简介
在分析物种和基因相关性，或者分析多界物种与基因或者其它因子的相关性时，常常用到Spearman相关性网络来展示多个分组之间的相关性。这里从实际案例出发，尝试用Gephi和R软件实现物种和基因的Spearman相关性网络分析。
标签：#微生物组数据分析 #MicrobiomeStatPlot #物种和基因Spearman相关网络分析 #R语言可视化 #Species and Gene Spearman Correlation Network Analysis
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/SpearmanCorrelationNetworkAnalysis
或公众号后台回复“MicrobiomeStatPlot”领取
这是来自于上海交通大学医学院Haoyan Chen和Jie Hong课题组2023年发表于Cell Host & Microbe上的一篇论文。论文题目为：Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts. https://doi.org/10.1016/j.chom.2023.10.005
图 6 | 阻断反应者和无反应者中多界标记和代谢差异丰度 KO 基因的共现网络。
细菌节点用绿色表示，真菌节点用蓝色表示，KO基因用红色表示。正相关性用橙色表示，负相关性用蓝色表示。
结果
为了探索代谢功能与微生物群之间的关系，研究人员评估了差异代谢 KO 基因与多界标记之间的相关性。注意到，真菌裂殖酵母 (Schizosaccharomyces octosporus) 是响应者多界网络的中心 (图 5B)，与 2 个 KO 基因呈正相关。在无响应者中没有观察到这种情况 (图 6C)，这表明裂殖酵母的富集及其代谢活动可能对响应者具有特异性。
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p_list = c("igraph","Hmisc","psych","dplyr","tidyr")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 Load the packagesuppressWarnings(suppressMessages(library(igraph)))suppressWarnings(suppressMessages(library(Hmisc)))suppressWarnings(suppressMessages(library(psych)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(tidyr)))
实战
# 载入数据# Load datamic <- read.table("data/Species_data.txt", sep="\t", header=T, check.names=F,row.names = 1)mic = apply(mic, 2, function(x) x/100)gene <- read.table("data/KO_data.txt", sep="\t", header=T, check.names=F,row.names = 1)group <- read.table("data/group.txt", sep="\t", header=T, check.names=F)mic <- as.data.frame(t(mic))mic$sample <- rownames(mic)gene <- as.data.frame(t(gene))gene$sample <- rownames(gene)df <- merge(mic, gene, by = "sample")rownames(df) <- df$sampledf <- df[-1]head(df)# 计算相关性并以p>0.05作为筛选阈值进行数据处理data<-as.matrix(df)cor<- corr.test(data, method="spearman",adjust="BH")data.cor <- as.data.frame(cor$r)r.cor<-data.frame(cor$r)[91:117,1:90]p.cor<-data.frame(cor$p)[91:117,1:90]r.cor[p.cor>0.05] <- 0r.cor[abs(r.cor) < 0.3] <- 0# 构建网络连接属性及节点属性# 将数据转换为long format进行合并并添加连接属性r.cor$from = rownames(r.cor)p.cor$from = rownames(p.cor)p_value <- p.cor %>%gather(key = "to", value = "p", -from) %>%data.frame()#p_value$FDR <- p.adjust(p_value$p,"BH")p_value <- p_value[, -3]cor.data<- r.cor %>%gather(key = "to", value = "r", -from) %>%data.frame() %>%left_join(p_value, by=c("from","to")) %>%mutate(linecolor = ifelse(r > 0,"positive","negative"),linesize = abs(r))cor.data <- cor.data[abs(cor.data$r)>0.3, ]write.csv(cor.data, "results/Species_KO_all_correlations_0.2.csv")###设置节点属性vertices <- c(as.character(cor.data$from),as.character(cor.data$to)) %>%as_tibble() %>%group_by(value) %>%summarise()colnames(vertices) <- "name"vertices <- vertices %>%left_join(group,by="name")vertices$group <- factor(vertices$group, levels = c("Species","KO" ))vertices <- vertices %>%arrange(group)#构建graph数据结构并添加网络基础属性、保存数据###构建graph数据结构graph <- graph_from_data_frame(cor.data, vertices = vertices, directed = FALSE )E(graph)$weight <- abs(E(graph)$r)V(graph)$label <- V(graph)$name###保存数据#write_graph(graph, "Healthy_180_net13_new0911.graphml", format="graphml")write_graph(graph, "results/Species_KO_0.2.graphml", format="graphml")# 可视化方式1：基于Gephi软件进行可视化 https://gephi.org/# 可视化方式2：利用igraph进行可视化g <- graph# 准备网络图布局数据# Preparing network diagram layout data。layout1 <- layout_in_circle(g)layout5 <- layout_with_graphopt(g)## 设置绘图颜色## Setting the drawing colorcolor <- c("#879b56","#ce77ad")names(color) <- unique(V(g)$group)V(g)$point.col <- color[match(V(g)$group,names(color))]## 边颜色按照相关性正负设置## The edge color is set according to the positive or negative correlation#E(g)$color <- ifelse(E(g)$linecolor == "positive","#ff878c",rgb(0,147,0,maxColorValue = 255))E(g)$color <- ifelse(E(g)$linecolor == "positive","#ff878c","#5ea6c2")pdf("results/network_group_graphopt.pdf",family = "Times",width = 10,height = 12)par(mar=c(5,2,1,2))plot.igraph(g, layout=layout5,vertex.color=V(g)$point.col,vertex.border=V(g)$point.col,vertex.size=6,vertex.frame.color="white",vertex.label=g$name,vertex.label.cex=0.8,vertex.label.dist=0,vertex.label.degree = pi/2,vertex.label.col="black",edge.arrow.size=0.5,edge.width=abs(E(g)$r)*6,)# 设置图例legend(title = "group",list(x = min(layout1[,1])-0.05,y = min(layout1[,2])-0.05),legend = c(unique(V(g)$group)),fill = color,)legend(title = "|r-value|",list(x = min(layout1[,1])+0.6,y = min(layout1[,2])-0.05),legend = c(0.2,0.4,0.6,0.8,1.0),col = "black",lty=1,lwd=c(0.2,0.4,0.6,0.8,1.0)*4,)legend(title = "Correlation (±)",list(x = min(layout1[,1])+1.0,y = min(layout1[,2])-0.05),legend = c("positive","negative"),col = c("#ff878c",rgb(0,147,0,maxColorValue = 255)),lty=1,lwd=1)dev.off()#> png#> 2
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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