View - ResearchGate

Identifying Differentially Expressed Gene Combinations 187Table 1A Synopsis of the Key Features of the MethodsJoint differen-Effect of largetial expression Multidimensi- increase More than Continuouspattern onal extension in marginal two pheno- mediatorMethod targeted available separation typic classes variableS cross“cross” No None No NoS shift“shift” No Depends on Yes NodataENT “cross” and Yes Decreases Yes No“shift”LA (PLA) “cross” Yes None Yes YesECF “cross” and No Increase Yes No“shift”class 2 data is rotated with a degree θ counterclockwise, is not monotone in θfor a fixed ρ. Rather the value of ECF y|x increases with θ except for a dip whenθ is such that the data of class 2 is vertically aligned. This results in the nonmonotonepattern of the power function of Fig. 7. This is owing to the fact thatthe ECF y|xcalculation is weighted by the marginal distribution of x, whichresults in narrowing the comparison between classes to a region of relativelysmall class effect.3.3. ConclusionSeveral statistical approaches are now available for identifying joint differentialexpression. In this chapter a definition of joint differential expression isproposed, several approaches are reviewed, and a simulation is used to comparethe three methods that can at present be used for exhaustive searches of all pairsor all triples of genes in realistically large gene sets. A compendium of propertiesof different methods is presented in Table 1. Overall, no method appears tobe uniformly superior. However, in the two-gene analyses investigated in thesimulation, the performance of the ECF-statistic is consistently reliable. TheCorScor approach maintains an intuitive interpretability and is by far the mostattractive computationally. The entropy scoring approach shows promise forcomprehensive searches of three-gene sets.4. Notes4.1. Other ApproachesIn briefly this section, a few other distinctive approaches are described. Forfull details, the reader is referred to original sources. Methods described in