I am working on a very limited budget RNAseq. We have 4-5 human biopsy samples from three groups (one control an two test) . I wish to know if
1) we should pool the RNA from biological replicates and run three technical replicates ? or
2) Should we run 3 biological replicates in each group with no technical replicates ?
Which is a smarter choice 1 or 2 ?
A budget way is to pool equimolar amounts of biological replicates and run a single RNAseq with that sample. The resulting data is then the average of those three biological reps. There is some risk in that approach though because you cannot isolate anomalies.
Doubtless, 2 is a better choice. A good article on the best practices for RNAseq: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4728800/.
3 biologival replicates are a minimum.
Good luck
I recommend independent biological replicates as opposed to pooling since the technical reproducibility of these assays once optimized should be decent (r=.95). I have only once been able to publish with stats on 3 vs 3 (2 degrees of freedom). My experience is at least 5 (and often more) biological replicates per cohort are needed to have a chance to publish in a high impact journal. But it depends on the question...and the reviewers.
Hi Deepak,
I believe option 2 is the best. The reason for this is simple and clear.
If you go for 1, you loose individual information that is particular to each sample, though it may not always be important. But technical replicate won't add value unless you employ a different machine or method.
If you go for option 2, you get both biological and technical replicates, kind of a mixture of the two. You can always pool the data while doing the analysis. Therefore, the option of pooling remains valid.
Best wishes!
Rajender
Thanks everyone.
My problem being these are rare biposies and cat get more than 3 per group. With the inter individual variations we see in gene expression.. how would one sift out the noise to get the true differentials ?
With more replicates, you have no choice. In your conditions, with 3 biological replicates (not technical), some of DEG will be not detected as significantly different even if they are and inversely, few of them will be false positive. In a first approach, you should determine the main genes/pathways which are differentially expressed.
Your statistical power is low, but you will see only the big differences. You will have lots of false negatives, but you will be sure about the positive ones with limma DEA.
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