction in your case network is, when compared with the null expectation from your control network. It is more accurate to represent the ALPACA module as a bipartite graph with edges between TFs and genes since that is the type of network you are providing as input. (If you need to compute Pearson correlations between the genes, you can do that directly from your normalized gene expression data using R base functions like cor.)

Best, Megha