WGCNA - Outliers after creating sample tree
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jpwl2 • 0
Last seen 18 months ago

I am currently trying to run WGCNA for my microarray dataset. I have run QC checks and removed outliers, then I have normalised using rma() and finally when I run the code to make a sample tree I had 1 outlier. I then removed that outlier manually and run the code from the start with the samples without the outlier sample. However when I go to make the sample tree again there is always another outlier. What is happening?

I am up to the hclust() step in the WGCNA data input tutorial.

MicroarrayData WGCNA Microarray • 648 views
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Last seen 48 minutes ago
Republic of Ireland

What you are interpreting as an outlier may, in fact, not be an outlier. You have not shown any graphic or provided any other information such that we can independently check. In hierarchical clustering, there will virtually always be at least one sample that separates from a high-level branching structure into its own node - this is normal, and is not necessarily indicative of an outlier.

Please also check a PCA bi-plot of your samples, and note the percent explained variation on the axes for PC1 and PC2;

If you want, you can provide the GSE number of the study that you're analysing so that I can take a quick look at it.


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This is my own data and I have included what happens after using hclust()1 When I remove that sample that is very high manually it always shows another one that is that high again after reclustering.

I'm not sure what you mean the percent explained variation on the axes of a PCA plot.


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