1. As I navigate through the complexities of miRNA expression analysis using qPCR, my study involves a panel of 20 plates, each containing 96 wells—18 dedicated to patient samples and 2 for controls.
In categorizing my samples into newly diagnosed lymphoma/leukemia, those in remission, and those exhibiting resistance to treatment, I aim to calculate essential parameters such as ΔCT, ΔΔCT, and fold change. Is it appropriate to determine the average CT values for each subgroup to obtain a representative measure of miRNA expression within these distinct clinical states? keep in mind each group contain 3 samples from different individuals. Also is't acceptable to take multiple reference genes and take average of them for normalization?
Additionally, I encounter undetermined CT values; what would be the most judicious approach to handle these values? Should I assign them as 35?
Similarly, CT values exceeding 35 pose a challenge. How can I establish thresholds for further analysis in order to maintain data accuracy? because I cant delete any thing from genes as they are panel of miRNA
Moving into the statistical analysis phase, which methods, like ANOVA or t-tests, would be most effective in discerning significant differences between the categories of my samples?
Finally, in presenting my findings, how can I ensure clarity and transparency, incorporating well-organized tables and figures to visually convey the intricate dynamics of miRNA expression?
Moreover, how should I adeptly discuss the biological implications of my results while addressing potential limitations in my study?"
It's worth noting that the source of my samples is plasma, and they are derived from patients with hematological malignancies at various stages of the disease. Furthermore, each sample has been processed only once without any technical repeats.
Hi Lugien!
I also am working in measuring plasma derived miRNA levels by qPCR. I recently ran into a program to do the analysis that seems easily applicable. The program name is LinReg, and it uses the non-corrected fluorescence data of the qPCR (doi:10.1093/nar/gkp045).
Here I left the youtube video where someone explains it.
https://www.youtube.com/watch?v=hPO7ptWOT7M&t=1089s
Also, the question about the reference genes, you can use more than one but you have to do the geometrical mean, this is well explained in both of these videos: https://www.youtube.com/watch?v=EsAidz53Ktk
The statistical analysis that you would use it's going to depend on the biological question you want to answer. If you want to compare more than 2 groups, you should do an ANOVA, but if you one to compare each group against a control group for instance, you should do a t-test or Mann Whitney (depends on the data distribution).
About the undetermined CT values; I don't know how many replicates you do per sample. We usually do 3 replicates and if one patient don't give a Ct in any replicate, I would repeat it one time. If still does not give a Ct, I would discard the miRNA in that sample (maybe it has too little -or none- amount).
Hope this was helpful, sorry for my english!
Victoria Cairoli
Hello victoria, firstly thank you for your response.
I dont make any replicate and many samples in my plates have undetermined ct values and values above 35, so from this I dont know how I can deal with this to complete my calculation ?
the other thing also I have 2 control , so here also take geomean or arithmatic mean ?
You should do at least 2 replicates.. otherwise you won't know if the result you obtain is due to a mistake or is it really your sample.
I don't know how are you planning to do your calculations. If you do the ΔΔCT method you should have a reference gene (-housekeeping- you can use more than one and do the geometric mean) and a sample "control" that could be the healthy sample. But I am not sure. you should think better how you are going to express your results and then plan the runs.
However, I strongly recommend at least two replicates.
Victoria Cairoli
I acknowledge the importance of replication, but unfortunately, the funds we've secured are insufficient to address these issues. Yes, I'm interested in calculating delta delta ct and see fold change. I want to use multiple of reference genes to increase accuracy, So as I understood from you also 2 control samples take geomean of ct values ?
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