Question: Are 2 replicates per sample sufficient for RNA-seq data analysis?

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ag1805x •

**120**wrote:I am learning to analyse RNA-seq. I sourced a data set from ENA and tried to use new tuxedo suite to identify DE genes. After analysis I find that the q values are all above 0.5. Also they are same. p-values seems good. I got 973 genes where p<0.05. The highest fold change observed is 15919921.96. Is this normal?

This is highly suspicious.

35kYou should not worry too much about the these absolutely highest FCs in NGS. These are typically outliers due to any kind of bias, be it PCR/amplification errors, alignment errors (low-complexity regions) or similar. If these FC exceed, lets say the 99th quantile, you

mighteven consider to either discard or winsorize them.11kWow, does that fold change break the World record?

33kThe million dollar question! Two are definitely not enough, but sometimes we have to deal with it and use only those. Three are usually considered a good number, but statistically speaking you'd need 20-30 ^^

2.6kI dont think so - sounds quite strange. Did you look at the p-value distribution? Lastly to answer the question usually at least 3 replicates are recommended.

1.0kYes even I do agree atleast 3 replicates are better, but the data set I am working on has only 2.

Please have a look at the p-value distribution histogram here. It seems good enough.

120There is a paper which suggests that 12 replicates per condition is actually the 'bare minimum' for sufficient statistical power, though that paper is probably still the only one to date which has ever done that many!

I'd echo everyone else and say you need a minimum of 3. Think of it like this, without a 3rd datapoint, it's impossible to know if one or the other of those points are anomalous. This is akin to fitting lines/polynomials to data on graphs, if you have 2 points, you'll always draw a straight line. Generally the order of the polynomial you can draw should always be 1 less than the number of points you have, and ideally the fewer the better (within reason!)

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