getESet: Prepare Data Set for caOmicsV Plot
In hzhanghenry/caOmicsV: Visualization of multi-dimentional cancer genomics data

Description Usage Arguments Value Author(s) Examples

This function will validate each dataset then convert them to matrix and wrap all of them in one list object.

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    getESet(sampleNames, geneNames, sampleData, heatmapData=list(), 
        categoryData=list(), binaryData=list(), summaryData=list(), 
        secondGeneNames=NULL)

`sampleNames`	character vector, sample names, must be same or exist in every data set.
`geneNames`	character vector, gene names, must be same or exist in every data set.
`sampleData`	data frame with rows for samples and columns for features.
`heatmapData`	list of data frame(s) for heatmap plot. The first column of each data frame is row names and others are numeric values. The list could be empty, or having one or more data frame in a list object. Heatmap data should be log2 values or z-scores.
`categoryData`	list of data frame(s). The first column of each data frame is row names and others are numeric values. The list could be empty, or having one or more data frame in a list object
`binaryData`	list of data frame(s). The first column of each data frame is row names and others are binary values. The listcould be none, or one or more data frame in a list object
`summaryData`	list of data frames with summary information for samples (columns) or for genes (rows). The first column is for ID following by one or more columns of summary data.
`secondGeneNames`	character vector, gene names that will be plot on right side of biomatrix plot layout

`sampleNames`	character verctor, sample names
`geneNames`	character verctor, gene names
`secondGeneNames`	character verctor, for example, miRNA names
`sampleInfo`	a data frame, sample information such as Tumor/Normal, age, diagnosis
`heatmapData`	list of data matrix(s), e.g., RNASeq read counts at gene level
`categoryData`	list of data matrix(s), such as SNP in a gene, homozygous, or heterozygous, or wildtype
`binaryData`	list of data matrix(s), e.g., mutation status of the gene
`summaryInfo`	list of data matrix(s), such as percentage of highly expressed miRNA in all samples

Henry Zhang

    data(sampleDemoData) 
    data(RNA2miRNA)
    data(RNASeqDemoData)
    data(miRNADemoData)
    data(methylDemoData)
    data(CNVDemoData)

    sampleNames <- as.character(sampleDemoData[,1])
    geneNames <- as.character(RNA2miRNA[,1])
    secondGeneNames <- as.character(RNA2miRNA[,2])

    normals <- grep("Normal", colnames(RNASeqDemoData))
    tumors  <- grep("Tumor", colnames(RNASeqDemoData))
    tumorExpr <- RNASeqDemoData[, tumors]
    normalExpr <- RNASeqDemoData[, normals]
    meanLog2Fold <- log2(rowMeans(tumorExpr/normalExpr))
    summaryData <- data.frame(geneNames, meanLog2Fold)

    eSet <- getESet(sampleNames, geneNames, sampleDemoData, 
        heatmapData=list(RNASeqDemoData, miRNADemoData), 
        categoryData=list(methylDemoData), 
        binaryData=list(CNVDemoData), 
        summaryData=list(summaryData), 
        secondGeneNames)