PointScoreCalc {MCbiclust} | R Documentation

## Calculate PointScore

### Description

Using two gene sets that are represented of a known bicluster (one gene set being up regulated while other gene set is down regulated), samples are scored based on how well they match the known regulation of the bicluster.

### Usage

    PointScoreCalc(gene.expr.matrix, gene.loc1, gene.loc2)

### Arguments

`gene.expr.matrix` | Gene expression matrix with genes as rows and samples as columns
`gene.loc1` | Location of the rows containing the genes in gene set 1 within the gene expression matrix
`gene.loc2` | Location of the rows containing the genes in gene set 2 within the gene expression matrix

### Details

The PointScore of a sample can be directly compared to the PC1 value. The PointScore is typically used to identify samples related to the upper/lower fork of a bicluster without running the complete main MCbiclust pipeline on a dataset.

### Value

Vector of point scores for each sample in the gene expression matrix

### Examples

    data(CCLE_small)
    data(Mitochondrial_genes)

    mito.loc <- (row.names(CCLE_small) %in% Mitochondrial_genes)
    CCLE.mito <- CCLE_small[mito.loc,]

    set.seed(102)
    CCLE.seed <- FindSeed(gem = CCLE.mito,
                          seed.size = 10,
                          iterations = 100,
                          messages = 1000)

    CCLE.sort <- SampleSort(gem = CCLE.mito,seed = CCLE.seed,sort.length = 11)

    # Full ordering are in Vignette_sort in sysdata.rda
    CCLE.samp.sort <- MCbiclust:::Vignette_sort[[1]]

    CCLE.pc1 <- PC1VecFun(top.gem = CCLE.mito,
                          seed.sort = CCLE.samp.sort,
                          n = 10)

    CCLE.hicor.genes <- as.numeric(HclustGenesHiCor(CCLE.mito,
                                                    CCLE.seed,
                                                    cuts = 8))

    CCLE.cor.mat <- cor(t(CCLE.mito[CCLE.hicor.genes,CCLE.seed]))

    gene.set1 <- labels(as.dendrogram(hclust(dist(CCLE.cor.mat)))[[1]])
    gene.set2 <- labels(as.dendrogram(hclust(dist(CCLE.cor.mat)))[[2]])

    gene.set1.loc <- which(row.names(CCLE.mito) %in% gene.set1)
    gene.set2.loc <- which(row.names(CCLE.mito) %in% gene.set2)

    ps.vec <- PointScoreCalc(CCLE.mito,gene.set1.loc,gene.set2.loc)

    cor(ps.vec[CCLE.samp.sort], CCLE.pc1)
    plot(ps.vec[CCLE.samp.sort])
    plot(CCLE.pc1)

[Package MCbiclust version 1.6.1 Index]