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Identifying Differentially Expressed Gene Combinations 177requirements for a joint differential-expression analysis using CorScor are thefollowing two objects:1. eset: either a matrix of gene-expression data, wherein columns correspond tosamples and rows correspond to genes, or an instance of the BioconductorexprSet class.2. Classlabel: either a binary factor or a binary numerical vector, describing the phenotypeof interest for each sample. If eset is an instance of the exprSet class, it canalso be the name of a binary covariate in the phenoData slot of the exprSet.The correlation scores can then be evaluated simply by the R command:corscor.output = corscor(eset, classlabel), which will generate a new object namedcorscor.output, of class corscor, described as follows.Users can also specify any of the following optional inputs:1. Annotation: an optional character vector, containing a preferred annotation for thegene names.2. Scenario: a character string, describing which scenario should be considered in thecorrelation scoring method. The two implemented options are “gapsubst” for thegap/substitution, or “shift” scenario, and “onoff” for the on/off, or “cross” scenario.3. Cor.method: a character string, describing the way in which correlations should becomputed. The default is “default,” which means that Pearson correlation is used inthe gap/substitution scenario, and Spearman correlation in the on/off scenario. Thechoice of either “Pearson” or “Spearman” overrules this default.4. Dumping: a logical, describing whether gene pairs with 10 or more exactly equalvalues of gene expression should be ruled out. This is designed to protect from majorartifacts from flooring. The default is true, but as long as the gene expression dataset is free of artifacts, this variable will not have any effect.In turn the object of class CorScor generated by the command corscor.output =corscor(eset, classlable) will include the following slots:1. Scores: a symmetrical matrix containing the CorScor values for each gene pair.a. x: a matrix containing a copy of the input gene-expression matrix.b. y: a numeric vector, which codes for the classlabels by zero and one.2. Annotation: a character vector, containing the annotation for the genes.3. Scenario: a character string, saying which scenario was used.4. Cor.method: a character string, saying which correlation method was used.5. Dumping: a logical, describing whether dumping was active or not.The package also provides functionality for follow-up analyses. The functionsprint and summary can be used to obtain an overview of the best gene pairs andtheir CorScor values. The function bestpairs yields the (column) indices of thesegenes. The function plot yields scatterplots displaying the gene pairs, and was usedto generate Figs. 1 and 2. Finally, hmap yields a heatmap-like, more generaloverview of the structure, such as shown in ref. 6.