microRNA network analysis
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4.6 years ago
lessismore ★ 1.2k

Hey all,

i have a list of microRNA (almost 300) for which i have raw counts in control and a stress condition for 2 genotypes. I was thinking to use the absolute values (after normalization) for a network construction for each genotype in the 2 conditions (stress and control) and compare these networks in order to find conserved modules between the genotypes.

Furthermore i would like to insert in some way the data i have from a target prediction analysis. But i have no RNA-seq data for the mRNA.

Any ideas or suggestion for facing these steps? Every tip (papers, softwares, guidelines) would be much appreciated

miRNA Network analysis Visualization • 1.4k views
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4.6 years ago

Hey / Buenos días, it looks like you have interesting data.

My first question is why you are thinking about using absolute values?

Based on your experimental set-up with with genotypes, I recommend CGBayesNets by my colleague Mike: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1003676 It's implemented in MATLAB, but it does specifically allow you to see how a network varies by things like genotype.

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Hola Kevin, thanks for your answer.

My first question is why you are thinking about using absolute values?

Ive been reading that you can use either absolute values (in my case TPM) or log2 values of the fold change (Control/Stress). So you think i could make 2 trials on that?

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Hey, what do you mean by 2 trials?

I would be very interested in seeing how the network changes in the stress condition based on the 2 genotypes. You could build 2 different networks in order to do this, and them compare them (for example, compare community structures - 'communities' are akin to modules in WGCNA).

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My question is: would you use your TPM or the log2 of the fold change control/stress?

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For the network construction, I would use the TPM values. It should be possible to then highlight any of your genes that were significant to see in which modules they group in the network plot.

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...but there is no 'right' or 'wrong' here - you can also use the fold changes.

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