生物信息學(xué)習(xí)的正確姿勢(shì)

NGS系列文章包括NGS基礎(chǔ)、在線繪圖、轉(zhuǎn)錄組分析?（Nature重磅綜述|關(guān)于RNA-seq你想知道的全在這）、ChIP-seq分析?（ChIP-seq基本分析流程）、單細(xì)胞測(cè)序分析?(重磅綜述：三萬(wàn)字長(zhǎng)文讀懂單細(xì)胞RNA測(cè)序分析的最佳實(shí)踐教程)、DNA甲基化分析、重測(cè)序分析、GEO數(shù)據(jù)挖掘（典型醫(yī)學(xué)設(shè)計(jì)實(shí)驗(yàn)GEO數(shù)據(jù)分析 (step-by-step)）、批次效應(yīng)處理等內(nèi)容。

寫(xiě)在前面

讓點(diǎn)隨機(jī)排布在一個(gè)區(qū)域，保證點(diǎn)之間不重疊,并且將點(diǎn)的圖層放到最上層，保證節(jié)點(diǎn)最清晰，然后邊可以進(jìn)行透明化，更加突出節(jié)點(diǎn)的位置。這里我新構(gòu)建了布局函數(shù) PolyRdmNotdCirG 來(lái)做這個(gè)隨機(jī)排布。調(diào)用的是packcircles包的算法。使用和其他相似函數(shù)一樣，這里我們重點(diǎn)介紹一下使用這種算法構(gòu)造的二分網(wǎng)絡(luò)布局。

微生物網(wǎng)絡(luò)

ggClusterNet 安裝

ggClusterNet包依賴(lài)的R包均在cran或者biocductor中，所以未能成功安裝，需要檢查依賴(lài)是否都順利安裝。如果網(wǎng)路問(wèn)題，無(wú)法下載R包，可以在github中手動(dòng)下載安裝：具體安裝方法參考：玩轉(zhuǎn)R包

#---ggClusterNet devtools::install_github("taowenmicro/ggClusterNet") #--如果無(wú)法安裝請(qǐng)檢查網(wǎng)絡(luò)或者換個(gè)時(shí)間

導(dǎo)入R包和輸入文件

#--導(dǎo)入所需R包#------- library(ggplot2) library(ggrepel) library(ggClusterNet) library(phyloseq) library(dplyr)# 數(shù)據(jù)內(nèi)置 #-----導(dǎo)入數(shù)據(jù)#------- data(ps)#--可選 #-----導(dǎo)入數(shù)據(jù)#------- ps = readRDS("../ori_data/ps_liu.rds")

這里我們提取一部分OTU，節(jié)省出圖時(shí)間。

# ps data(ps)ps_sub = filter_taxa(ps, function(x) sum(x ) > 20 , TRUE) ps_sub = filter_taxa(ps_sub, function(x) sum(x ) < 30 , TRUE) ps_sub

div_network函數(shù) 用于計(jì)算共有和特有關(guān)系

這個(gè)函數(shù)是之前我寫(xiě)的專(zhuān)門(mén)用于從OTU表格整理成Gephi的輸入文件，所以大家直接用這個(gè)函數(shù)即可轉(zhuǎn)到gephi進(jìn)行操作。這次為了配合二分網(wǎng)絡(luò)，我設(shè)置了參數(shù)flour = TRUE，代表是否僅僅提取共有部分和特有部分。

# ?div_network result = div_network(ps_sub,num = 6)edge = result[[1]] head(edge)# levels(edge$target) # node = result[[2]] # head(node) # # tail(node) data = result[[3]] dim(data)#----計(jì)算節(jié)點(diǎn)坐標(biāo) # flour參數(shù)，設(shè)置是否僅僅展示共有和特有的二分網(wǎng)絡(luò)

div_culculate函數(shù) 核心算法，用于計(jì)算二分網(wǎng)絡(luò)的節(jié)點(diǎn)和邊的表格

參數(shù)解釋：

• distance = 1.1：

  中心一團(tuán)點(diǎn)到樣本點(diǎn)距離

• distance2 = 1.5：

  中心點(diǎn)模塊到獨(dú)有OTU點(diǎn)之間距離

• distance3 = 1.3：

  樣本點(diǎn)和獨(dú)有OTU之間的距離

• order = FALSE ：

  節(jié)點(diǎn)是否需要隨機(jī)擾動(dòng)效果

result <- div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)edge = result[[1]] head(edge)plotdata = result[[2]] head(plotdata) #--這部分?jǐn)?shù)據(jù)是樣本點(diǎn)數(shù)據(jù) groupdata <- result[[3]]

對(duì)OTU進(jìn)行注釋，方便添加到圖形上

為了讓節(jié)點(diǎn)更加豐富，這里我對(duì)節(jié)點(diǎn)文件添加了注釋信息。

# table(plotdata$elements) node = plotdata[plotdata$elements == unique(plotdata$elements), ]otu_table = as.data.frame(t(vegan_otu(ps_sub))) tax_table = as.data.frame(vegan_tax(ps_sub)) res = merge(node,tax_table,by = "row.names",all = F) dim(res) head(res) row.names(res) = res$Row.names res$Row.names = NULL plotcord = resxx = data.frame(mean =rowMeans(otu_table)) head(xx) plotcord = merge(plotcord,xx,by = "row.names",all = FALSE) head(plotcord) # plotcord$Phylum row.names(plotcord) = plotcord$Row.names plotcord$Row.names = NULL head(plotcord)p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),data = edge, size = 0.3,color = "yellow") +geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +geom_text_repel(aes(X1, X2,label = elements ), data = groupdata) +theme_void()pggsave("4.png",p,width = 12,height = 8)

map = as.data.frame(sample_data(ps_sub))map$Group2 <- rep(c("A1","A2","A3","A4","A5","A6"),3)sample_data(ps_sub) <- map# ?div_network result = div_network(ps_sub,num = 3,group = "Group2",flour = TRUE)edge = result[[1]] head(edge)# levels(edge$target) # node = result[[2]] # head(node) # # tail(node)data = result[[3]] dim(data)#----計(jì)算節(jié)點(diǎn)坐標(biāo) # flour參數(shù)，設(shè)置是否僅僅展示共有和特有的二分網(wǎng)絡(luò)result <- div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)edge = result[[1]] head(edge)plotdata = result[[2]] head(plotdata)groupdata <- result[[3]]# table(plotdata$elements) node = plotdata[plotdata$elements == unique(plotdata$elements), ]otu_table = as.data.frame(t(vegan_otu(ps_sub))) tax_table = as.data.frame(vegan_tax(ps_sub)) res = merge(node,tax_table,by = "row.names",all = F) dim(res) head(res) row.names(res) = res$Row.names res$Row.names = NULL plotcord = resxx = data.frame(mean =rowMeans(otu_table)) head(xx) plotcord = merge(plotcord,xx,by = "row.names",all = FALSE) head(plotcord) # plotcord$Phylum row.names(plotcord) = plotcord$Row.names plotcord$Row.names = NULL head(plotcord)p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),data = edge, size = 0.3,color = "yellow") +geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +geom_text_repel(aes(X1, X2,label = elements ), data = groupdata) +theme_void() p ggsave("4.png",p,width = 12,height = 8)

map = as.data.frame(sample_data(ps_sub))map = map[1:12,]# map$Group2 <- rep(c("A1","A2","A3","A4","A5","A6"),2) sample_data(ps_sub) <- mapresult = div_network(ps_sub,num = 3,group = "Group",flour = TRUE)edge = result[[1]] head(edge)# levels(edge$target) # node = result[[2]] # head(node) # # tail(node)data = result[[3]] dim(data)result <- div_culculate(table = result[[3]],distance = 1.1,distance2 = 1.5,distance3 = 1.3,order = FALSE)edge = result[[1]] head(edge)plotdata = result[[2]] head(plotdata)groupdata <- result[[3]]# table(plotdata$elements) node = plotdata[plotdata$elements == unique(plotdata$elements), ]otu_table = as.data.frame(t(vegan_otu(ps_sub))) tax_table = as.data.frame(vegan_tax(ps_sub)) res = merge(node,tax_table,by = "row.names",all = F) dim(res) head(res) row.names(res) = res$Row.names res$Row.names = NULL plotcord = resxx = data.frame(mean =rowMeans(otu_table)) head(xx) plotcord = merge(plotcord,xx,by = "row.names",all = FALSE) head(plotcord) # plotcord$Phylum row.names(plotcord) = plotcord$Row.names plotcord$Row.names = NULL head(plotcord)p = ggplot() + geom_segment(aes(x = X1, y = Y1, xend = X2, yend = Y2),data = edge, size = 0.3,color = "yellow") +geom_point(aes(X1, X2,fill = Phylum,size =mean ),pch = 21, data = plotcord) +geom_point(aes(X1, X2),pch = 21, data = groupdata,size = 5,fill = "blue",color = "black") +geom_text_repel(aes(X1, X2,label = elements ), data = groupdata) +theme_void()p# ggsave("4.png",p,width = 12,height = 22)

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