Question

Unable to get soft threshold power using WGCNA for RNA-seq data

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divara01 • 0

@divara01-11644

Last seen 7.5 years ago

Icahn School of Medicine at Mount Sinai

I'm trying to create an unsigned coexpression network for some RNA-seq data (number of genes = 20555, number of subjects = 379). I have already processed the data through TMM/Voom. When I tried to create the coexpression network (using the same commands as in the WGCNA tutorials), specifically the pickSoftThreshold function, my data does not reach 0.90. It actually only reaches ~0.70. My understanding of the purpose of raising my data to a beta was to remove any noise embedded in the data. However, haven't I already done this by running TMM/Voom?

I suppose I have 2 main questions:

1) Could someone explain the difference of raising RNA-seq data to a soft-thresholding beta versus normalizing RNAseq data with TMM/voom?

2) Since the data is not reaching 90% to determine a soft threshold beta, what should I do to generate a coexpression network?

Amy

rnaseq wgcna picksoftthreshold • 1.7k views

ADD COMMENT • link updated 7.5 years ago by Lluís Revilla Sancho ▴ 730 • written 7.5 years ago by divara01 • 0

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First, Voom may not be the best approach here since the point of voom is to create weights for each measurement, and WGCNA cannot use them (yet). I personally prefer the variance stabilizing transformation in DESeq2, but you could also simply transform the normalized counts using log2(x+1). See also WGCNA FAQ at https://labs.genetics.ucla.edu/horvath/CoexpressionNetwork/Rpackages/WGCNA/faq.html, item 4.

Second, I would plot a sample clustering tree and make sure there are no major branches that would indicate an overall expression driver (e.g., a batch effect). If you find one, it usually helps to adjust for it, e.g., using ComBat on the variance stabilized data. See the WGCNA FAQ, item 6 and possibly 5.

ADD REPLY • link 7.5 years ago Peter Langfelder ★ 3.0k

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Peter,

I found a a bioconductor tutorial applying WGCNA with TCGA RNA-Seq data (https://www.bioconductor.org/packages/devel/bioc/vignettes/CVE/inst/doc/WGCNA_from_TCGA_RNAseq.html) were they use Voom to normalize data and get rid of "not-varyig" genes. Should i trust their method? If not, how do you suggest to remove not-varying genes?

Thanx

ADD REPLY • link 5.7 years ago davide.carnevali • 0

score 0 · Answer 1 · 2016-10-11

The soft-threshold doesn't raise your data to that power but the correlation between genes. Which it is not corrected in TMM and voom. Voom correct the expression between samples to make them comparable, the soft-threshold makes stand out the higher correlations between genes.

I have found that there isn't really a consensus on which type of network have the RNA. Some papers use this step just as normalization without inferring anything else about the underlying network structure. You can still generate the coexpression network with lower fitting to a scale-free topology network, just be cautious about identifying the modules with hubs. The more papers reporting the structure of RNA, mRNA, miRNA, metabolites..., the better.