{"id":501,"date":"2022-05-18T17:09:49","date_gmt":"2022-05-18T09:09:49","guid":{"rendered":"https:\/\/linguopeng.top\/?p=501"},"modified":"2022-05-18T18:54:39","modified_gmt":"2022-05-18T10:54:39","slug":"tsne%ef%bc%8cmantel","status":"publish","type":"post","link":"https:\/\/linguopeng.top\/?p=501","title":{"rendered":"tSNE\uff0cmantel"},"content":{"rendered":"\n
# Huang WC, Liu Y, Zhang X, Zhang CJ, Zou D, Zheng S, Xu W, Luo Z, Liu F, Li M. \n#Comparative genomic analysis reveals metabolic flexibility of Woesearchaeota. \n#Nat Commun. 2021 Sep 6;12(1):5281. doi: 10.1038\/s41467-021-25565-9\nlibrary(ggplot2)\nlibrary(ggmap)\nlibrary(sp)\nlibrary(maptools)\nlibrary(maps)\nwoese<-read.table('Data4.tsv',sep='\\t',row.names=1,header=T,check.name=FALSE)\ncolors<-c('#d6274a','#4ea94a','#f6d534','#4862ab','#ea7a3a','#7e3a8e','#75c7d0','#b0569c','#f5bbbe','#b9cf39','#955f25')\n#ncol<-c('#b0569c','#ea7a3a','#75c7d0','#b9cf39','#7e3a8e','#4ea94a','#f6d534','#4862ab','#955f25','#f5bbbe','#d6274a')\nnames(colors)<-unique(woese$biotope)\nmp<-NULL\nmapworld<-borders(\"world\",colour = \"gray50\",fill=\"white\") #\nmp2<-mp+geom_jitter(data=woese,aes(x=longitude_deg,y=latitude_deg,size=woese$`relative abundance of Woesearchaeota (%)`,color=woese$biotope)) +\n  scale_color_manual(values=colors) +\n  xlab('longitude') +\n  ylab('latitude')\nmp2\nmp2<-mp2+theme(legend.position='bottom')\nmp2\nlibrary(Rtsne)\nlibrary(vegan)\nwunifrac<-read.table('unweighted_unifrac_otu_woese_beta.tsv',sep='\\t',header=T,row.names = 1)\nwunifrac[,ncol(wunifrac)]\nwunifrac.m<-as.matrix(wunifrac)\ntsne_model<-Rtsne(wunifrac.m,num_threads = 38,check_duplicates = F)\ndf_tsne<-as.data.frame(tsne_model$Y)\nrownames(df_tsne)<-rownames(wunifrac)\np<-ggplot(df_tsne,aes(x=V1,y=V2))+geom_point()\nmeta<-woese\np\nsalinity<-meta[which(rownames(meta)==rownames(df_tsne)),'empo_2']\nsalinity<-meta[rownames(df_tsne),'empo_2']\nbiotope<-meta[which(rownames(meta)==rownames(df_tsne)),'biotope']\nbiotope<-meta[rownames(df_tsne),'biotope']\ndf_tsne$saline<-salinity\ndf_tsne$biotope<-biotope\n\n##\n##PERMANOVA on unifrac using empo_2 and biotope\n\nempo_2<-adonis(wunifrac.m~salinity)\n\nbiotope_test<-adonis(wunifrac.m~biotope)\n\nlibrary(grid)\ngrob2 <- grobTree(textGrob(\"p-value=0.001\\nR^2=0.325\", x=0.75,  y=0.95, hjust=0,\n                           gp=gpar(col=\"black\", fontface=\"italic\")))\ngrob1<-grobTree(textGrob(\"p-value=0.001\\nR^2=0.138\", x=0.75,  y=0.95, hjust=0,\n                         gp=gpar(col=\"black\", fontface=\"italic\")))\n\np<-ggplot(df_tsne,aes(x=V1,y=V2,color=saline))+geom_point()+xlab('tSNE 1')+ylab('tSNE 2')+annotation_custom(grob1)\n#p<-p+theme(legend.position='none')\np1<-ggplot(df_tsne,aes(x=V1,y=V2,color=biotope))+geom_point()+scale_color_manual(values = colors)+xlab('tSNE 1')+ylab('tSNE 2')+annotation_custom(grob2)\n#p1<-p1+theme(legend.position='none')\n\n##alpha diversity\nalpha_div<-read.table('alpha_div_pd_whole_tree.txt',sep='\\t',header=T,row.names = 1)\nalpha_div$biotope<-meta[rownames(alpha_div),'biotope']\np0<-ggplot(alpha_div,aes(x=biotope,y=PD_whole_tree,fill=biotope))+\n  geom_boxplot()+\n  scale_fill_manual(values = colors)+\n  guides(fill=FALSE)+\n  theme(axis.text.x=element_text(angle=45,hjust = 1,vjust=1))\n## Show data points if n less than and equal 10\nsaline_env_alpha<-alpha_div[which(alpha_div$biotope=='saline environment'),]\nmarine_water_alpha<-alpha_div[which(alpha_div$biotope=='marine water'),]\nsaltmarshes_alpha<-alpha_div[which(alpha_div$biotope=='Saltmarshes'),]\np0<-p0+\n  geom_point(data=saline_env_alpha,aes(x=biotope,y=PD_whole_tree))+\n  geom_point(data=marine_water_alpha,aes(x=biotope,y=PD_whole_tree))+\n  geom_point(data=saltmarshes_alpha,aes(x=biotope,y=PD_whole_tree))\n\nlibrary(gridExtra)\nlay1 <- rbind(\n  c(1,1),\n  c(2,3))\nplots<-list(p0,p,p1)\nfinal_plot<-grid.arrange(grobs=plots,layout_matrix=lay1)\n#ggsave('G:\/woese_revision\/fig1\/fig1_revised.pdf',final_plot,height =10 ,width=14,dpi=300,useDingbats=FALSE)\n#ggsave('G:\/woese_revision\/fig1\/fig1c.pdf',p,height =5 ,width=5,dpi=300,useDingbats=FALSE)\n\n###Post hoc test\na<-aov(PD_whole_tree~biotope,data=alpha_div)\ntk<-TukeyHSD(a)\n###Post hoc abudance test\nabu_t<-aov(woese$`relative abundance of Woesearchaeota (%)`~biotope,data=woese)\ntK_abu_t<-TukeyHSD(abu_t)\n\n##Mantel \nsimple_cor<-meta[8:15]\nsimple_cor$Woesearchaeota<-meta[17]\nsimple_cor<-simple_cor[rownames(alpha_div),]\nmantel_f<-function(otu,env){\n  library(vegan)\n  library(dplyr)\n  vars<-colnames(env)\n  models<-list()\n  for (i in seq_along(vars)){\n    if (vars[i]!='Woesearchaeota'){\n      env_sub<-na.omit(env[vars[i]])\n      otu_sub<-otu[rownames(env_sub),rownames(env_sub)]\n      otu_dist<-as.matrix(otu_sub)\n      dis<-vegdist(env_sub,method='euclidean')\n      model<-vegan::mantel(otu_dist,dis,permutations = 999)\n      name<-vars[i]\n      num<-length(rownames(env_sub))\n      statistic<-model$statistic\n      signif<-model$signif\n      models[[i]]<-data.frame(name=name,statistic=statistic,signif=signif,number=num,row.names=NULL)\n    }\n  }\n  models %>% bind_rows()\n}\n\ninfo<-mantel_f(wunifrac,simple_cor)\n\nlibrary(gridExtra)\nlay1 <- rbind(\n  c(1,1),\n  c(2,2),\n  c(3,4))\nplots<-list(mp2,p0,p,p1)\nfinal_plot<-grid.arrange(grobs=plots,layout_matrix=lay1)\nggsave('fig1.pdf',final_plot,height =18,width=13,units = \"in\")<\/code><\/pre>\n\n\n\n
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