Dear All,

I am a Biologist trying to understand the statistics of RNA-Seq data.

Given that RNA-Seq follows NB distribution with Biological replicates, as NBD accounts for overdispersion in the data, I am not sure how to ascertain it to my data.

Although I understanstood these distributions through standard books I am unable to comprehend and relate it to RNA-Seq.

Differential expression is my aim.

I have simulated data, to test and understand some open source tools like edgeR, DESeq, Cufflinks etc.

I have real data set too.

I have two conditions with four replicates each.

If I have to know whether my data fits NBD or Poisson distribution, I have to check this across replicates of each gene of each condition??

If the above point is right, how do I do it?

Should I do some goodness of fit test like Chi-sqare test or just the mean variance relationship is enough?

Thanks in advance for your valuable inputs.

Poisson distribution accounts for technical variance. NB distribution accounts for both technical and biological variance.

NB distribution is also a Poisson-gamma mixture distribution. Imagine you have a single biological sample (RNA extract) that you take aliquots out of to make technical replicates. These technical replicates will be Poisson distributed.

Now imagine you have multiple biological samples. You take multiple technical replicates out each biological replicate. You essentially now have multiple Poisson distributions for each biological replicate. The multiple Poisson distributions for each biological replicate can be described by a gamma distribution. Thus NB distribution (Poisson-gamma mixture) is used for RNA-seq.

You can also think of it as the lambda variable of the Poisson distribution is gamma distributed.