I'm performing RNA-seq data analysis. I want to do healthy vs disease_stage_1, Healthy vs disease_stage_2, and Healthy vs disease_stage_3. In the case of healthy, disease_stage_1, disease_stage_2, and disease_stage_3 data sets, I have 19, 7, 8, and 15 biological replicates respectively.
Does this uneven number of replicates affect the data analysis?
Should I Use an even no of datasets like for every dataset, 7 biological replicates (As the lowest number of replicates here is 7)?
The number of biological replicates required for RNA-seq data analysis depends on several factors such as biological variability, technical variability, and sequencing depth. Ideally, RNA-seq experiments should have a minimum of three biological replicates to ensure statistical power, reduce false positives, and increase the reliability of the results.
However, the number of replicates required may vary depending on the specific research question, the degree of variability within the samples, and the effect size being studied. In general, the more biological replicates you have, the more robust your analysis will be. Having more replicates allows for better estimation of variance, greater statistical power, and improved accuracy in the detection of differentially expressed genes.
In your case, having an uneven number of replicates is not ideal, but it is not necessarily a problem. Ideally, you would want to have an equal number of replicates in each group to ensure the statistical power is distributed evenly. However, this is not always possible, especially in clinical studies where patient recruitment can be challenging.
If you can't increase the number of replicates in the groups with fewer samples, one approach is to use statistical methods that account for the unequal sample sizes, such as Welch's t-test or the moderated t-test. These methods are designed to handle situations where the variance of the groups is unequal, which is often the case in RNA-seq experiments.
Hence, while having an equal number of biological replicates in each group is ideal, it is not always feasible. You can still perform your analysis with the available number of replicates and use statistical methods that account for the uneven sample sizes to ensure the reliability and accuracy of your results.
I agree with Alwala in general, less than 3 samples per group is not even worth considering. There is an app online that will help you come up with sample size and/or power given certain parameters (https://cqs-vumc.shinyapps.io/rnaseqsamplesizeweb/) as a useful estimate tool. Regarding your uneven biological replicates, check to see if the statistical method used for differential expression and library normalization can tolerate uneven sample sizes. In general IMO, 8-10 minimum is a pretty good starting point.
Article RNA-seq differential expression studies: More sequence or mo...
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