Quote:
Originally Posted by natstreet
I'm not sure what the relationship between var and mean has to do with whether the samples are classified as biological or technical replicates. A biological replicate requires that the samples are independent. Extracting a second sample from the same individual will allow you to determine technical bias but it does not increase knowledge about how biologically reproducible a result it. Simon Andrews has posted some good answers in relation to this in other threads.

Thanks for the comment!
I'm just kind of thinking when we run DESeq or edgeR, both of them are assuming that different lanes follow a Negative Binomial distribution with the same "overdisperse parameter" corss all the lanes.
If there are 4 lanes Lane1... Lane4. If Lane 1 and Lane2 are pure tech reps, then the "overdisperse parameter" of Lane1 and Lane2 will be small. But If we look at Lane1 vs Lane3 vs Lane4, the "overdisperse parameter" will be much larger. Then the "same overdisperse parameter" assumption won't hold if we analyze 4 lanes together. and the estimator of the overdisperse parameter will be pretty poor. That's why we need to sumup "pure tech reps" in my understanding.
So I'm thinking if we could observe the var of Lane1 and Lane2 is the same as the var of Lane1, Lane3, Lane4, I'll treat Lane1 and Lane2 as "Notpure" technical reps since the "same overdisperse parameter" assumption holds. So I won't sum them up.
Although maybe it's hard to get a good estimator of var if we only have two lanes...
Does this make sense?
Thanks