In kevinblighe/CorLevelPlot: Visualise correlation results and test significancies of these

CorLevelPlot provides a quick and colourful way to visualise statistically significant correlations between any combination of categorical and continuous variables. Moreover, the statistical significancies of these correlations are also provided.

    library(knitr)
    opts_chunk$set(tidy = FALSE, message = FALSE, warning = FALSE)

Example CorLevelPlot plots

Install and load CorLevelPlot:

    devtools::install_github("kevinblighe/CorLevelPlot")
    library(CorLevelPlot)

Example 1: WGCNA (weighted gene co-expression network analysis) simulated data:

The following code taken from Tutorial for the WGCNA package for R - 1. Simulation of expression and trait data

    # simulate trait-to-eigengene data
    no.obs <- 50
    ESturquoise <- 0; ESbrown <- -0.6; ESgreen <- 0.6; ESyellow <- 0
    ESvector <- c(ESturquoise, ESbrown, ESgreen, ESyellow)
    nGenes1 <- 3000
    simulateProportions1 <- c(0.2, 0.15, 0.08, 0.06, 0.04)
    set.seed(1)
    MEgreen <- rnorm(no.obs)
    scaledy <- MEgreen * ESgreen + sqrt(1 - ESgreen ^ 2) * rnorm(no.obs)
    y <- ifelse( scaledy > median(scaledy), 2, 1)
    MEturquoise <- ESturquoise * scaledy + sqrt(1 - ESturquoise ^ 2) * rnorm(no.obs)
    MEblue <- 0.6 * MEturquoise + sqrt(1 - 0.6 ^ 2) * rnorm(no.obs)
    MEbrown <- ESbrown * scaledy + sqrt(1 - ESbrown ^ 2) * rnorm(no.obs)
    MEyellow <- ESyellow * scaledy + sqrt(1 - ESyellow ^ 2) * rnorm(no.obs)
    ModuleEigengeneNetwork1 <- data.frame(y, MEturquoise, MEblue, MEbrown, MEgreen, MEyellow)

    CorLevelPlot(data = ModuleEigengeneNetwork1,
        x = c("y", "MEturquoise", "MEblue", "MEbrown", "MEgreen", "MEyellow"),
        y = c("y", "MEturquoise", "MEblue", "MEbrown", "MEgreen", "MEyellow"),
        titleX = "X correlates",
        cexTitleX = 3.0,
        rotTitleX = 0,
        colTitleX = "forestgreen",
        fontTitleX = 2,
        titleY = "Y\ncorrelates",
        cexTitleY = 4.0,
        rotTitleY = 100,
        colTitleY = "gold",
        fontTitleY = 4,
        cexLabX = 1.0,
        rotLabX = 45,
        colLabX = "grey20",
        fontLabX = 1,
        cexLabY = 1.0,
        rotLabY = 30,
        colLabY = "royalblue",
        fontLabY = 1,
        posLab = "bottomleft",
        col = c("blue4", "blue3", "blue2", "blue1", "white", "red1", "red2", "red3", "red4"),
        posColKey = "right",
        cexLabColKey = 1.2,
        cexCorval = 1.0,
        fontCorval = 4,
        main = "WGCNA example",
        scale = FALSE,
        cexMain = 2,
        rotMain = 360,
        colMain = "red4",
        fontMain = 4,
        corFUN = "pearson",
        corUSE = "pairwise.complete.obs",
        signifSymbols = c("***", "**", "*", ""),
        signifCutpoints = c(0, 0.001, 0.01, 0.05, 1),
        colFrame = "white",
        plotRsquared = FALSE)

Example 2: Iris dataset principal components analysis:

    library(datasets)
    data(iris)

    # order the categories in the 'Species' column
    # CorLevelPlot will conver these to 1, 2, 3, ...
    iris$Species <- as.numeric(factor(iris$Species, levels=c("setosa", "versicolor", "virginica")))

    i <- CorLevelPlot(data = iris,
        x = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"),
        y = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"),
        col = c("white", "cornsilk1", "yellow", "gold", "forestgreen", "darkgreen"),
        cexCorval = 1.2,
        fontCorval = 2,
        posLab = "all",
        rotLabX = 45,
        scale = TRUE,
        main = bquote(Iris~r^2~correlates),
        plotRsquared = TRUE)

    pca <- stats::prcomp(iris[,c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width")])
    df <- data.frame(pca$x, iris)

    ii <- CorLevelPlot(data = df,
        x = c("PC1", "PC2", "PC3", "PC4"),
        y = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species"),
        cexTitleX = 2.0,
        rotTitleX = 0,
        fontTitleX = 2,
        titleY = "Iris components",
        cexTitleY = 2.0,
        rotTitleY = 90,
        fontTitleY = 2,
        posLab = "topright",
        col = c("blue1", "skyblue", "white", "pink", "red1"),
        posColKey = "bottom",
        cexLabColKey = 1.5,
        cexCorval = 1.5,
        fontCorval = 2,
        rotLabX = 45,
        scale = TRUE,
        main = "Iris PC correlates",
        colFrame = "white",
        plotRsquared = FALSE)

    require(rasterVis)
    require(gridExtra)
    require(grid)

    grid.arrange(
        arrangeGrob(i,
            top = textGrob("A",
            x = unit(0.05,"npc"),
            y = unit(0.9,"npc"),
            just = c("left","top"),
            gp = gpar(fontsize=32))),
        arrangeGrob(ii,
            top = textGrob("B",
            x = unit(0.05,"npc"),
            y = unit(0.9,"npc"),
            just = c("left","top"),
            gp = gpar(fontsize=32))),
        ncol = 2)

Example 3: World Health Organization (WHO) MONICA data:

    library(DAAG)
    data(monica)

    # order the categorical variables
    monica$outcome <- as.numeric(factor(monica$outcome, levels=c("dead", "live")))

    monica$diabetes[monica$diabetes=="nk"] <- NA
    monica$diabetes <- as.numeric(factor(monica$diabetes, levels=c("n", "y")))

    monica$hichol[monica$hichol=="nk"] <- NA
    monica$hichol <- as.numeric(factor(monica$hichol, levels=c("n", "y")))

    monica$stroke[monica$stroke=="nk"] <- NA
    monica$stroke <- as.numeric(factor(monica$stroke, levels=c("n", "y")))

    monica$sex <- as.numeric(factor(monica$sex, levels=c("m", "f")))

    monica$yronset <- as.numeric(factor(monica$yronset, levels=c("85","86","87","88","89","90","91","92","93")))

    monica$highbp[monica$highbp=="nk"] <- NA
    monica$highbp <- as.numeric(factor(monica$highbp, levels=c("n", "y")))

    monica$angina[monica$angina=="nk"] <- NA
    monica$angina <- as.numeric(factor(monica$angina, levels=c("n", "y")))

    monica$hosp <- as.numeric(factor(monica$hosp, levels=c("n", "y")))

    CorLevelPlot(data = monica,
        x = c("outcome", "diabetes", "highbp", "hichol", "angina", "hosp"),
        y = c("sex", "age", "yronset"),
        col = c("darkblue", "blue2", "black", "red2", "darkred"),
        cexCorval = 1.5,
        colCorval = "white",
        fontCorval = 2,
        posLab = "bottomleft",
        rotLabX = 45,
        posColKey = "top",
        cexLabColKey = 1.2,
        scale = TRUE,
        main = "World Health Organization",
        colFrame = "white",
        plotRsquared = FALSE)