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 <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/356/mathml/M6','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/356/mathml/M6">View MathML</a> 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 <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/356/mathml/M7','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/356/mathml/M7">View MathML</a> 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 <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/356/mathml/M8','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/356/mathml/M8">View MathML</a> 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.

Hudson et al.

Hudson et al. BMC Genomics 2012 13:356   doi:10.1186/1471-2164-13-356

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