Microarrays aren't poisson processes. You aren't modeling discrete events. We can't think of it like "what is the probability of having k number of reads for a given gene". That's because microarrays are based on continuous signal intensities.

You can think of sequencing reads as success/fail trials (bernoulli -> binomial -> poisson), you can't think of continuous signal intensities that way.

Just because something has variation (actually, all real data has variation) doesn't mean it's Poisson or shot noise. Just look at any graph of the poisson distribution: the random variable is discrete numbers. Can you get continuous signal intensities to fit such a distribution? No.

Also, you can use limma for RNA-seq (see: limma-voom, which applies some special transformation so you aren't actually fitting raw count data). It works well RNA-seq. Negative binomial is only one way to model RNA-seq data for DE analysis; many packages (e.g. sleuth, limma) don't model it that way.

RNA-seq is count data (discrete), microarray is measured data (continuous). This is a pretty big difference to start with.

520From what I understanding, counting reads from RNA-Seq is like sampling reads aligned on specific gene from reads pool. It represents Poisson process where we have small p (probability) and large n (total reads). Plus we have biological variation between samples. Therefore, we got Poisson with larger variance ~ Negative Binomial distribution. For Microarray data, I imagine we intuitively have the same technical variation (Poisson) and biological variation. Would not this form NB distribution as well instead of normal distribution?

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