I am completing WGCNA on RNAseq data for 3 groups of patients disease responders, non-responders and controls. I am using signed network and have generated a module- trait association table with the modules. I have some modules with a positive PCC (red) and some with a negative PCC (green). I have plotted the modules of interest using Gene Significance and Module Membership and review p values and correlation.
Some of the genes within these modules come out with high -ve GS and some with high +ve GM, likewise same with MM, both high -ve and high +ve, and pathways they form looking at GO are highly plausible with the disease of interest. How do I interpret both +ve and -ve GS and +ve & -ve MM within the same module? I have read online tutorials and youtube videos from authors of programme but cannot find the answers to explain. If anyone knows where I can find this info would greatly appreciate it.
net = blockwiseModules(Cell_4, power=4, maxBlockSize = 13400, TOMType = "signed", minModuleSize=30, reassignThreshold =0, mergeCutHeight = 0.25, numericLabels = TRUE, pamRespectsDendro = FALSE, saveTOMs = TRUE, saveTOMFileBase = "PBCreponseTOMB", verbose= 3)
Your code uses "signed TOM" but an unsigned network. To construct a signed network, you should use argument
I also recommend doubling the soft thresholding power; alternatively, you can use
and keep the power as it is. The results will be very similar. The confusion between signed network and signed TOM is very common; blame the author (i.e. me) for using confusing terminology. If you want to learn more about what signed TOM does, you can read this technical report.
Thank you kindly for your reply! I will try repeating the code tonight and hopefully it will sort the problem. Thank you kindly Victoria
**UPDATE!! I checked my code and there was an error (using an R object from the unsigned network). Fixed it, and now there are no negative values. So if anyone is reading this and has a similar issue, check your code first :)
Hello! I have a similar question. I created a signed network but am noticing negative -MM associated with the module (some are quite strong at -0.7). I made sure to include networkType = "signed" in my code:
net = blockwiseModules(dat.Expr0, power = 14, TOMType = "signed", minModuleSize = 30, networkType = "signed", reassignThreshold = 0, mergeCutHeight = 0.25, numericLabels = TRUE, pamRespectsDendro = FALSE, saveTOMs = TRUE, saveTOMFileBase = "ffTOM", verbose = 3, maxBlochSize=10000)
This was my code to identify module membership: datKME=signedKME(dat.Expr0, moduleEigengenes, outputColumnName="MM.")
Please let me know why there might be genes with negative and positive module memberships. Thank you!!
Do you have negative kME in the module the gene is assigned to, or in a different module? signedKME computes kME of all genes to all modules, not just the assigned module for each gene.