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Hi I am reading this paper and trying to analyse the same datasets in my work: dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data

In this paper, the authors are working on several time course gene expression datasets, including "Drosophila melanogaster embryogenesis" dataset (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6186) . This dataset has the whole genome ( 14064 genes) of Drosophila melanogaster measured over 31 time-points.

In the dynGenie3 paper, we analyse a subset of these 14064 genes, as they write: "We focused our analysis on the 1000 genes whose expression vary the most across the time series."

I want to know how do we measure the variance in expression across time series? What statistical tests might have been performed? I couldn't find any more information in the paper so I am assuming it might be something simple and frequently done. Need some hints.

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Maybe my thoughts are too simple, but wouldn't the computation of the variance per gene suffice for this? You really do not need a statistical test, since they are not talking about DEGs, just high-variant genes.

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  • $\begingroup$ I think so too, so I just calculate the variance of each gene and pick the top 1000? The expression values are normalised. Variance will just tell how much the expression of genes differ from the mean? $\endgroup$ Mar 24 at 23:11
  • $\begingroup$ That would be my way to go: Get the genes that spread the most and label them as "most variant". A non-variant gene would just be a flat profile, hence the distance to the mean for each time point would be quite low. $\endgroup$
    – MWP
    Mar 26 at 13:44
  • $\begingroup$ Thank you. I did that now and I hope it is consistent with that they might have done. $\endgroup$ Mar 27 at 10:31
  • $\begingroup$ Hope it helps! Keeps me posted if you find that they used another method. $\endgroup$
    – MWP
    Mar 29 at 14:58
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    $\begingroup$ Actually i contacted the authors and this is what they did. Thanks a lot. $\endgroup$ Mar 31 at 13:50

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