這是來自于上海交通大學(xué)醫(yī)學(xué)院Haoyan Chen和Jie Hong課題組2023年發(fā)表于Cell Host & Microbe上的一篇論文。論文題目為：Multi-kingdom gut microbiota analyses define bacterial-fungal interplay and microbial markers of pan-cancer immunotherapy across cohorts. https:///10.1016/j.chom.2023.10.005

圖 6 |  阻斷反應(yīng)者和無反應(yīng)者中多界標(biāo)記和代謝差異豐度 KO 基因的共現(xiàn)網(wǎng)絡(luò)。

為了探索代謝功能與微生物群之間的關(guān)系，研究人員評估了差異代謝 KO 基因與多界標(biāo)記之間的相關(guān)性。注意到，真菌裂殖酵母 (Schizosaccharomyces octosporus) 是響應(yīng)者多界網(wǎng)絡(luò)的中心 (圖 5B)，與 2 個(gè) KO 基因呈正相關(guān)。在無響應(yīng)者中沒有觀察到這種情況 (圖 6C)，這表明裂殖酵母的富集及其代謝活動(dòng)可能對響應(yīng)者具有特異性。

源代碼及測試數(shù)據(jù)鏈接：

https://github.com/YongxinLiu/MicrobiomeStatPlot/

或公眾號后臺(tái)回復(fù)“MicrobiomeStatPlot”領(lǐng)取

# 基于CRAN安裝R包，檢測沒有則安裝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)}
# 加載R包 Load the packagesuppressWarnings(suppressMessages(library(igraph)))suppressWarnings(suppressMessages(library(Hmisc)))suppressWarnings(suppressMessages(library(psych)))suppressWarnings(suppressMessages(library(dplyr)))suppressWarnings(suppressMessages(library(tidyr)))

# 載入數(shù)據(jù)# 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$sampledf <- df[-1]head(df)# 計(jì)算相關(guān)性并以p>0.05作為篩選閾值進(jìn)行數(shù)據(jù)處理data<-as.matrix(df)cor<- corr.test(data, method="spearman",adjust="BH")data.cor <- as.data.frame(cor$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# 構(gòu)建網(wǎng)絡(luò)連接屬性及節(jié)點(diǎn)屬性# 將數(shù)據(jù)轉(zhuǎn)換為long format進(jìn)行合并并添加連接屬性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")) %>%  #diff$p.value <- p.adjust(diff$p.value,"BH")  #filter(FDR <= 1e-5, from != to) %>%  #filter(p <= 0.001, from != to) %>%  mutate(    linecolor = ifelse(r > 0,"positive","negative"),    linesize = abs(r)  )cor.data <- cor.data[abs(cor.data$r)>0.3, ]write.csv(cor.data, "results/Species_KO_all_correlations_0.2.csv")###設(shè)置節(jié)點(diǎn)屬性vertices <- c(as.character(cor.data$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$group, levels = c("Species","KO" ))vertices <- vertices %>%  arrange(group)#構(gòu)建graph數(shù)據(jù)結(jié)構(gòu)并添加網(wǎng)絡(luò)基礎(chǔ)屬性、保存數(shù)據(jù)###構(gòu)建graph數(shù)據(jù)結(jié)構(gòu)graph <- graph_from_data_frame(cor.data, vertices = vertices, directed = FALSE )E(graph)$weight <- abs(E(graph)$r)V(graph)$label <- V(graph)$name###保存數(shù)據(jù)#write_graph(graph, "Healthy_180_net13_new0911.graphml", format="graphml")write_graph(graph, "results/Species_KO_0.2.graphml", format="graphml")# 可視化方式1：基于Gephi軟件進(jìn)行可視化 https://gephi.org/# 可視化方式2：利用igraph進(jìn)行可視化g <- graph# 準(zhǔn)備網(wǎng)絡(luò)圖布局?jǐn)?shù)據(jù)# Preparing network diagram layout data。layout1 <- layout_in_circle(g)layout5 <- layout_with_graphopt(g)## 設(shè)置繪圖顏色## Setting the drawing colorcolor <- c("#879b56","#ce77ad")names(color) <- unique(V(g)$group) V(g)$point.col <- color[match(V(g)$group,names(color))] ## 邊顏色按照相關(guān)性正負(fù)設(shè)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,     )# 設(shè)置圖例legend(  title = "group",  list(x = min(layout1[,1])-0.05,       y = min(layout1[,2])-0.05),   legend = c(unique(V(g)$group)),  fill = color,  #pch=1)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

使用此腳本，請引用下文：

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:///10.1002/imt2.83