I have been following the WGCNA tutorial by Peter Langfelder and Steve Horvath (https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/WGCNA/Tutorials/) but with my own dataset however my final dendogram basically shows ALL the genes in my dataset are clustering together. What should I do or what parameters should affect this?
I am deciding to give an answer, as this will likely attract hits from search engines (based on your question title).
This frequently happens whereby most or all genes are assigned to a single module, and you will have to go back through each step to understand why. We cannot see any of your code, your input, or your output, so, we are not to know precisely where the issue may lie.
Some things at which to look and on which to ponder:
what is your input data? - input data should be normalised and,
preferably, transformed to log (natural or base 2) or regularised
log, or it should be variance-stabiled or converted to Z-scores
is your input data too 'flat'? - check it in histograms, boxplots,
and scatterplots. A person with OCPD (Obsessive Compulsive Personality Disorder.. different from OCD) will want a very neat dataset
with all 'lumps' removed'; however, biology never works that way. In
the act of making data too 'clean', one may inadvertently eliminate
the very signal that one wishes to detect
what is your sample n? - low sample n will be problematic
review the output of all of your WGCNA commands - do not just run the commands blindly from start to finish
ensure that you have chosen the correct soft threshold power