Sample Clustering by Accounting for Gene Fold Change and p-value

Is there a way to perform clustering analysis by taking into account both gene fold change (LFC) and its corresponding p-value. My intuition is that while performing sample clustering, especially on data from different studies in complex diseases, there are some genes that contribute greater to sample variance than others in every population cohort. To take this into account, I am thinking of using both gene FC and p-value to have a score representative of this fact. I have thought of multiplying LFC with -log10 p-value but is there a reasonable justification (Statistical or otherwise) to this?

The recently proposed Topconfect package in R is the closest to this approach using a different method of course, but is there a better justification, if any, to my proposed approach?