Hi,
I am new in bioinformatics and have a background in neuroimaging where we often use a baseline to build our models. Meaning, all the relationships between variables (correlation or other measurements) are inferred from the difference of activity between or condition of interest and in the baseline. Although these types of differential models are gold standard in my field, I've heard that studies of differential co-expression in RNA-seq is controversed. Does anyone can explain me why (difficulty of choice of a baseline...etc) and/or point me to publications that discuss the topic?
Thank you very much!
Sandrine
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sandrine.muller.research •
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Just to throw another couple of ideas out there.
With simple correlation analyses, like generating a huge correlation matrix for all of your variables, you can also derive a P value from the correlation function (in R at least) in order to back up whatever values you obtain. Through this, you can also plot the values and identify 'structure' in your dataset, as you can see from my first figure below.
Another thing that I've recently been researching this past year has been graph theory, minimal spanning trees, and the identification of 'communities' in these. There are functions in R for this in the packages igraph and plotrix. he data is the same as per the correlation matrix. In the correlation plot below, for example, you can see 'blocks' of highly positively and negatively (inversed) correlated samples - these are akin to modules and communities in a network analysis.
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Hi! Today I was reading on co-expression networks, but it really depends on how your experiment is and what tools you have at your disposal. For example, seems like WGCNA works really good, however, seems you need quite the number of samples to run a significant analysis. On using differentially expressed genes, here is what is wrote on their FAQ:
I do not know if this is the case for all the tools, but it is definitely something to keep in mind. Cheers :)
Thank you @biofalconch for your answer! Indeed, I can understand their point. However, don't you think that you may have a lot of correlations that happen "by chance" if you are not controlling for random noise (from a baseline) ? I guess when you correlate the modules with a disease for instance, a lot of the genes in the module can be false positives... or am I having an inadequate reasonning?
Yes! It may be bad to leave the whole dataset, and the first part of the same question of the FAQ adresses this (probably shouldn't have left it out). But here it is
So what I got from this is "filter at your own risk"
WGCNA is indeed fundamentally based on correlation - that's how it initially identifies modules. Once identified, it then transforms the module by single value decomposition (i.e. PCA) in order to derive the loadings for each gene to each module. WGCNA is really great in certain situations.