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WGCNA implementation for differential co-expression analysis

Hi everyone,

I have 10 different biological conditions and I would like to identify modules(gene sets) which are differentially regulated with respect to biological conditions. To do so,

I have calculated the fold chage for each of the conditions. Based on differential data , I have calculated gene to gene correlation (signed correlation) using wgcna and the modules have been identified. From dendrogram and heatmap, I can infer the module-module & module to trait relationship.

But, in wgcna tutorials, they have used preprocessed gene expression data (not differential data) for identifying the modules, and not differntial data. So,I would like to know that the procedure implemented ie., using differntial data to identify modules, is correct or not.

Thanking you for your time

Sathya

differential-co-expression-analysis microarray
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updated by Ram Ram • written by Sathya • Sathya

1 answer

The authors recommend against using DE data. Here is the note from the WGCNA website:

We do not recommend filtering genes by differential expression. WGCNA is designed to be an unsupervised analysis method that clusters genes based on their expression profiles. Filtering genes by differential expression will lead to a set of correlated genes that will essentially form a single (or a few highly correlated) modules. It also completely invalidates the scale-free topology assumption, so choosing soft thresholding power by scale-free topology fit will fail. "

You should be using normalized counts for all your experiments (TMM, fpkm or vst) as input.

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updated by Ram Ram • written by Liz Fernandez • Liz Fernandez

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