I'm going to apply multiDE, an R package for detection of DEG in multiple treatment conditions, on some RNA-seq time series data... (I wanna assume each time point as a treatment condition)
Let's assume Yidg denotes the normalized read counts for sample "i", in condition "d" for gene "g".
We also assume that Yidg marginally follows the negative binomial distribution with expectation "μdg" and dispersion parameter "ϕg" (i.e., the variance of Yidg is μdg+ϕgμ2dg).
The statistical methodology behind this package is a two factorial log linear model : logμdg = μ + αd + βg + γdg = μ + αd + βg + UdVg,
where μ is the grand mean, αd is the main effect for condition d, βg is the main effect for gene g, and γdg:=UdVg is the interaction effect between gene g and condition d.
My professor has asked me to estimate the main effect for condition (α), the main effect for gene (β) and the effect of interaction between gene and condition (γ). While the package can only show "Ud" in its output...
I'm in grave need of someone to help me please find out how I can estimate those effects...
My main problem is I don't know how I can calculate μdg. Maybe if I can calculate it, then applying a regression strategy would be helpful to estimate those effects...
here it is the link to the full paper: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4917940/
If this is not an appropriate forum to ask such question and you know any statistical forum where I can ask this question, that would be nice to know...
Thanks in advance
If there is anything confusing about my question or you need more explanation, please let me know... I highly need to find the answer... How do you check if your data follows Negative Binomial (NB) distribution? How do you estimate the NB distribution's parameters? These all will help me get closer to the answer to my main question above... Thanks