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.