Hello STARs,

I am performing WGCNA.

This is code to figure out modules:

netwk <- blockwiseModules(input_mat, # <= input here

                      # == Adjacency Function ==
                      power = picked_power,                # <= power here
                      networkType = "signed",

                      # == Tree and Block Options ==
                      deepSplit = 2,
                      pamRespectsDendro = F,
                      # detectCutHeight = 0.75,
                      minModuleSize = 30,
                      maxBlockSize = 11000,

                      # == Module Adjustments ==
                      reassignThreshold = 0,
                      mergeCutHeight = 0.25,

                      # == TOM == Archive the run results in TOM file (saves time)
                      saveTOMs = T,
                      saveTOMFileBase = "ER",

                      # == Output Options
                      numericLabels = T,
                      verbose = 3)

input_mat is the transpose of my expression matrix; row.names is my treatment name (i.e. 8 treatments) and column. names are geneid.

I am gogooling how to calculate intramodularConnectivity. And, I am trying this code:

intramodularConnectivity.fromExpr(input_mat, mergedColors, networkType = "signed", power = 6, scaleByMax = FALSE, ignoreColors = if (is.numeric(netwk$colors)) 0 else "grey", getWholeNetworkConnectivity = TRUE)

in which, mergedColors are like chr [1:9714] "turquoise", "blue", "blue" ``````````````;; here is question, should I put netwk$colors here? mergedColors = labels2colors(netwk$colors) saved as like this eariler.

Anyway, after running this code, I can see the four columns: kTotal, kWithin, kOut, and Kdiff... in this factors, should I consider kWithin to figure hub genes?

Together, I would like to know my code for intramoduclaConnectivity.. is correct or not.

Thank you.