Hi! I have measures from an epigenetic variable from two different groups (Treatment and control) measured in two different tissues. I have identified differences for this epigenetic variables between Treatment and control groups (Group) in each tissue separately, splitting my data into two subsets (one for each tissue) using the following models in R:

model_tissue_1 <- lm(Epigenetic_variable ~ Group + Age + Sex + Cell_proportion_1 + Cell_proportion_2 + Cell_proportion_3 + Cell_proportion_4, data = my_data_tissue_1)

model_tissue_2 <- lm(Epigenetic_variable ~ Group + Age + Sex + Cell_proportion_1 + Cell_proportion_2 + Cell_proportion_3 + Cell_proportion_4 + Cell_proportion_5 + Cell_proportion_6 + pH, data = my_data_tissue_2)

Please note that each model includes different cell proportions as covariates, according to the studied tissue. Also, model_tissue_2 includes an additional covariate that was not measured in the other tissue (pH).

Now, I would like to evaluate cross-tissue differences for the Epigenetic_variable among the groups, i.e., to identify Epigenetic variable differences between tissue_1 and tissue_2 for individuals in the Control group, as well as in the Treatment group, if possible considering all the covariates from each model. Is there a way to do this?

Thanks :)

Confused also...I also have the similar problem. And I hope someone can help me.

I received very useful feedback on this link https://stats.stackexchange.com/questions/479733/how-to-compare-the-same-variable-using-models-with-different-covariates?noredirect=1#comment885844_479733.

I tried two approaches: 1) I separated my data in a different way, instead of separating my data by tissue type I did it by Group, and I added tissue as a covariate. Then, I tested for possible interactions among the included covariates, for example, tissue: age, tissue: group, age: sex, etc. At the end, I did not include specific covariates for the model from each tissue (cell proportion from each tissue and pH) as I did not have such information for all the samples.

At the end, my model was:

model_treatment <- lm (Epigenetic_variable ~ Tissue + Age + Sex) model_control <- lm (Epigenetic_variable ~ Tissue + Age + Sex) And I searched for differences by tissue for each group (Treatment and control)

2) Using the whole dataset, I used a linear model recoding the original groups (treatment and control) to a new variable (Group_tissue) including the examined tissue, i.e., treatment_tissue1, treatment_tissue1, Control_tissue1, and Control_tissue2.

lm (Epigenetic_variable ~ Group_tissue + Age + Sex).

I obtained similar results with both approaches.