Hello,
I discovered that a compound that I use, which get integrated into RNA, might have an unspecific impact on mRNA stability of a gene of interest.
To confirm that, I pretreated my cells for 2h with this compound to let it be incorporated into RNA, then did a timecourse with Actinomycin D to block transcription and observe my target of interest mRNA stability through time.
After qPCR, I have a list of RQ values, all calculated using an untreated timepoint 0 control as a reference. My data are the following : DMSO, Compound, Actinomycin D, Compound + Actinomycin D at 1, 2, 3 and 4 hours + the timepoint 0 control. Experiment was performed 4 times.
Once plotted, the results give me 4 lines, each of them representing the impact of one treatment through time. To prove that my compound impacts mRNA stability, I need to prove that the line with the Compound + Actinomycin D is statistically significantly lower compared to Actionmycin D alone.
Could you help me to select the best statistical test to use for this question ?
So far, here are the other strategies I tried:
1 - I analyzed the qPCR data using each DMSO condition as a RQ reference for their respective timepoint, giving me a histogram comparing all treatments together at each timepoint and performed a two-way ANOVA on them. If I'm correct, this strategy assesses at which timepoint, the treatments are different from each other. However, I would like now to analyse the data globally, and not separately, timepoint by timepoint.
2 - I followed the data analysis section of this paper : Article mRNA Stability Assay Using Transcription Inhibition by Actin...
Which, if I understand it correctly, calculates how well each of my curves will follow a decay model to calculate a decay rate. However, this is not exactly the answer I want and moreover, Prism gave me the following answer :
One phase decay - Least squares fit
Prism has identified at least one unstable parameter.This suggests that your data may be incomplete or don’t fully describe the selected model.Because of this, confidence intervals for all parameters should be interpreted with caution.
For at least one parameter, Prism was able to find a best-fit value, but was unable to calculate a complete confidence interval. This best-fit value should be interpreted with caution.
As I think this other strategy doesn't fit my needs and my data don't seem to be adequate for it, I do not plan on doing more on this second strategy.
Thank you very much for your help.
hello Mathieu, I followed a statistical training last week.
I think one-way ANOVA is the right test for each time point, if it is significant you have to do a Tukey's test after to compare groups one by one.
If you want a global analysis, for each condition, try to mix all your data at different time-points like they were replicates.
You can do also a Mann-Withney test to compare median of each group.
Tell me if you success
Thank you very much for your answer.
I took the time to discuss with different people and to read Prism Tutorial as much as I could. And while I agree that a one-way Anova can be useful if I take each timepoint separately to confirm the impact of treatment, I don't think this strategy is sufficient if I want to take into consideration all data in one go. This is why I did a two-way ANOVA with the Tukey's test as you suggest for the RQ using the DMSO conditions as controls while checking at all timepoints altogether.
As I looked online to other papers, I found one comparing similar types of data which used a two-way ANOVA with a Bonferroni's multiple comparison test and comparing each treatment condition means. This seems to be correct to me so far as Prism indeed compared all treatment to each other.
Do you or other people think this strategy could be the good one ?
Thank you again.
- Badges
- Science topic