hi ali mottahharynia

before saying my suggestion my question is that you get specimens of mouse in same time ?? and you know genes that you study have a circadian maneer or no ??

I remember once when I asked Dr. Vaez about analyzing fluctuating data such as transcriptomics, he recommended the use of Principal Component Analysis , (some studies even apply several times PCA to their data ) to clear noise and fluctuations without meaning .

good luck

Hi Ali,

first of all, how many time points you have in your series? Depending on that, you may perform different approaches. It would be important to observe whether the oscillation pattern you have is not due to noise... classic measures such as average, median, variance, may help you as a starting point.

Bests,

Hello Ali Motahharynia

Can you please post one of your graphs to see more details?

Thank you

Hi Mohammadjavad Naghdibadi

thanks for your response,

All the mice were sacrificed at the same time, moreover, due to some evidence, they have circadian patterns however the circadian patterns were explained for 24 hours but we see these patterns on different days. We also do the PCA and the results were satisfactory. More to that based on our previous research, we described 3 main criteria to distinguish noise from biologically significant alteration and due to these criteria, it doesn't seem that these patterns can only be described by stochasticity or noise in gene expression

Dear Jesús Espinal-Enríquez

thanks for your response,

I have 7-time points with intervals of 3 days in my experiment, as I mentioned before our team described 3 main criteria to discriminate noise from biologically significant alteration. The first criterion is about the magnitude of the expression between different groups and that difference was noticeable in my study. The second criterion is about the consistency of the number of time points that one group is higher or lower than the other group and our study was also fulfilled that criterion and the third criterion was about the reproducibility of the data. Due to these criteria, it doesn't seem that the fluctuation in the expression pattern of these genes can only be described by noise in gene expression. Lately negative and positive cyclic feedback was proposed to have a significant role on the formation and robustness of these patterns, but isn't there any other explanation besides these matters? Is it possible that the circadian clock plays an important role in the formation of these patterns on different days(Although all the mice were sacrificed at the same time of the day)?

Hi Ali Javadmanesh

Here is one of my graph.

Thank you

Hi Ali Motahharynia

There are some genes with periodical expression e.g. GH1 in human would have a release almost every 4 hours, and there are some other genes with circadian pattern. Even this could be due to your treatments. I see an overall decrease, although the fluctuation is questionable. 1. you need to make sure everything in your qPCR is very precise, 2. You need to study your gene function extensively.

Good luck

Hi again, Ali Motahharynia

I think you should plot your points using error bars with replicates. Unfortunately, you can not conclude anything because you don't have replicates (at least three per time point).

Now, assuming that your points are the average of some replicates, what is exactly your question? I mean, they look similar in their evolution, but I don't know what you want to know about them. Could you be more specific in your question?

And finally, an advice for the future, please put names in your plots.

Regards,