I am new in WGCNA. I have two RNA-seq data sets collected from two different developmental stages of maize endosperm (developmental stages can be considered as "interesting" condition). Both data sets have same format with GeneID at first column, and FPKM values for samples at rest columns.The sequencing and processing procedures are similar except that the first data set is "2X50-nucleotide paired-end reads" and the second is "2X100-nucleotide paired-end reads". Also, the variation between two data sets are very large, like the first is around 5~500-fold greater than the second for same Gene ID. My concern is whether I could combine them together to run WGCNA? or whether I need to do some adjustments before I take any action? If yes, could you give me some suggestions about how to adjust the data in order to make it valid for WGCNA. Thanks in advance.
Hi,
I have not related background about your questions. But you can ask Sanzhen Liu ([email protected]) or Junjie Fu ([email protected]). Both of them are good at Data analysis. They also work on maize and may give you some good advices.
Good luck!
In case you haven't already, it is helpful to check out the tutorials for WGCNA first - it helps knowing about the background of the method before deciding whether to pool samples or not. In this case, the authors recommend running either a consensus module analysis (which looks at each dataset separately first before comparing them), or adjusting for the categorical variable using the comBat function. You can also try both and see how the results differ.
There is also a page specifically dedicated to meta analyses of distinct datasets: https://labs.genetics.ucla.edu/horvath/htdocs/CoexpressionNetwork/JMiller/
Regarding the expression differences, those are not necessarily a problem, provided the data is not just noise (e.g. high in one sample, zero in the rest) or due to technical differences not accounted for in the separation. If these expression differences have a mainly biological basis, you can still get useful data out of modules that are highly expressed in one group and absent in the other.
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