Perfect Scatter Plots with Correlation and Marginal Histograms - Articles (2024)

• Prerequisites
  • Required R package
  • Demo data sets
• Basic plots
• Color by groups
• Add concentration ellipses
• Add point labels
• Bubble chart
• Color by a continuous variable
• Add marginal plots
• Add 2d density estimation
• Application to gene expression data
• Further readings

Prerequisites

if(!require(devtools)) install.packages("devtools")devtools::install_github("kassambara/ggpubr")

install.packages("ggpubr")

library(ggpubr)

# Load datadata("mtcars")df <- mtcars# Convert cyl as a grouping variabledf$cyl <- as.factor(df$cyl)# Inspect the datahead(df[, c("wt", "mpg", "cyl", "qsec")])

## wt mpg cyl qsec## Mazda RX4 2.62 21.0 6 16.5## Mazda RX4 Wag 2.88 21.0 6 17.0## Datsun 710 2.32 22.8 4 18.6## Hornet 4 Drive 3.21 21.4 6 19.4## Hornet Sportabout 3.44 18.7 8 17.0## Valiant 3.46 18.1 6 20.2

Basic plots

ggscatter(df, x = "wt", y = "mpg", add = "reg.line", # Add regression line conf.int = TRUE, # Add confidence interval add.params = list(color = "blue", fill = "lightgray") )+ stat_cor(method = "pearson", label.x = 3, label.y = 30) # Add correlation coefficient

You can change the point shape, by specifying the argument shape, for example:

ggscatter(df, x = "wt", y = "mpg", shape = 18)

To see the different point shapes commonly used in R, type this:

show_point_shapes()

Color by groups

Grouping variable: cyl. To add a correlation coefficient per group, specify the grouping variable using the mapping function aes() as follow.

ggscatter(df, x = "wt", y = "mpg", add = "reg.line", # Add regression line conf.int = TRUE, # Add confidence interval color = "cyl", palette = "jco", # Color by groups "cyl" shape = "cyl" # Change point shape by groups "cyl" )+ stat_cor(aes(color = cyl), label.x = 3) # Add correlation coefficient

# Extending the regression line --> fullrange = TRUE# Add marginal rug (marginal density) ---> rug = TRUEggscatter(df, x = "wt", y = "mpg", add = "reg.line", # Add regression line color = "cyl", palette = "jco", # Color by groups "cyl" shape = "cyl", # Change point shape by groups "cyl" fullrange = TRUE, # Extending the regression line rug = TRUE # Add marginal rug )+ stat_cor(aes(color = cyl), label.x = 3) # Add correlation coefficient

Add concentration ellipses

Main arguments:

ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", shape = "cyl", ellipse = TRUE)

# Change the ellipse type to 'convex'ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", shape = "cyl", ellipse = TRUE, ellipse.type = "convex")

# Add group mean points and starsggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", shape = "cyl", ellipse = TRUE, mean.point = TRUE, star.plot = TRUE)

Add point labels

Main arguments:

# Use row names as point labelsdf$name <- rownames(df)ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", label = "name", repel = TRUE)

# Select some labels to showggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", label = "name", repel = TRUE, label.select = c("Toyota Corolla", "Merc 280", "Duster 360"))

# Show labels according to some criteria: x and y valuesggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", label = "name", repel = TRUE, label.select = list(criteria = "`x` > 4 & `y` < 15"))

Bubble chart

In a bubble chart, points size is controlled by a continuous variable, here “qsec”. In the R code below, the argument alpha is used to control color transparency. alpha should be between 0 and 1.

ggscatter(df, x = "wt", y = "mpg", color = "cyl", palette = "jco", size = "qsec", alpha = 0.5)+ scale_size(range = c(0.5, 15)) # Adjust the range of points size

Color by a continuous variable

The R code below, will color points according to the values of a continuous variable, here “mpg”. By default, a blue gradient color is created. This can be changed using the helper function gradient_color() [in ggpubr].

# Color by continuous variablep <- ggscatter(df, x = "wt", y = "mpg", color = "mpg")p# Change gradient colorp + gradient_color(c("blue", "white", "red"))

Add marginal plots

The function ggMarginal() [in ggExtra package], can be used to easily add a marginal histogram, density or boxplot to a scatter plot.

First, install the ggExtra package as follow: install.packages(“ggExtra”); then type the following R code:

# Add density distribution as marginal plotlibrary("ggExtra")p <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", palette = "jco", size = 3, alpha = 0.6)ggMarginal(p, type = "density")# Change marginal plot typeggMarginal(p, type = "boxplot")

One limitation of ggExtra is that it can’t cope with multiple groups in the scatter plot and the marginal plots. In the R code below, we provide a solution using the cowplot package.

# Scatter plot colored by groups ("Species")sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", palette = "jco", size = 3, alpha = 0.6)+ border() # Marginal density plot of x (top panel) and y (right panel)xplot <- ggdensity(iris, "Sepal.Length", fill = "Species", palette = "jco")yplot <- ggdensity(iris, "Sepal.Width", fill = "Species", palette = "jco")+ rotate()# Cleaning the plotssp <- sp + rremove("legend")yplot <- yplot + clean_theme() + rremove("legend")xplot <- xplot + clean_theme() + rremove("legend")# Arranging the plot using cowplotlibrary(cowplot)plot_grid(xplot, NULL, sp, yplot, ncol = 2, align = "hv", rel_widths = c(2, 1), rel_heights = c(1, 2))

Add marginal boxplot:

# Scatter plot colored by groups ("Species")sp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", color = "Species", palette = "jco", size = 3, alpha = 0.6, ggtheme = theme_bw()) # Marginal boxplot of x (top panel) and y (right panel)xplot <- ggboxplot(iris, x = "Species", y = "Sepal.Length", color = "Species", fill = "Species", palette = "jco", alpha = 0.5, ggtheme = theme_bw())+ rotate()yplot <- ggboxplot(iris, x = "Species", y = "Sepal.Width", color = "Species", fill = "Species", palette = "jco", alpha = 0.5, ggtheme = theme_bw())# Cleaning the plotssp <- sp + rremove("legend")yplot <- yplot + clean_theme() + rremove("legend")xplot <- xplot + clean_theme() + rremove("legend")# Arranging the plot using cowplotlibrary(cowplot)plot_grid(xplot, NULL, sp, yplot, ncol = 2, align = "hv", rel_widths = c(2, 1), rel_heights = c(1, 2))

The problem with the above plots, is the presence of extra spaces between the main plot and the marginal density plots. Recently, in a tweet post, Claus Wilke provides the following solution for creating a perfect scatter plot with marginal density plots or histogram plots:

library(cowplot) # Main plotpmain <- ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width, color = Species))+ geom_point()+ ggpubr::color_palette("jco")# Marginal densities along x axisxdens <- axis_canvas(pmain, axis = "x")+ geom_density(data = iris, aes(x = Sepal.Length, fill = Species), alpha = 0.7, size = 0.2)+ ggpubr::fill_palette("jco")# Marginal densities along y axis# Need to set coord_flip = TRUE, if you plan to use coord_flip()ydens <- axis_canvas(pmain, axis = "y", coord_flip = TRUE)+ geom_density(data = iris, aes(x = Sepal.Width, fill = Species), alpha = 0.7, size = 0.2)+ coord_flip()+ ggpubr::fill_palette("jco")p1 <- insert_xaxis_grob(pmain, xdens, grid::unit(.2, "null"), position = "top")p2<- insert_yaxis_grob(p1, ydens, grid::unit(.2, "null"), position = "right")ggdraw(p2)

Add 2d density estimation

# Add 2d density estimationsp <- ggscatter(iris, x = "Sepal.Length", y = "Sepal.Width", color = "lightgray")sp + geom_density_2d()# Gradient colorsp + stat_density_2d(aes(fill = ..level..), geom = "polygon")# Change gradient color: customsp + stat_density_2d(aes(fill = ..level..), geom = "polygon")+ gradient_fill(c("white", "steelblue"))# Change the gradient color: RColorBrewer palettesp + stat_density_2d(aes(fill = ..level..), geom = "polygon") + gradient_fill("YlOrRd")

Application to gene expression data

We’ll use the gene expression data set described in our previous tutorial: Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data.

expr <- read.delim("https://raw.githubusercontent.com/kassambara/data/master/expr_tcga.txt", stringsAsFactors = FALSE)

The data set contains the mRNA expression for five genes of interest - GATA3, PTEN, XBP1, ESR1 and MUC1 - from 3 different data sets:

• Breast invasive carcinoma (BRCA),
• Ovarian serous cystadenocarcinoma (OV) and
• Lung squamous cell carcinoma (LUSC)

The following plots show the association between GATA3 and ESR1 genes expression.

# Association between GATA3 and ESR1# Color points by dataset# Add correlation coefficient by datasetggscatter(expr, x = "GATA3", y = "ESR1", size = 0.3, rug = TRUE, # Add marginal rug color = "dataset", palette = "jco") + stat_cor(aes(color = dataset), method = "spearman")

Facet/split by data set, add regression line and confidence interval:

ggscatter(expr, x = "GATA3", y = "ESR1", size = 0.3, color = "dataset", palette = "jco", facet.by = "dataset", #scales = "free_x", add = "reg.line", conf.int = TRUE) + stat_cor(aes(color = dataset), method = "spearman", label.y = 6)

Combining multiple plots. Visualize the correlation of GATA3 with two other genes (ESR1 and MUC1)

ggscatter(expr, x = "GATA3", y = c("ESR1", "MUC1"), size = 0.3, combine = TRUE, ylab = "Expression", color = "dataset", palette = "jco", add = "reg.line", conf.int = TRUE) + stat_cor(aes(color = dataset), method = "spearman")

Further readings

See also the ggpmisc R package to add linear model equation to a scatter plot.