Calculating TOM in blockwise WGCNA
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C A • 0
Last seen 17 months ago
University of Louisville

I am following the WGCNA tutorial on my own dataset, but I am currently stuck in section 5.

Specifically in the first section of the guide.

# Calculate topological overlap anew: this could be done more efficiently by saving the TOM 
# calculated during module detection, but let us do it again here. 
dissTOM = 1-TOMsimilarityFromExpr(datExpr, power = 6); 
# Transform dissTOM with a power to make moderately strong connections more visible in the heatmap 
plotTOM = dissTOM^7; 
# Set diagonal to NA for a nicer plot diag(plotTOM) = NA; 
# Call the plot function
TOMplot(plotTOM, geneTree, moduleColors, main = "Network heatmap plot, all genes")

I am trying to calculate dissTOM, but since my dataset is too large I calculated the modules in a block wise manner, described in section 2c.

To calculate TOM, the function appears to re-calculate adjacency. The guide itself states this will not work for modules calculated blockwise, but I am not sure how to modify the code to fit my data. How do I recalculate TOM if I used the block wise approach to create my network?

WGCNA TOM block-wise • 290 views
Entering edit mode
Last seen 12 months ago
United States

You should repeat the code for each block. The output of your blockwiseModules call will contain the information about which genes belong to which block (component blockGenes).

But, generally speaking, I don't recommend creating these plots for large data sets. They take a long time to create and their information value is, in my opinion, fairly low.


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