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在微生物多样性分析中，STAMP(Statistical analysis of megagenomic profiles)分析常常用来比较多组样本之间物种或功能相对丰度的差异情况，能够得到组间具有显著差异的物种和功能。STAMP分析通过扩展柱状图(Extended error bar)的形式来展示最终的结果。 也可以用STAMP软件完成功能更多的差异分析，网站地址为https://beikolab.cs./software/STAMP，软件的使用可参考https://mp.weixin.qq.com/s/1EFYt2KJOIx_zmT5Xltkeg; https://mp.weixin.qq.com/s/2zSvLgcJ2pGv7MyMLKaICw 标签：#微生物组数据分析 #MicrobiomeStatPlot #R语言可视化 #STAMP 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/STAMP_R 或公众号后台回复“MicrobiomeStatPlot”领取 这是来自于温州医科大学附属医院Chen Gang团队和温州医科大学Wang Yi团队2023年发表于Gut Microbes上的一篇论文，论文题目为：A distinct microbiota signature precedes the clinical diagnosis of hepatocellular carcinoma. https:///10.1080/19490976.2023.2201159 图 5 | 前瞻性队列中 HCC 组和对照组之间的主要 KEGG 通路与粪便样本的宏基因组测序数据。 Diamond 软件检测到两组肠道菌群中基于鸟枪宏基因组序列的差异 KEGG 通路。列出了前 20 个项目以及相应的 95% 置信区间和调整后的 p 值。 同时，KEGG通路分析通过宏基因组测序发现了HCC中多条生物代谢通路（图5）。方法学上，构建了Diamond算法对两组间KEGG通路进行注释和计算统计差异（图5，调整后p < 0.05）。 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 # 基于CRAN安装R包，检测没有则安装p_list = c("tidyverse", "ggsci", "magrittr", "ggh4x", "rstatix", "ggsignif", "ggpubr", "ggnewscale", "patchwork", "reshape2", "ggplot2")for(p in p_list){if (!requireNamespace(p)){install.packages(p)} library(p, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE)}
# install.packages("devtools")# 基于github安装library(devtools)if(!requireNamespace("ggchicklet", quietly = TRUE)) install_github("hrbrmstr/ggchicklet")
# 加载R包 Load the packagesuppressWarnings(suppressMessages(library(ggchicklet)))suppressWarnings(suppressMessages(library(tidyverse)))suppressWarnings(suppressMessages(library(ggsci)))suppressWarnings(suppressMessages(library(magrittr)))suppressWarnings(suppressMessages(library(ggh4x)))suppressWarnings(suppressMessages(library(rstatix)))suppressWarnings(suppressMessages(library(ggsignif)))suppressWarnings(suppressMessages(library(ggpubr)))suppressWarnings(suppressMessages(library(ggnewscale)))suppressWarnings(suppressMessages(library(patchwork)))suppressWarnings(suppressMessages(library(reshape2)))suppressWarnings(suppressMessages(library(ggplot2)))
# 载入数据# Load datadata <- read.table("data/pathway.txt",header = TRUE,row.names = 1,sep = "\t")group <- read.table("data/group.txt",header = FALSE,sep = "\t")group = group[-1,]# 构建矩阵# Construct matrixrow=as.numeric(length(row.names(data))) col=as.numeric(length(colnames(data))) col_sum=rep(colSums(data), row)col_sum=matrix(col_sum, nrow = col, ncol = row)# 计算相对丰度# Calcualate relative abundancedata2=data/t(col_sum)colSums(data2)#> Healthy01 Healthy02 Healthy03 Healthy04 Healthy05 Healthy06 Healthy07 #> 1 1 1 1 1 1 1 #> Healthy08 Healthy09 Healthy10 Healthy11 Healthy12 Healthy13 Healthy14 #> 1 1 1 1 1 1 1 #> Healthy15 Healthy16 Healthy17 Healthy18 Healthy19 Healthy20 Patients01 #> 1 1 1 1 1 1 1 #> Patients02 Patients03 Patients04 Patients05 Patients06 Patients07 Patients08 #> 1 1 1 1 1 1 1 #> Patients09 Patients10 Patients11 Patients12 Patients13 Patients14 Patients15 #> 1 1 1 1 1 1 1 #> Patients16 Patients17 Patients18 Patients19 Patients20 #> 1 1 1 1 1# 过滤掉平均丰度低于5%的功能分类# Filter out functional categories with an average abundance below 5%data = data2data <- data*100data <- data %>% filter(apply(data,1,mean) > 0.5)data <- t(data)data1 <- data.frame(data,group$V2)colnames(data1) <- c(colnames(data),"Group")data1$Group <- as.factor(data1$Group)# 不符合正态分布，使用非参检验# Does not conform to normal distribution, use non-parametric test# shapiro.test(data1$`Amino acid metabolism`)# shapiro.test(data1$`Biosynthesis of other secondary metabolites`)# shapiro.test(data1$Alistipes_putredinis)# diff <- data1 %>% # select_if(is.numeric) %>%# map_df(~ broom::tidy(t.test(. ~ Group,data = data1)), .id = 'var')# # diff$p.value <- p.adjust(diff$p.value,"bonferroni")# diff <- diff %>% filter(p.value < 0.05)# 非参检验# wilcox testlibrary(tidyverse)diff <- data1 %>% select_if(is.numeric) %>% map_df(~ broom::tidy(wilcox.test(. ~ Group,data = data1, conf.int = TRUE)), .id = 'var')diff$p.value <- p.adjust(diff$p.value,"BH")diff <- diff %>% filter(p.value < 0.05)# write.csv(diff, "results/Pathway_wixcox_test_0.05_abun_filter_0.05.csv")## 绘图数据构建(Data construction)## 左侧条形图(Left bar plot)abun.bar <- data1[,c(diff$var,"Group")] %>% rstatix::gather(variable,value,-Group) %>% group_by(variable,Group) %>% summarise(Mean = mean(value))## 右侧散点图(Scatter plot on the right)diff.mean <- diff[,c("var","estimate","conf.low","conf.high","p.value")]diff.mean$Group <- c(ifelse(diff.mean$estimate >0,levels(data1$Group)[1], levels(data1$Group)[2]))diff.mean <- diff.mean[order(diff.mean$estimate,decreasing = TRUE),]## 左侧条形图(Left bar plot)cbbPalette <- c("#5ebcc2","#d9a285")abun.bar$variable <- factor(abun.bar$variable,levels = rev(diff.mean$var))p1 <- ggplot(abun.bar,aes(variable,Mean,fill = Group)) + scale_x_discrete(limits = levels(diff.mean$var)) + coord_flip() + xlab("") + ylab("Mean proportion (%)") + theme(panel.background = element_rect(fill = 'transparent'), panel.grid = element_blank(), axis.ticks.length = unit(0.2,"lines"), axis.ticks = element_line(color='black'), axis.line = element_line(colour = "black"), axis.title.x=element_text(colour='black', size=9,face = "bold"), axis.text=element_text(colour='black',size=7,face = "bold"), legend.title=element_blank(), legend.text=element_text(size=12,face = "bold",colour = "black", margin = margin(r = 20)), #legend.position = c(-0.1,0.1), legend.position = "top", legend.direction = "horizontal", legend.key.width = unit(0.8,"cm"), legend.key.height = unit(0.5,"cm"))#p1for (i in 1:(nrow(diff.mean) - 1)) p1 <- p1 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf, fill = ifelse(i %% 2 == 0, 'white', 'gray95'))p1 <- p1 + geom_bar(stat = "identity",position = "dodge",width = 0.7,colour = "black",linewidth = 0.1) + scale_fill_manual(values=cbbPalette)#p1## 右侧散点图(Scatter plot on the right)diff.mean$var <- factor(diff.mean$var,levels = levels(abun.bar$variable))diff.mean$p.value <- signif(diff.mean$p.value,3)diff.mean$p.value <- as.character(diff.mean$p.value)p2 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) + theme(panel.background = element_rect(fill = 'transparent'), panel.grid = element_blank(), axis.ticks.length = unit(0.4,"lines"), axis.ticks = element_line(color='black'), axis.line = element_line(colour = "black"), axis.title.x=element_text(colour='black', size=6,face = "bold"), axis.text=element_text(colour='black',size=7,face = "bold"), axis.text.y = element_blank(), legend.position = "none", axis.line.y = element_blank(), axis.ticks.y = element_blank(), plot.title = element_text(size = 10,face = "bold",colour = "black",hjust = 0.5)) + scale_x_discrete(limits = levels(diff.mean$var)) + coord_flip() + xlab("") + ylab("Difference in mean proportions (%)") + labs(title="95% confidence intervals") for (i in 1:(nrow(diff.mean) - 1)) p2 <- p2 + annotate('rect', xmin = i+0.5, xmax = i+1.5, ymin = -Inf, ymax = Inf, fill = ifelse(i %% 2 == 0, 'white', 'gray95'))p2 <- p2 + geom_errorbar(aes(ymin = conf.low, ymax = conf.high), position = position_dodge(0.8), width = 0.3, size = 0.40) + geom_point(shape = 21,size = 1.5) + scale_fill_manual(values=cbbPalette) + geom_hline(aes(yintercept = 0), linetype = 'dashed', color = 'black')#p2 p3 <- ggplot(diff.mean,aes(var,estimate,fill = Group)) + geom_text(aes(y = 0,x = var),label = diff.mean$p.value, hjust = 0,fontface = "bold",inherit.aes = FALSE,size = 3) + geom_text(aes(x = nrow(diff.mean)/2 +0.5,y = 0.85),label = "P-value (corrected)", srt = 90,fontface = "bold",size = 2.5) + coord_flip() + ylim(c(0,1)) + theme(panel.background = element_blank(), panel.grid = element_blank(), axis.line = element_blank(), axis.ticks = element_blank(), axis.text = element_blank(), axis.title = element_blank())## 图像拼接(Combined plot)p <- p1 + p2 + p3 + plot_layout(widths = c(2,2,1))#p## 保存图像# Save plotggsave("results/Pathway_stamp_abun_filter_0.05.pdf",p,width = 8,height = 6)
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:///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 本公众号现全面开放投稿，希望文章作者讲出自己的科研故事，分享论文的精华与亮点。投稿请联系小编（微信号：yongxinliu 或 meta-genomics）
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