<p>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.</p>

CC BY-SA 4.0
2021-03-24T23:11:57.013Z
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?
Hamda Binte Ajmal
CC BY-SA 4.0
2021-03-26T13:44:27.167Z
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.
MWP
CC BY-SA 4.0
2021-03-27T10:31:56.597Z
Thank you. I did that now and I hope it is consistent with that they might have done.
Hamda Binte Ajmal
CC BY-SA 4.0
2021-03-29T14:58:03.603Z
Hope it helps! Keeps me posted if you find that they used another method.
MWP
CC BY-SA 4.0
2021-03-31T13:50:47.473Z
Actually i contacted the authors and this is what they did. Thanks a lot.
Hamda Binte Ajmal
CC BY-SA 4.0
2021-03-24T15:43:43.543Z
2021-03-24T15:43:43.543Z
MWP
<p>Hi I am reading this paper and trying to analyse the same datasets in my work:
<strong>dynGENIE3: dynamical GENIE3 for the inference of gene networks from time series expression data</strong></p>
<p>In this paper, the authors are working on several time course gene expression datasets, including &quot;Drosophila melanogaster embryogenesis&quot; dataset (<a href="https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6186" rel="nofollow noreferrer">https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE6186</a>) . This dataset has the whole genome ( 14064 genes) of Drosophila melanogaster measured over 31 time-points.</p>
<p>In the dynGenie3 paper, we analyse a subset of these  14064 genes, as they write:
<em>&quot;We focused our analysis on the 1000 genes whose expression vary the most across the time series.&quot;</em></p>
<p>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.</p>

<gene><gene-expression>
How to find genes whose expression vary the most in time-series?
# How to find genes whose expression vary the most in time-series?

(Asked by: username_0 on 2021-03-24)

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.

**Tags:** gene, gene-expression

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## 1 answers

### Answer by username_1 on 2021-03-24. Score: 0 | Accepted answer

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.

> **Comments:**
> **username_0**: 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?  
> **username_1**: 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.  
> **username_0**: Thank you. I did that now and I hope it is consistent with that they might have done.  
> **username_1**: Hope it helps! Keeps me posted if you find that they used another method.  
> **username_0**: Actually i contacted the authors and this is what they did. Thanks a lot.  

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