caOmicsV (version 1.2.0)

getPlotDataSet: Prepare Data Set for caOmicsV Plot

Description

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

Usage

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

Arguments

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

Value

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

Examples

    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)

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