CVPlot: Make correlation vector plot

Description Usage Arguments Value Examples

Description

A function to visualise the differences between different found biclusters. Output is a matrix of plots. Each correlation vector is plotted against each other across the entire measured gene set in the lower diagonal plots, and a chosen gene set (e.g. mitochondrial) in the upper diagonal plots. The diagnal plots themselves show the density plots of the entire measured and chosen gene set. There are addition options to set the transparancy of the data points and names of the correlation vectors.

Usage

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CVPlot(cv.df, geneset.loc, geneset.name, alpha1 = 0.005, alpha2 = 0.1,
  cnames = NULL)

Arguments

cv.df

A dataframe containing the correlation vectors of one or more patterns.

geneset.loc

A gene set of interest (e.g. mitochondrial) to be plotted separately from rest of genes.

geneset.name

Name of geneset (e.g. mitochondrial genes)

alpha1

Transparency level of non-gene set genes

alpha2

Transparency level of gene set genes

cnames

Character vector containing names for the correlation vector

Value

A plot of the correlation vectors

Examples

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data(CCLE_small)
data(Mitochondrial_genes)

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

CCLE.seed <- list()
CCLE.cor.vec <- list()

for(i in 1:3){
    set.seed(i)
    CCLE.seed[[i]] <- FindSeed(gem = CCLE.mito,
                               seed.size = 10,
                               iterations = 100,
                               messages = 100)}

for(i in 1:3){
    CCLE.cor.vec[[i]] <-  CVEval(gem.part = CCLE.mito,
                                 gem.all = CCLE_small,
                                 seed = CCLE.seed[[i]],
                                splits = 10)}



CCLE.cor.df <- (as.data.frame(CCLE.cor.vec))

CVPlot(cv.df = CCLE.cor.df, geneset.loc = mito.loc,
       geneset.name = "Mitochondrial",alpha1 = 0.5)

compmedlab/MCbiclust documentation built on March 9, 2020, 12:14 a.m.