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Opposing expression profiles in the same WGCNA module

I've run WGCNA using the 'signed' approach and 18 for the soft threshold power (determined using the 'Scale independence' and 'Mean connectivity' figures as shown in the tutorial). I am confused with the output modules because many of them include genes with opposing expression profiles (negatively correlated).

I've attached an example here where I have plotted the Z-scores (row-wise) of the genes in one of the modules. You can see that the genes in the top ~2/3 are up-regulated in the right-most samples, while the bottom ~1/3 are down-regulated in those same samples. I thought the 'signed' approach should prevent such clustering from happening. Am I misunderstanding something or do I just have poor clustering?

Any other suggestions for prioritizing transcription factors for downstream experiments? I was hoping to use the 'connectivity' metrics from WGCNA for this.

enter image description here

clustering wgcna
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written by cfourps • cfourps

I thought the 'signed' approach should prevent such clustering from happening. Am I misunderstanding something or do I just have poor clustering?

This is odd, can you post the code used to generate the network?

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written by andres.firrincieli andres.firrincieli

Thanks for helping. Here's the code for building the network:

net = blockwiseModules(datExpr, power = 18, # 16 (8 for signed hybrid) recommended for signed network consisting of 20-30 samples; 14 for 30-40
                       TOMType = "signed", #"signed", #signed recommended in faqs
                       minModuleSize = 30, numericLabels = TRUE, pamRespectsDendro = FALSE, 
                       saveTOMs = TRUE, saveTOMFileBase = "TOM",
                       verbose = 3, #detectCutHeight = 0.95,#mergeCutHeight = 0.25, # 0.25 is used in tutorial for mergecutheight
                       corType = "pearson", #bicor recommended in faqs, but it gave warnings: bicor: zero MAD in variable 'x'. Pearson correlation was used for individual columns with zero (or missing) MAD
                       maxBlockSize=100000, nThreads=8, randomSeed=32786)
• 0 views • link
written by cfourps • cfourps

Maybe you forgot to set networkType = "signed"

Let me know

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written by andres.firrincieli andres.firrincieli

Oh! Sorry I must've confused the 'TOMtype' and 'networkType' parameters.

Edit: I just confirmed that setting that parameter prevents negatively correlated genes from being clustered, as I was expecting. Thank you for pointing out the error!

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written by cfourps • cfourps

the networkType is for the adjacency matrix. By default is networkType = "unsigned". That might be the reason why your modules includes negatively correlated genes

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written by andres.firrincieli andres.firrincieli

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