Barplot Gradient

It is similar to the bubble chart, but on the basis of the histogram, a color gradient rectangle is used to simultaneously display the visualization of two variables.

Setup

• System Requirements: Cross-platform (Linux/MacOS/Windows)

• Programming language: R

• Dependent packages: data.table; jsonlite; ggplot2; stringr

# Install packages
if (!requireNamespace("data.table", quietly = TRUE)) {
  install.packages("data.table")
}
if (!requireNamespace("jsonlite", quietly = TRUE)) {
  install.packages("jsonlite")
}
if (!requireNamespace("ggplot2", quietly = TRUE)) {
  install.packages("ggplot2")
}
if (!requireNamespace("stringr", quietly = TRUE)) {
  install.packages("stringr")
}

# Load packages
library(data.table)
library(jsonlite)
library(ggplot2)
library(stringr)

Data Preparation

The first column is Go Term (Go language code), the second column is the number of genes, and the third column is pvalue.

# Load data
data <- data.table::fread(jsonlite::read_json("https://hiplot.cn/ui/basic/barplot-gradient/data.json")$exampleData$textarea[[1]])
data <- as.data.frame(data)

# convert data structure
data[, 1] <- str_to_sentence(str_remove(data[, 1], pattern = "\\w+:\\d+\\W"))
topnum <- 7
data <- data[1:topnum, ]
data[, 1] <- factor(data[, 1], level = rev(unique(data[, 1])))

# View data
head(data)

                               Term Count   PValue
1                   Immune response    20 9.61e-08
2     Defense response to bacterium    11 3.02e-06
3                   Cell chemotaxis     8 5.14e-06
4                     Cell adhesion    17 2.73e-05
5             Complement activation     8 3.56e-05
6 Extracellular matrix organization    11 4.23e-05

Visualization

# Barplot Gradient
p <- ggplot(data, aes(x = Term, y = Count, fill = -log10(PValue))) +
  geom_bar(stat = "identity") +
  ggtitle("GO BarPlot") +
  scale_fill_continuous(low = "#00438E", high = "#E43535") +
  scale_x_discrete(labels = function(x) {str_wrap(x, width = 65)}) +
  labs(fill = "-log10 (PValue)", y = "Term", x = "Count") +
  coord_flip() +
  theme_bw() +
  theme(text = element_text(family = "Arial"),
        plot.title = element_text(size = 12,hjust = 0.5),
        axis.title = element_text(size = 12),
        axis.text = element_text(size = 10),
        axis.text.x = element_text(angle = 0, hjust = 0.5),
        legend.position = "right",
        legend.direction = "vertical",
        legend.title = element_text(size = 10),
        legend.text = element_text(size = 10))

p

Figure 1: Barplot Gradient

As shown in the figure, blue is a low pvalue color, and red is a high pvalue color. As the pvalue increases, the color changes from blue to red. The abscissa indicates the number of genes.