This post is about Dynamic Tree Cut, the method used, together with hierarchical clustering, to identify modules (clusters) in WGCNA. To put this post in context, in WGCNA, through several steps, one constructs a variable-variable similarity matrix which is then used for clustering. (The clustering similarity is usually the Topological Overlap Matrix, TOM, but it … Continue reading Why WGCNA modules don’t always agree with the dendrogram?
Most anyone working with any kind of data will have no trouble with binary outcomes (for example, case vs. control) and with relating them to continuous variables such as gene expression profiles. Indeed, the Student t-test or simple linear regression are some of the first topics encountered in data analysis. Categorical outcomes that encode more … Continue reading Working with categorical variables