Table 2.

Higher-order metrics arising from combinations of the basic measures documented in Table1

Measure	Algebra formulae	Description	Example in skeletal muscle context
Phenotype Impact Factor	$PI{F}_i=\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$2$}\right.\left(E_{i,A}+E_{i,B}\right)d{E}_i=A_{i}d{E}_i$	Average (normalized) expression of the i-th gene across the two conditions multiplied by its differential expression. In other words, PIF weights the differential expression of a given gene by its overall abundance.	MYL2 very strongly.
Regulatory Impact Factor, Option 1	$RIF{1}_i=\frac{1}{n_{\mathit{dE}}}\sum _{j=1}^{j=n_{\mathit{dE}}}\left(PI{F}_{j}d{C}_{i,j}^2\right)$	For the i-th regulator and across all the j differentially expressed genes (j = 1, …, ndE) RIF1 looks at the average PIF of the i-th regulator weighted by the squared differential co-expression between the i-th regulator and the j-th differentially expressed gene. It addresses the question: Which regulator is consistently highly differentially co-expressed with the abundant differentially expressed gene?	MSTN very strongly.
Regulatory Impact Factor, Option 2	$RIF{2}_i=\frac{1}{n_{\mathit{dE}}}\sum _{j=1}^{j=n_{\mathit{dE}}}\left[{\left(E_{j,A}{r}_A\left(i,j\right)\right)}^2-{\left(E_{j,B}{r}_B\left(i,j\right)\right)}^2\right]$	For the i-th regulator and across all the j differentially expressed genes (j = 1, …, ndE) RIF1 looks at the average change in predictive ability of the i-th regulator to predict the abundance of the j-th differentially expressed gene. It addresses the question: Which regulator has the most altered ability to predict the abundance of differentially expressed genes.	MSTN very strongly.