residual Squared Coefficient of Variation (rCV²) vs Distance to Median (DM) I am working with single cell RNA-seq data. I obtained the squared coefficient of variation (CV²) as a measure of gene expression variability:



I want a metric to express gene expression noise that eliminates/takes into account the observed relationship between CV² of a gene and its mean expression so I can compare this metric across genes/conditions:
 

So far I have found two metrics that try to tackle this problem:


Distance to median (DM):



  A median-based trend is fitted to the log-transformed CV² against the log-transformed mean. The DM is defined as the residual from the trend for each gene. This statistic is a measure of the relative variability of each gene, after accounting for the empirical mean-variance relationship 



residual CV² (rCV²): 



  Fit between the mean log2 expression and CV² using a gamma generalised linear model as parametrisation then computing the absolute deviation of the CV² values from the fitted CV² values.


Do you have any clue about which metric is better or if there exists any other similar metrics?
There has been a lot of work done on this problem already, particularly these two papers:

https://www.biorxiv.org/content/10.1101/576827v2

https://www.biorxiv.org/content/10.1101/574574v1