Support for complex mixed model

[nomascus]

Hi, I'm curious if DEqMS allows for the possibility of fine tuning the linear mixed model that it runs on each protein. Basically, my problem is that I am working with samples from a wild population of animals, which introduces non-random structure to the dataset. For example, if i were just running a linear mixed model of the data (currently MaxQuant output normalized with MSstats), the structure would be something like this:

Protein_Intensity ~ Age + (1|Social_Group/Individual) + (1|Sex) + (1|Season)

In this case, I am trying to see if the age of the individual predicts protein intensity, while accounting for the fact that there are 2 samples from each individual (one in each of 2 seasons), that each individual belongs to a social group, and they can be males or female. However, I presume that just running lmer on each protein intensity value would result in some problems given the large number of NAs in the dataset, which is why I am looking for a more appropriate program.

Is this something I can modify in this step or elsewhere? design = model.matrix(~0+cond)? If not, do you happen to know of a different program that would be more suitable for this type of analysis?

Thanks!

[yafeng]

@nomascus Hi,
Yes, the design matrix is defined at this step: design = model.matrix(~0+cond)
There is a detailed guide how to create design matrix here.
Hope it can help you!

Yafeng