棒棒糖图简介
火柴图（棒棒糖图，Stick plot）是一种用于可视化分类变量之间的关系或比较的图表类型。它通常由一系列竖直排列的线段或棒棒糖组成，每个线段代表一个分类变量，并且线段的长度表示该分类变量的频数或比例。火柴图常用于显示多个分类变量之间的关系或比较，特别是在统计分析和数据可视化中。
标签：#微生物组数据分析 #MicrobiomeStatPlot #棒棒糖图 #R语言可视化 #Lollipop chart
作者：First draft(初稿)：Defeng Bai(白德凤)；Proofreading(校对)：Ma Chuang(马闯) and Jiani Xun(荀佳妮)；Text tutorial(文字教程)：Defeng Bai(白德凤)
源代码及测试数据链接：
https://github.com/YongxinLiu/MicrobiomeStatPlot/项目中目录 3.Visualization_and_interpretation/Lollipop Chart
或公众号后台回复“MicrobiomeStatPlot”领取
参考文献：https://www.nature.com/articles/s41586-024-07291-6
中国科学院分子细胞科学卓越创新中心（分子细胞卓越中心）高栋团队联合北京大学白凡团队、分子细胞卓越中心陈洛南团队、深圳湾实验室于晨团队合作在Nature在线发表题为“Sex differences orchestrated by androgens at single-cell resolution”的研究论文。
图 4 | 雄激素调节具有性别差异的免疫分区。
结果
在这些 AASB 免疫细胞类型中，我们注意到高表达 Gata3、Areg 和 Rora 的第 2组先天性淋巴细胞（ILC2s）（图 4e）于 2010 年代18-23 被发现，是心脏、泪腺、肝脏、胰腺、唾液腺、脾脏和胃这七个组织共有的 AASB 阴性免疫细胞类型（图 4f,g）。
源代码及测试数据链接：
https://github.com/YongxinLiu/MicrobiomeStatPlot/
或公众号后台回复“MicrobiomeStatPlot”领取
软件包安装
# 基于CRAN安装R包，检测没有则安装 Installing R packages based on CRAN and installing them if they are not detectedp_list = c("ggplot2", "reshape2", "readxl", "patchwork", "dplyr", "grid","cowplot", "gridExtra", "openxlsx")for(p in p_list){if (!requireNamespace(p)){install.packages(p)}library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)}# 加载R包 Loading R packagessuppressWarnings(suppressMessages(library(ggplot2)))suppressWarnings(suppressMessages(library(reshape2)))suppressWarnings(suppressMessages(library(readxl)))suppressWarnings(suppressMessages(library(patchwork)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(grid)))suppressWarnings(suppressMessages(library(cowplot)))suppressWarnings(suppressMessages(library(gridExtra)))suppressWarnings(suppressMessages(library(openxlsx)))
实战1
参考：https://mp.weixin.qq.com/s/JNUmkkjFGbYyTtAxYIJOag
# 加载数据 Load the datadf <- data.frame(mtcars)df1 <- df[c(1:9), c(1, 2)]df1 <- cbind(Names = rownames(df1), df1)rownames(df1) <- NULL# 颜色方案 Colour schemesoptimized_colors <- c("#5ebcc2", "#46a9cb", "#5791c9", "#C77CFF", "#7a76b7", "#945893", "#9c3d62", "#946f5c", "#882100")# 绘制棒棒糖图形 Drawing Lollipop Graphicsp1 <- ggplot(df1, aes(x = Names, y = mpg)) +geom_segment(aes(x = Names, xend = Names, y = 0, yend = mpg - 0.3), color = "gray70", size = 0.6) +geom_point(aes(color = Names), size = 8, shape = 1) +geom_point(aes(color= Names),size=6)+geom_text(aes(label = round(mpg, 1), y = mpg + 0.5), hjust = 0.2, size = 3.2, color = "black") +scale_color_manual(values = optimized_colors) +scale_y_continuous(expand = c(0, 0), limits = c(0, 30)) +coord_flip() +theme_classic(base_size = 14) +theme(axis.title.y = element_blank(),axis.title.x = element_text(size = 13, face = "bold"),axis.text.x = element_text(size = 12, color = "black"),axis.text.y = element_text(size = 12, color = "black"),axis.line = element_line(color = "black", size = 0.8),axis.ticks = element_line(color = "black", size = 0.6),panel.grid.major = element_blank(),panel.grid.minor = element_blank(),legend.position = "none") +labs(y = "Miles per Gallon (mpg)")# 保存为PDF Save as PDFggsave("results/Lollipop_chart1.pdf", plot = p1, width = 6, height = 4)
实战2
组间差异棒棒糖图 Lollipop chart of differences between groups
参考：https://mp.weixin.qq.com/s/p4oHLqRKyaE6dVJUAzzm8A
# 加载数据 Load the datadf<-read.csv("data/test_otu.csv",row.names = 1)# 构建测试数据框，构建Effect_size的数据# Build a test data frame and build the data of Effect_sizeplot_data<-df[1000:1006,c(7,7)]plot_data<- as.data.frame(scale(plot_data))colnames(plot_data)<-c("Effect_size","other")# 添加group和RichnessGroup <- as.data.frame(c("Site1", "Site2", "Site3", "Site4", "Site5", "Site6", "Site7"))Richness <- as.data.frame(c(rep("Bacteria", 7)))plot_data <- cbind(plot_data, Group, Richness)colnames(plot_data) <- c("Microbes", "Error_bar", "Group", "Richness")# 绘图，geom_point添加散点，geom_segment添加直线，annotate('text')添加y轴# Drawing, geom_point adds scattered points, geom_segment adds straight lines, annotate('text') adds y-axisp2 <- ggplot(plot_data, aes(x = Group, y = Microbes, color = Group)) +#geom_point(size = 8, shape = 1, fill = "white") +geom_point(aes(color = Group), size = 8, shape = 1) +geom_point(aes(color= Group),size=6)+geom_segment(aes(x = Group, xend = Group, y = 0, yend = Microbes), size = 1, color = "gray60") +scale_color_manual(values = c("#5ebcc2", "#46a9cb", "#5791c9", "#C77CFF", "#7a76b7", "#945893", "#9c3d62")) +scale_y_continuous(expand = c(0, 0), limits = c(-2, 2)) +#theme_classic() +theme_bw()+coord_flip() +geom_hline(aes(yintercept = 0), size = 0.7, color = "gray42") +annotate("rect", xmin = 0, xmax = 7.48, ymin = -2, ymax = 2, alpha = 0.2, fill = "#94b0b2") +facet_grid(~Richness, scales = "free", space = "free_x") +annotate('text', label = '***', x = 4, y = 1.60, size = 4.5, color = "#5ebcc2") +theme(strip.background = element_rect(fill = "#FFF6E1"),strip.text = element_text(size = 12, face = 'bold', color = "#a16e53"),axis.text = element_text(color = 'black', size = 11),axis.title = element_blank())# 添加facet(真菌)# Add facet (fungus)Richness <- as.data.frame(c(rep("Fungi", 7)))plot_data <- cbind(plot_data[, 1:3], Richness)colnames(plot_data) <- c("Microbes", "Error_bar", "Group", "Richness")# 绘图，geom_point添加散点，geom_segment添加直线，annotate('text')添加y轴# Drawing, geom_point adds scattered points, geom_segment adds straight lines, annotate('text') adds y-axisp3 <- ggplot(plot_data, aes(x = Group, y = Microbes, color = Group)) +#geom_point(size = 4, shape = 16, fill = "white") +geom_point(aes(color = Group), size = 8, shape = 1) +geom_point(aes(color= Group),size=6)+geom_segment(aes(x = Group, xend = Group, y = 0, yend = Microbes), size = 1, color = "gray60") +scale_color_manual(values = c("#5ebcc2", "#46a9cb", "#5791c9", "#C77CFF", "#7a76b7", "#945893", "#9c3d62")) +scale_y_continuous(expand = c(0, 0), limits = c(-2, 2)) +#theme_classic() +theme_bw()+coord_flip() +geom_hline(aes(yintercept = 0), size = 0.7, color = "gray42") +annotate("rect", xmin = 0, xmax = 7.48, ymin = -2, ymax = 2, alpha = 0.2, fill = "#e6f4f1") +facet_grid(~Richness, scales = "free", space = "free_x") +annotate('text', label = '***', x = 4, y = 1.60, size = 4.5, color = "#5ebcc2") +theme(strip.background = element_rect(fill = "#e6f4f1"),strip.text = element_text(size = 12, face = 'bold', color = "#5ebcc2"),axis.text = element_text(color = 'black', size = 11),axis.title = element_blank())# 拼图,或者用AI组合，可以更灵活的调整大小# Puzzle, or use AI combination, you can adjust the size more flexiblyp4 <- cowplot::plot_grid(p2, p3 ,ncol= 2, rel_widths = c(1, 1), labels=LETTERS[1:2])#p4# 保存为PDF Save as pdfggsave("results/Lollipop_chart2.pdf", plot = p4, width = 10, height = 6)
实战3
双向棒棒糖图 Bidirectional Lollipop Chart
参考：https://mp.weixin.qq.com/s/j7HovQWMtlAV2FM2pFt6RQ
# 读取数据data <- read.table(file = "data/data2.txt", sep = "\t", header = TRUE, check.names = FALSE)# 将数据转换为长格式# Convert the data to long formatmelted_data <- melt(data, id.vars = c("protein"))melted_data$value <- as.numeric(as.character(melted_data$value))# 提取数据中的唯一变量名# Extract unique variable names from the dataunique_names <- unique(melted_data$protein)# 定义更新的颜色向量# Define the updated color vector# "#1E90FF", "#FF6347", "#DA70D6", "#FF1493", "#00FA9A", "#FF4500", "#6A5ACD")colors <- c("#d2da93", "#5196d5", "#00ceff", "#ff630d", "#35978b", "#e5acd7", "#77aecd","#ec8181", "#dfc6a5", "#e50719", "#d27e43", "#8a4984", "#fe5094", "#8d342e")# 创建颜色映射字典# Create a color mapping dictionarycolor_dict <- setNames(colors, unique_names)# 左图：组织1数据的条形图，y轴反向，并在柱子顶端添加圆圈# Left: Bar chart of Organization 1 data, with the y-axis reversed and circles added to the top of the barsp5 <- ggplot(dplyr::filter(melted_data, variable == "level_in_tissueA"),aes(x = factor(protein, levels = unique_names), y = value)) +geom_bar(stat = "identity", width = 0.15, aes(fill = protein), show.legend = FALSE) +geom_point(aes(y = value, size = value, fill = protein, color = protein), shape = 21) +scale_fill_manual(values = color_dict) +scale_color_manual(values = color_dict) +scale_size(range = c(3, 10)) +coord_flip() +theme_classic()+theme(axis.text.x = element_text(angle = 30, hjust = 1, size = 10),axis.text.y = element_blank(),axis.ticks = element_blank(),panel.grid = element_blank(),axis.line.y = element_blank(),axis.text = element_text(color = "black")) +labs(subtitle = "level_in_tissueA", x = NULL, y = NULL,size = "value of tissueA") +scale_y_reverse()# 右图：组织2数据的条形图，并在柱子顶端添加圆圈# Right: Bar chart of Organization 2 data with circles added at the top of the barsp6 <- ggplot(dplyr::filter(melted_data, variable == "level_in_tissueB"),aes(x = factor(protein, levels = unique_names), y = value)) +geom_bar(stat = "identity", width = 0.15, aes(fill = protein), show.legend = FALSE) +geom_point(aes(y = value, size = value, fill = protein, color = protein), shape = 21) +scale_fill_manual(values = color_dict) +scale_color_manual(values = color_dict) +scale_size(range = c(3, 10)) +coord_flip() +theme_classic()+theme(axis.text.x = element_text(angle = 30, hjust = 1, size = 10),panel.grid = element_blank(),axis.text.y = element_blank(),axis.ticks = element_blank(),axis.line.y = element_blank(),axis.text = element_text(color = "black")) +labs(subtitle = "level_in_tissueB", x = NULL, y = NULL,size = "value of tissueB")combined_plot <- p5 + p6 + plot_layout(guides = 'collect', widths = c(1, 1))# 将图形保存为PDF Save as pdfggsave("results/Lollipop_chart3.pdf", combined_plot, width = 10, height = 7)
实战4
转置棒棒糖图 Transpose the lollipop plot
参考： https://mp.weixin.qq.com/s/d9Z2f3QEmnIfD0d7E475wQProgenitor-like exhausted SPRY CD8 T cells poteniate responsiveness to neoadjuvant PD-1 blockade in esophageal squamous cellcarcinoma.https://pubmed.ncbi.nlm.nih.gov/37832554/
# 加载数据 Load datadata <- read.xlsx("data/data.xlsx",check.names = F)data$type <- factor(data$type, levels = data$type)# 设置颜色 Set colorcol_set =c("type1" = "#d2da93","type2" = "#5196d5","type3" = "#00ceff","type4" = "#ff630d","type5" = "#35978b","type6" = "#e5acd7","type7" = "#77aecd","type8" = "#ec8181","type9" = "#dfc6a5","type10" = "#e50719")# Plot# 绘图p7 <- ggplot(data,aes(x = type, y = r)) +geom_segment(aes(x = type, xend = type, y = 0, yend = r),linetype = "solid",size = 1,color = "gray40") +geom_hline(yintercept = 0,linetype = "dashed",size = 1,colour="gray40") +#geom_point(aes(color = type),color = col_set,size = 17) +geom_point(aes(color = type), color = col_set, size = 8, shape = 1) +geom_point(aes(color= type),color = col_set, size=6)+geom_text(aes(label = r), color = ifelse(data$r != 0.96, "black", 'red'),size = 5) +geom_text(aes(label = p),hjust = ifelse(data$r >= 0, 1.5, -0.5),vjust = -0.5,angle = 90,fontface = 'italic',color =ifelse(data$p != 'p<0.001', "black", 'red'), size = 5) +scale_y_continuous(limits = c(-0.5, 1.0),breaks = c(-0.5, 0, 0.5, 1.0),labels = c(-0.5, 0, 0.5, 1.0)) +labs(y = "Spearman correlation coefficient") +theme_classic() +theme(plot.title = element_text(size = 20, hjust = 0.5),axis.text.x = element_text(size = 16, angle = 45, hjust=1, face = 'bold'),axis.text.y = element_text(size = 16, face = 'bold'),axis.title.x = element_blank(),axis.title.y = element_text(size = 20, face = 'bold'),plot.margin = unit(c(1, 1, 1, 1), "cm"))#p7# 将图形保存为PDF Save as pdfggsave("results/Lollipop_chart4.pdf", p7, width = 10, height = 7)
排版Combo plots
组合多个子图为发表格式
library(cowplot)width = 89height = 59p0 = plot_grid(p1, p4, combined_plot, p7, labels = c("A", "B", "C", "D"), ncol = 2)ggsave("results/Lollipop_chart_all.pdf", p0, width = width * 5, height = height * 6, units = "mm")
使用此脚本，请引用下文：
Yong-Xin Liu, Lei Chen, Tengfei Ma, Xiaofang Li, Maosheng Zheng, Xin Zhou, Liang Chen, Xubo Qian, Jiao Xi, Hongye Lu, Huiluo Cao, Xiaoya Ma, Bian Bian, Pengfan Zhang, Jiqiu Wu, Ren-You Gan, Baolei Jia, Linyang Sun, Zhicheng Ju, Yunyun Gao, Tao Wen, Tong Chen. 2023. EasyAmplicon: An easy-to-use, open-source, reproducible, and community-based pipeline for amplicon data analysis in microbiome research. iMeta 2: e83. https://doi.org/10.1002/imt2.83
Copyright 2016-2024 Defeng Bai baidefeng@caas.cn, Chuang Ma 22720765@stu.ahau.edu.cn, Jiani Xun 15231572937@163.com, Yong-Xin Liu liuyongxin@caas.cn
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