I have been going through a set of tutorials on WGCNA for building weighted gene co-expression networks. One particular tutorial uses Yeast expression data collected over 44 time points across cell cycles. To create an adjacency matrix, a Pearson correlation is calculated for each pair of genes using expression over time. However, as I understand it, a Pearson correlation is not appropriate for time series data because the data are correlated over time periods. I just wonder if anyone has an explanation for why it would be appropriate to use a Pearson correlation with Yeast time series data. I would like to use WGCNA to analyze time series data in which expression values are obtained at multiple time points in the same patient, and I need to be able to justify this. Thanks for your thoughts on the matter.
I am assuming that you are referring to the Steve Horvath's Yeast tutorial. In this tutorial, Pearson's correlation is run on the different eigengenes (datME) and not directly on gene expression over time. You obtain correlation between the various module eigengenes and cluster based on that.