[R-sig-ME] info model glmer

[Guidantonio Malagoli Tagliazucchi]

Hi,

I have a dataset with two main groups (e.g. healthy and disease, column
"status") and inside each group other two groups (status_genomic).  I have
another column with the tissues (tissue). Columns are genomic features.

input_df<-data.frame(status=as.factor(sample(0:1,2000,replace=T)),
status_genomic=as.factor(sample(0:1,2000,replace=T)),
tissue=paste('tissue',sample(c('a','b','c','d'),2000,replace=T),sep='_'),
replicate(40, rnorm(2000)))

colnames(input_df)[-c(1:3)]<-paste(rep(paste('genomic_feature'),40),1:40,sep='_')

Rows are subjects, columns genomic features.

My aim is identifying of the genomic features (genomic_feature) that are
predictive of the status (column status) and status_genomic. For example I
would like to identify the genomic_feature X with  a relation with the
status but also with the status_genomic. From my model I would like to
exclude the effect of the tissue that is a confounder.

I thought to the following model:

my_formula<-as.formula(status ~ genomic_feature1 + genomic_feature2 +
genomic_feature3 [...] + status_genomic + (1 | tissue))


glmer(my_formula, input_df, family= binomial("logit")


I am not totally sure that this is correct (especially for status_genomic).
Do you have any suggestion who you can provide to me to develop a better
model? Sorry for this question, but i am not an expert.

Thanks in advance for the support,

Gui

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