Hi. The most relevant approach for me is the one explained in Resources for gene signature creation (continuous outcome).

• How should I search for articles written using such a modeling approach? Perhaps you can suggest me one? Generally speaking, if you can suggest to me learning resources on that approach, that'd be great.

Some specific questions I have (and feel free to refer me to learning resources instead of answering) :

1. My continuous outcome, strictly speaking has occurred before the gene expression. I think it does not matter when performing the regression what is the response and what is the explanatory variable. I am interested in the p-value (which is the same regardless of the direction). Does it matter for publication's sake? Conceptually it is nicer for me to put the continuous phenotype as the response, despite being earlier in time.

2. After finding individual statistically significant genes , is it necessary to build a "final" model including all of the significant (after BH correction) genes? Why is it necessary?

3. Afaik, if I am interested in finding genes that react differently to the continuous phenotype between two conditions, I should add a dummy variable for the conditions, and then the interaction variable (between the gene and the condition) would tell me that (if significant). I.e. Pheno ~ Gene + Condition + Gene:Condition. Is that correct? Thanks.

To use a linear regression, obviously, the residuals of your input parameters have to follow a normal distribution.

I will perform the linear regression for the continuous variable versus each gene separately, and then look at the p-value. To assess the assumptions of the model, it seems that I need to look at the assumptions of the model for each gene separately. How can this be done for thousands of genes?