{"id":518,"date":"2022-07-26T17:43:53","date_gmt":"2022-07-26T09:43:53","guid":{"rendered":"https:\/\/linguopeng.top\/?p=518"},"modified":"2022-07-26T17:43:55","modified_gmt":"2022-07-26T09:43:55","slug":"pcoa","status":"publish","type":"post","link":"https:\/\/linguopeng.top\/?p=518","title":{"rendered":"PCOA"},"content":{"rendered":"\n
#\u8f7d\u5165\u7ed8\u56fe\u6240\u9700\u7684\u5305\nlibrary(vegan)\nlibrary(ape)\nlibrary(ggplot2)\nlibrary(ggrepel)\n#\u5206\u7ec4\u6570\u8f83\u5c11\u3001\u7ec4\u5185\u751f\u7269\u5b66\u91cd\u590d\u8f83\u591a\ndata <- read.csv(\"otu.txt\", head=TRUE,sep=\"\\t\",row.names = 1)\n#\u8f7d\u5165\u5206\u7ec4\u6587\u4ef6\ngroups <- read.table(\"group.txt\",sep = \"\\t\",header = F,colClasses = c(\"character\"))\n#\u5c06\u5206\u7ec4\u6587\u4ef6\u8f6c\u6362\u4e3a\u5217\u8868\ngroups <- as.list(groups)\n#\u5c06\u6570\u636e\u8f6c\u7f6e\u3001\u53bb\u9664\u7f3a\u5931\u503c\ndata <- t(data)\ndata[is.na(data)] <- 0\n#\u8ba1\u7b97Bray-Curtis\u8ddd\u79bb\ndata <- vegdist(data,method = \"bray\")\n#\u8fdb\u884cPCoA\u5206\u6790\npcoa<- pcoa(data, correction = \"none\", rn = NULL)\n#\u5236\u4f5c\u7ed8\u56fe\u6587\u4ef6\nPC1 = pcoa$vectors[,1]\nPC2 = pcoa$vectors[,2]\nplotdata <- data.frame(rownames(pcoa$vectors),PC1,PC2,groups$V2)\ncolnames(plotdata) <-c(\"sample\",\"PC1\",\"PC2\",\"group\")\npich=c(21:24)\n#\u5236\u4f5c\u7ed8\u56fe\u53c2\u6570\u7684\u8d4b\u503c\u5411\u91cf\u3002\n#\u7528\u4e8e\u586b\u5145\u6837\u672c\u70b9\u7684\u989c\u8272\ncbbPalette <- c(\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\")\n#\u6837\u672c\u70b9\u7684\u8fb9\u6846\u989c\u8272\nPalette <- c(\"#000000\", \"#000000\", \"#000000\", \"#000000\")\n#\u7528\u4e8e\u7ed8\u5236\u6a2a\u7eb5\u5750\u6807label\u7684\u6587\u672c\uff0c\u4ee5\u663e\u793a\u89e3\u91ca\u6bd4\u4f8b\npc1 <-floor(pcoa$values$Relative_eig[1]*100)\npc2 <-floor(pcoa$values$Relative_eig[2]*100)\notu.adonis=adonis(data~V2,data = groups,distance = \"bray\")\n#\u5339\u914d\u6a2a\u7eb5\u5750\u6807\nggplot(plotdata, aes(PC1, PC2)) +\n  #\u7ed8\u5236\u6837\u672c\u70b9\uff0c\u6839\u636e\u5206\u7ec4\u5339\u914d\u989c\u8272\u548c\u5f62\u72b6\uff0csize\u8c03\u6574\u70b9\u7684\u5927\u5c0f\n  geom_point(aes(colour=group,shape=group,fill=group),size=12)+\n  geom_text(aes(x = 0.05,y = 0.35,label = paste(\"PERMANOVA:\\n  \n    Group_A VS Group_B\\n    p-value = \",otu.adonis$aov.tab$`Pr(>F)`[1],sep = \"\")),\n    size = 10,hjust = 0)+\n  stat_ellipse(aes(fill = group),geom = \"polygon\",level = 0.95,alpha = 0.3)+\n  #\u5339\u914d\u5f62\u72b6\u3001\u8fb9\u6846\u548c\u586b\u5145\u7684\u56fe\u4f8b\n  scale_shape_manual(values=pich)+\n  scale_colour_manual(values=Palette)+\n  scale_fill_manual(values=cbbPalette)+\n  #\u8bbe\u7f6e\u6807\u9898\u548c\u6a2a\u7eb5\u5750\u6807label\u6587\u5b57\n  labs(title=\"PCoA - The composition of gut microbiome\") + \n  xlab(paste(\"PC1 ( \",pc1,\"%\",\" )\",sep=\"\")) + \n  ylab(paste(\"PC2 ( \",pc2,\"%\",\" )\",sep=\"\"))+\n  theme(text=element_text(size=30))+\n  #\u6dfb\u52a0\u6a2a\u7eb5\u4e24\u6761\u865a\u7ebf\n  geom_vline(aes(xintercept = 0),linetype=\"dotted\")+\n  geom_hline(aes(yintercept = 0),linetype=\"dotted\")+\n  #\u8c03\u6574\u80cc\u666f\u3001\u5750\u6807\u8f74\u3001\u56fe\u4f8b\u7684\u683c\u5f0f\n  theme(panel.background = element_rect(fill='white', colour='black'),\n        panel.grid=element_blank(), \n        axis.title = element_text(color='black',size=34),\n        axis.ticks.length = unit(0.4,\"lines\"), \n        axis.ticks = element_line(color='black'),\n        axis.line = element_line(colour = \"black\"), \n        axis.title.x=element_text(colour='black', size=34),\n        axis.title.y=element_text(colour='black', size=34),\n        axis.text=element_text(colour='black',size=28),\n        legend.title=element_blank(),\n        legend.text=element_text(size=34),\n        legend.key=element_blank(),legend.position = c(0.12,0.88),\n        legend.background = element_rect(colour = \"black\"),\n        legend.key.height=unit(1.6,\"cm\"))+\n  #\u8bbe\u7f6e\u6807\u9898\u7684\u683c\u5f0f\n  theme(plot.title = element_text(size=34,colour = \"black\",hjust = 0.5,face = \"bold\"))<\/code><\/pre>\n\n\n\n
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#\u8f7d\u5165\u5206\u6790\u5305\r\nlibrary(vegan)\r\nlibrary(ape)\r\n#\u8f7d\u5165\u5206\u6790\u6570\u636e\r\ndata <- read.csv(\"otu.txt\", head=TRUE,sep=\"\\t\",row.names = 1)\r\n#\u5c06\u6570\u636e\u8f6c\u7f6e\u3001\u53bb\u9664\u7f3a\u5931\u503c\r\ndata <- t(data)\r\ndata[is.na(data)] <- 0\r\n#\u8ba1\u7b97Bray-Curtis\u8ddd\u79bb\r\ndata <- vegdist(data, method = \"bray\")\r\n#\u8fdb\u884cPCoA\u5206\u6790\r\npcoa<- pcoa(data, correction = \"none\", rn = NULL)\r\n#\u8f7d\u5165\u5206\u7ec4\u6587\u4ef6\r\ngroups <- read.table(\"group.txt\",sep = \"\\t\",header = F,colClasses = c(\"character\"))\r\n#\u5c06\u5206\u7ec4\u6587\u4ef6\u8f6c\u6362\u4e3a\u5217\u8868\r\ngroups <- as.list(groups)\r\n#\u5236\u4f5c\u7ed8\u56fe\u6587\u4ef6\r\nPC1 = pcoa$vectors[,1]\r\nPC2 = pcoa$vectors[,2]\r\nplotdata <- data.frame(rownames(pcoa$vectors),PC1,PC2,groups$V2)\r\ncolnames(plotdata) <-c(\"sample\",\"PC1\",\"PC2\",\"group\")\r\n#\u8bbe\u7f6e\u5f62\u72b6\u70b9\u6570\r\npich=c(21:24)\r\n#\u7528\u4e8e\u586b\u5145\u6837\u672c\u70b9\u7684\u989c\u8272\r\ncbbPalette <- c(\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\")\r\n#\u6837\u672c\u70b9\u7684\u8fb9\u6846\u989c\u8272\r\nPalette <- c(\"#000000\", \"#000000\",\"#000000\",\"#000000\")\r\n#\u7528\u4e8e\u7ed8\u5236\u6a2a\u7eb5\u5750\u6807label\u7684\u6587\u672c\uff0c\u4ee5\u663e\u793a\u89e3\u91ca\u6bd4\u4f8b\r\npc1 <-floor(pcoa$values$Relative_eig[1]*100)\r\npc2 <-floor(pcoa$values$Relative_eig[2]*100)\r\n#\u8f7d\u5165\u7ed8\u56fe\u5305\r\nlibrary(ggplot2)\r\n#\u5339\u914d\u6a2a\u7eb5\u5750\u6807\r\nggplot(plotdata, aes(PC1, PC2)) +\r\n  #\u7ed8\u5236\u6837\u672c\u70b9\uff0c\u6839\u636e\u5206\u7ec4\u5339\u914d\u989c\u8272\u548c\u5f62\u72b6\uff0csize\u8c03\u6574\u70b9\u7684\u5927\u5c0f\r\n  geom_point(aes(colour=group,shape=group,fill=group),size=12)+\r\n  #\u5339\u914d\u5f62\u72b6\u3001\u8fb9\u6846\u548c\u586b\u5145\u7684\u56fe\u4f8b\r\n  scale_shape_manual(values=pich)+\r\n  scale_colour_manual(values=Palette)+\r\n  scale_fill_manual(values=cbbPalette)+\r\n  #\u8bbe\u7f6e\u6807\u9898\u548c\u6a2a\u7eb5\u5750\u6807label\u6587\u5b57\r\n  labs(title=\"PCoA - The composition of gut microbiome\") + \r\n  xlab(paste(\"PC1 ( \",pc1,\"%\",\" )\",sep=\"\")) + \r\n  ylab(paste(\"PC2 ( \",pc2,\"%\",\" )\",sep=\"\"))+\r\n  theme(text=element_text(size=30))+\r\n  #\u6dfb\u52a0\u6a2a\u7eb5\u4e24\u6761\u865a\u7ebf\r\n  geom_vline(aes(xintercept = 0),linetype=\"dotted\")+\r\n  geom_hline(aes(yintercept = 0),linetype=\"dotted\")+\r\n  #\u8c03\u6574\u80cc\u666f\u3001\u5750\u6807\u8f74\u3001\u56fe\u4f8b\u7684\u683c\u5f0f\r\n  theme(panel.background = element_rect(fill='white', colour='black'),\r\n        panel.grid=element_blank(), \r\n        axis.title = element_text(color='black',size=34),\r\n        axis.ticks.length = unit(0.4,\"lines\"), \r\n        axis.ticks = element_line(color='black'),\r\n        axis.line = element_line(colour = \"black\"), \r\n        axis.title.x=element_text(colour='black', size=34),\r\n        axis.title.y=element_text(colour='black', size=34),\r\n        axis.text=element_text(colour='black',size=28),\r\n        legend.title=element_blank(),\r\n        legend.text=element_text(size=34),\r\n        legend.key=element_blank(),\r\n        legend.background = element_rect(colour = \"black\"),\r\n        legend.key.height=unit(1.6,\"cm\"))+\r\n  #\u8bbe\u7f6e\u6807\u9898\u7684\u683c\u5f0f\r\n  theme(plot.title = element_text(size=34,colour = \"black\",hjust = 0.5,face = \"bold\"))<\/code><\/pre>\n\n\n\n
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#\u540c\u65f6\u663e\u793a\u4e24\u79cd\u5206\u7c7b\u65b9\u6cd5\r\ndata <- read.csv(\"otu_taxa_table.txt\", head=TRUE,sep=\"\\t\",row.names = 1)\r\ngroups <- read.table(\"group.list.txt\",sep = \"\\t\",header = F,colClasses = c(\"character\"))\r\ngroups <- as.list(groups)\r\ndata <- t(data)\r\ndata[is.na(data)] <- 0\r\ndata <- vegdist(data,method = \"bray\")\r\npcoa<- pcoa(data, correction = \"none\", rn = NULL)\r\nPC1 = pcoa$vectors[,1]\r\nPC2 = pcoa$vectors[,2]\r\nplotdata <- data.frame(rownames(pcoa$vectors),PC1,PC2,groups$V2,groups$V3)\r\ncolnames(plotdata) <-c(\"sample\",\"PC1\",\"PC2\",\"Treatment\",\"Time\")\r\npich=c(21:25)\r\ncbbPalette <- c(\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\")\r\npc1 <-floor(pcoa$values$Relative_eig[1]*100)\r\npc2 <-floor(pcoa$values$Relative_eig[2]*100)\r\nggplot(plotdata, aes(PC1, PC2)) +\r\n  geom_point(aes(shape=Time,fill=Treatment,colour=Treatment),size=10)+ \r\n  scale_shape_manual(values=pich)+\r\n  scale_colour_manual(values=cbbPalette)+\r\n  scale_fill_manual(values=cbbPalette)+\r\n  labs(title=\"PCoA - PC1-VS-PC2\") + \r\n  xlab(paste(\"PC1 ( \",pc1,\"%\",\" )\",sep=\"\")) + \r\n  ylab(paste(\"PC2 ( \",pc2,\"%\",\" )\",sep=\"\"))+\r\n  theme(text=element_text(size=30))+\r\n  geom_vline(aes(xintercept = 0),linetype=\"dotted\")+\r\n  geom_hline(aes(yintercept = 0),linetype=\"dotted\")+\r\n  theme(panel.background = element_rect(fill='white', colour='black'),\r\n        panel.grid=element_blank(), \r\n        axis.title = element_text(color='black',size=34),\r\n        axis.ticks.length = unit(0.4,\"lines\"), \r\n        axis.ticks = element_line(color='black'),\r\n        axis.line = element_line(colour = \"black\"), \r\n        axis.title.x=element_text(colour='black', size=34),\r\n        axis.title.y=element_text(colour='black', size=34),\r\n        axis.text=element_text(colour='black',size=28),\r\n        legend.title=element_text(colour = \"black\",size = 34,face = \"bold\"),\r\n        legend.text=element_text(size=34),\r\n        legend.key=element_blank(),\r\n        legend.background = element_rect(colour = \"black\"),\r\n        legend.key.height=unit(1.6,\"cm\"))+\r\n  theme(plot.title = element_text(size=34,colour = \"black\",hjust = 0.5,face = \"bold\"))<\/code><\/pre>\n\n\n\n
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#stat_ellipse(aes(fill = Treatment),geom = \"polygon\",level = 0.95,alpha = 0.3)+\r\n#level\u8868\u793a\u7f6e\u4fe1\u9608\u503c\uff0c\u901a\u5e38\u4e3a0.95\uff0calpha\u4e3a\u7f6e\u4fe1\u692d\u5706\u989c\u8272\u7684\u900f\u660e\u5ea6\u3002\r\n#\u5355\u7ec4\u6837\u54c1\u6570\u76ee\u81f3\u5c11\u8981\u67094\u4e2a\uff0c\u4e0d\u7136\u753b\u4e0d\u51fa\u6765\u692d\u5706\u3002\r\n#legend.position = c(0.12,0.88)\r\n#\u53ef\u4ee5\u770b\u5230\u9ed8\u8ba4\u7684\u7ed8\u56fe\u65b9\u5f0f\u4e2d\uff0c\u56fe\u4f8b\u662f\u663e\u793a\u5728\u56fe\u50cf\u53f3\u4fa7\uff0c\u786e\u5b9e\u662f\u6709\u70b9\u5360\u5730\u65b9\uff0c\r\n#\u901a\u8fc7\u89c2\u5bdf\u56fe\u50cf\uff0c\u53d1\u73b0\u56fe\u50cf\u7684\u5de6\u4e0a\u65b9\u7a7a\u95f4\u8f83\u5927\uff0c\u56e0\u6b64\u5728\u7cca\u6d82\u4ee3\u7801\u7684theme\u4e2d\u6dfb\u52a0\u4e00\u884c\u547d\u4ee4\uff0c\r\n#\u56fe\u4f8b\u4f4d\u7f6e\u7684\u4fee\u6539\u6ca1\u6709\u529e\u6cd5\u81ea\u52a8\u751f\u6210\uff0c\u9700\u8981\u5148\u7ed8\u5236\u4e00\u904d\u9ed8\u8ba4\u7684\u56fe\u50cf\uff0c\u901a\u8fc7\u89c2\u5bdf\u786e\u8ba4\u56fe\u4f8b\u53ef\u4ee5\u9632\u6b62\u7684\u4f4d\u7f6e\uff0c\r\n#\u4ee5\u9632\u5bf9\u6837\u672c\u70b9\u5f62\u6210\u906e\u6321\u3002\r\n#\u901a\u5e38\u60c5\u51b5\u4e0b\uff0c\u9664\u4e86PCoA\u4e4b\u5916\uff0c\u8fd8\u9700\u8981\u4f7f\u7528PERMANOVA\u6765\u68c0\u9a8c\u4e0d\u540c\u7ec4\u6837\u672c\u95f4\u7684\u5fae\u751f\u7269\u7fa4\u843d\u662f\u5426\u5177\u6709\u663e\u8457\u5dee\u5f02\u3002\r\n#\u4f7f\u7528vegan\u5305\u4e2d\u7684adonis\u51fd\u6570\u8fdb\u884cPERMANOVA\u5206\u6790\u3002\r\n#otu.adonis=adonis(data~V2,data = groups,distance = \"bray\")\r\n#\u4e4b\u540e\u5728\u7ed8\u56fe\u4ee3\u7801\u4e2d\u6dfb\u52a0\u5982\u4e0b\u4e00\u884c\u547d\u4ee4\uff0c\u5c06PERMANVOA\u7684\u7ed3\u679c\u5728PCoA\u56fe\u4e2d\u8fdb\u884c\u663e\u793a\u3002\r\n#geom_text(aes(x = 0.05,y = 0.35,label = paste(\"PERMANOVA:\\n   \r\n# Group_A VS Group_B\\n    p-value = \",otu.adonis$aov.tab$`Pr(>F)`[1],sep = \"\")),\r\n# size = 10,hjust = 0)\r\n#PERMANOVA\u7ed3\u679c\u7684x\u548cy\u5750\u6807\u8981\u6839\u636e\u56fe\u50cf\u5b9e\u9645\u60c5\u51b5\u8fdb\u884c\u8bbe\u7f6e\uff0c\u9632\u6b62\u663e\u793a\u4e0d\u5168\u6216\u8005\u906e\u6321\u6837\u672c\u70b9\u3002\r\n#\u7531\u4e8e\u672c\u793a\u4f8b\u7b2c\u4e8c\u79cd\u65b9\u5f0f\u6709\u5206\u4e3a5\u7c7b\uff0c\u56e0\u6b64pich\u7684\u8d4b\u503c\u6539\u4e3a\u4e8621-25\uff0c\u8bf7\u6839\u636e\u81ea\u5df1\u6570\u636e\u7684\u5206\u7ec4\u6570\u76ee\u81ea\u884c\u8c03\u6574\u3002\r\n#\u7531\u4e8e\u5206\u7ec4\u662f\u6309\u716712\u4e2a\u6708\u8fdb\u884c\u5206\u7ec4\u7684\uff0c\u56e0\u800c\u9700\u8981\u901a\u8fc7\u6307\u5b9a\u5206\u7ec4\u56e0\u5b50\u7684\u987a\u5e8f\r\n#\u4ee5\u8fbe\u5230\u56fe\u4f8b\u4e2d\u4ece1\u81f312\u6708\u4f9d\u6b21\u663e\u793a\u7684\u76ee\u7684\u3002\r\n#\u7531\u4e8e\u5206\u7ec4\u670912\u4e2a\uff0c\u800cR\u4e2d\u63d0\u4f9b\u7684\u5177\u6709\u586b\u5145\u989c\u8272\u7684\u5f62\u72b6\u8fdc\u6ca1\u6709\u8fd9\u4e48\u591a\uff0c\r\n#\u56e0\u6b64\u81f3\u9009\u62e9\u4e864\u4e2a\u5f62\u72b6\u901a\u8fc7\u91cd\u590d3\u6b21\u6765\u8fbe\u5230\u5f62\u72b6\u7684\u5339\u914d\u3002\r\n#\u901a\u8fc7legned.key.height\u8c03\u6574\u56fe\u4f8b\u7684\u9ad8\u5ea6\uff0c\u4ece\u800c\u8fbe\u5230\u56fe\u4f8b\u6b63\u597d\u4e0e\u56fe\u50cf\u9ad8\u5ea6\u4e00\u81f4\u3002\r\n#\u4ece\u56fe\u603b\u53ef\u4ee5\u770b\u51fa\u6709\u4e00\u4e9b\u6837\u672c\u5b58\u5728\u79bb\u7fa4\u73b0\u8c61\uff0c\r\n#\u8981\u60f3\u5728\u56fe\u4e2d\u5c55\u793a\u54ea\u4e2a\u6837\u672c\u79bb\u7fa4\u5c31\u9700\u8981\u5728\u6837\u672c\u70b9\u9644\u8fd1\u6807\u51fa\u6837\u672c\u540d\u79f0\u3002\r\n#\u5e38\u89c4\u7684\u65b9\u6cd5\u53ef\u4ee5\u4f7f\u7528geom_text\u901a\u8fc7\u5bf9\u6837\u672c\u70b9\u5750\u6807\u7684\u5339\u914d\u6807\u51fa\u6837\u672c\u540d\u79f0\uff0c\r\n#\u6bd4\u5982\u53ef\u4ee5\u5728\u7ed8\u56fe\u4ee3\u7801\u4e2d\u6dfb\u52a0\u5982\u4e0b\u547d\u4ee4\u3002geom_text(aes(label=sample),size=5,hjust=0.5,vjust=-1)\r\n#\u6dfb\u52a0\u7684\u6837\u672c\u540d\u79f0\u4f1a\u7531\u4e8e\u90e8\u5206\u6837\u54c1\u8ddd\u79bb\u8f83\u8fd1\u800c\u51fa\u73b0\u91cd\u53e0\u906e\u6321\uff0c\r\n#\u5f53\u7136\u53ef\u4ee5\u901a\u8fc7\u4e00\u4e2a\u540d\u79f0\u4e00\u4e2a\u540d\u79f0\u7684\u8c03\u6574\u6765\u89e3\u51b3\uff0c\u4f46\u662f\u6548\u7387\u5b9e\u5728\u662f\u4f4e\u3002\r\n#\u6bd4\u8f83\u5feb\u901f\u7684\u65b9\u6cd5\u662f\u901a\u8fc7ggerpel\u5305\u6dfb\u52a0\u6837\u672c\u540d\u79f0\uff0c\u5176\u4f1a\u81ea\u52a8\u8c03\u6574\u906e\u6321\u7684\u6837\u672c\u540d\uff0c\u4ece\u800c\u8fbe\u5230\u533a\u5206\u7684\u76ee\u7684\u3002\r\n#\u8f7d\u5165ggerple\u5305\u540e\uff0c\u5728\u7ed8\u56fe\u547d\u4ee4\u4e2d\u6dfb\u52a0\u5982\u4e0b\u4ee3\u7801\u3002\r\n#geom_label_repel(aes(PC1,PC2,label = plotdata$sample),fill = \"white\",color = \"black\", \r\n                  box.padding = unit(0.6,\"lines\"),segment.colour = \"grey50\",               \r\n                  label.padding = unit(0.35,\"lines\"))\r\n#ggerple\u6dfb\u52a0\u6837\u672c\u540d\u79f0\u5728\u6837\u672c\u8fc7\u4e8e\u5bc6\u96c6\u7684\u60c5\u51b5\u4e5f\uff0c\r\n#\u5076\u5c14\u4e5f\u4f1a\u51fa\u73b0\u4e2a\u522b\u6837\u672c\u540d\u79f0\u6709\u906e\u6321\u7684\u60c5\u51b5\uff0c99.9%\u4ee5\u4e0a\u6ca1\u6709\u95ee\u9898\u3002\r\n#geom_polygon(data = plotdata,aes(fill = Treatment),alpha = 0.5,show.legend = FALSE)+<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
#\u5206\u7ec4\u6570\u76ee\u8f83\u591a\u3001\u7ec4\u5185\u751f\u7269\u5b66\u91cd\u590d\u8f83\u5c11\r\ndata <- read.csv(\"otu 2.txt\", head=TRUE,sep=\"\\t\",row.names = 1)\r\ndata <- t(data)\r\ndata[is.na(data)] <- 0\r\ndata <- vegdist(data)\r\npcoa<- pcoa(data, correction = \"none\", rn = NULL)\r\ngroups <- read.table(\"group 2.txt\",sep = \"\\t\",header = F,colClasses = c(\"character\"))\r\ngroups <- as.list(groups)\r\ngroups$V2 <- factor(groups$V2,levels = c(\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\r\n\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"))\r\nPC1 = pcoa$vectors[,1]\r\nPC2 = pcoa$vectors[,2]\r\nplotdata <- data.frame(rownames(pcoa$vectors),PC1,PC2,groups$V2)\r\ncolnames(plotdata) <-c(\"sample\",\"PC1\",\"PC2\",\"group\")\r\npc1 <-floor(pcoa$values$Relative_eig[1]*100)\r\npc2 <-floor(pcoa$values$Relative_eig[2]*100)\r\npich=rep(c(21:24),3)\r\nlibrary(RColorBrewer)\r\ncbbPalette <- brewer.pal(12,\"Set3\")\r\nPalette <- c(rep(\"#000000\",12))\r\nggplot(plotdata, aes(PC1, PC2)) +\r\n  geom_point(aes(colour=group,shape=group,fill=group),size=8)+\r\n  geom_label_repel(aes(PC1,PC2,label = sample),fill = \"white\",color = \"black\",\r\n                   box.padding = unit(0.6,\"lines\"),\r\n                   segment.colour = \"grey50\",\r\n                   label.padding = unit(0.35,\"lines\")) +\r\n  scale_shape_manual(values=pich)+\r\n  scale_colour_manual(values=Palette)+\r\n  scale_fill_manual(values=cbbPalette)+\r\n  labs(title=\"PCoA - The composition of microbiome in sediment\") + \r\n  xlab(paste(\"PC1 ( \",pc1,\"%\",\" )\",sep=\"\")) + \r\n  ylab(paste(\"PC2 ( \",pc2,\"%\",\" )\",sep=\"\"))+\r\n  theme(text=element_text(size=30))+\r\n  geom_vline(aes(xintercept = 0),linetype=\"dotted\")+\r\n  geom_hline(aes(yintercept = 0),linetype=\"dotted\")+\r\n  theme(panel.background = element_rect(fill='white', colour='black'),\r\n        panel.grid=element_blank(), \r\n        axis.title = element_text(color='black',size=34),\r\n        axis.ticks.length = unit(0.4,\"lines\"),\r\n        axis.ticks = element_line(color='black'),\r\n        axis.line = element_line(colour = \"black\"), \r\n        axis.title.x=element_text(colour='black', size=34),\r\n        axis.title.y=element_text(colour='black', size=34),\r\n        axis.text=element_text(colour='black',size=28),\r\n        legend.title=element_blank(),\r\n        legend.text=element_text(size=28),\r\n        legend.key=element_blank(),legend.position = \"right\",\r\n        legend.background = element_rect(colour = \"black\"),\r\n        legend.key.height=unit(1.55,\"cm\"))+\r\n  theme(plot.title = element_text(size=34,colour = \"black\",hjust = 0.5,face = \"bold\"))<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n\n\n\n
#geom_polygon(data = plotdata, aes(fill = group), alpha = 0.5, show.legend = FALSE)+\r<\/code><\/pre>\n\n\n\n
\"\"<\/div><\/figure>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[3],"class_list":["post-518","post","type-post","status-publish","format-standard","hentry","category-uncategorized","tag-r"],"_links":{"self":[{"href":"https:\/\/linguopeng.top\/index.php?rest_route=\/wp\/v2\/posts\/518","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/linguopeng.top\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/linguopeng.top\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/linguopeng.top\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/linguopeng.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=518"}],"version-history":[{"count":1,"href":"https:\/\/linguopeng.top\/index.php?rest_route=\/wp\/v2\/posts\/518\/revisions"}],"predecessor-version":[{"id":525,"href":"https:\/\/linguopeng.top\/index.php?rest_route=\/wp\/v2\/posts\/518\/revisions\/525"}],"wp:attachment":[{"href":"https:\/\/linguopeng.top\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=518"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/linguopeng.top\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=518"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/linguopeng.top\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=518"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}