I have a question regarding the appropriate correlation to use for smaller sample sizes when creating an adjacency matrix in WGCNA. I have 8 samples in my condition, and although these are not ideal numbers for WGCNA, I would like to give this a try. I was recommended using the bicor correlation instead of the default Pearson correlation, as bicor uses median values instead of using mean values when calculating the stat. This makes it more robust for smaller sample sizes.
Question 1. Will bicor correlation indeed be better for a sample size of 8?
Question 2. I'm not entirely sure how to implement this in WGCNA when creating the adjacency matrix. I've tried to add the corFnc = "bicor" argument in the adjacency() function. Please see code:
adjacency_matrix = adjacency((data), power = 12, type = "signed", corFnc = "bicor") diag(adjacency_matrix) = 0 dissTOM = 1-TOMsimilarity(adjacency_matrix, TOMType = "signed") geneTree = flashClust(as.dist(dissTOM), method = "average")
I get the following warning when I implement the adjacency() function with "bicor":
Warning message: In bicor(datExpr, use = "p") : bicor: zero MAD in variable 'x'. Pearson correlation was used for individual columns with MAD=NA.
I would appreciate any help.