asd

Annotation_Tables/nitrogen.clean.list

+PROKKA_00042
+PROKKA_01867
+PROKKA_00534
+PROKKA_01618
+PROKKA_00311
+PROKKA_00704
+PROKKA_01184
+PROKKA_01185
+PROKKA_01200
+PROKKA_01203
+PROKKA_02313
+PROKKA_02317
+PROKKA_01202
+PROKKA_01201
+PROKKA_02431
+PROKKA_01169
+PROKKA_01163
+PROKKA_01165
+PROKKA_01162
+PROKKA_01164
+PROKKA_01166
+PROKKA_00421
+PROKKA_01174
+PROKKA_01179
+PROKKA_00420
+PROKKA_01178
+PROKKA_01167
+PROKKA_01177
+PROKKA_01168
+PROKKA_01182
+PROKKA_01183

Annotation_Tables/ox.clean.list

+PROKKA_00588
+PROKKA_02121
+PROKKA_02379
+PROKKA_02380
+PROKKA_00334
+PROKKA_00463
+PROKKA_00141
+PROKKA_00956
+PROKKA_00464
+PROKKA_02122
+PROKKA_02123
+PROKKA_00361
+PROKKA_01356
+PROKKA_00965
+PROKKA_00966
+PROKKA_00293
+PROKKA_00852
+PROKKA_00318
+PROKKA_02417
+PROKKA_00473
+PROKKA_00823

Combined/src/plot_comparisons_figure4.R

@@ -126,23 +126,41 @@ x2=tab[!(tab$Signif),]
 #   
 # p=p+theme_bw() +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme(panel.grid.minor = element_blank())
 
+x1= x1 %>%
+  mutate(general_cats= gsub("via ", "via\n", general_cats)) %>%
+  mutate(general_cats= gsub("NADH ", "NADH\n", general_cats))  %>%
+  mutate(general_cats= gsub("stress ", "NADH\n", general_cats))
+
+x1$general_cats = gsub("z_Other", "Other/None", x1$general_cats)
+x1$general_cats = forcats::fct_relevel(x1$general_cats, "Other/None", after = Inf)
+
 p <- ggplot() + 
   #p <- ggplot(m_split,aes(x=cog_name,y=value)) + 
   #p <- ggplot(xxx,aes(x=Class_from_Pwy_association,y=value)) + 
-  geom_point(data=x1,stat="identity",aes(x=general_cats,y=log2FC,fill=type),position= position_jitterdodge(), alpha = 0.6,size=2.0,shape=21) + 
-  geom_point(data=x2,stat="identity",aes(x=general_cats,y=log2FC,fill=type),position= position_jitterdodge(), alpha = 0.4,size=1.5,shape=23)+
+  geom_point(data=x1,stat="identity",aes(x=general_cats,y=log2FC,fill=type),position= position_jitterdodge(), alpha = 0.6,size=2.7,shape=21) + 
+  #geom_point(data=x2,stat="identity",aes(x=general_cats,y=log2FC,fill=type),position= position_jitterdodge(), alpha = 0.4,size=1.5,shape=23)+
   #geom_point(aes(shape=Signif),position = position_jitterdodge(), alpha = 0.7) +
   geom_hline(yintercept = 0,alpha=0.4) +
   geom_hline(yintercept = 2,alpha=0.15) +
   geom_hline(yintercept = -2,alpha=0.15) +
+  theme_bw()+
   #geom_line(aes(group = Geneid))+ 
   ylab("log2FC") + 
-  xlab("Generalised functional category")
+  xlab("Generalised functional category")+
+  scale_fill_manual(values=x[1:2]) +
+  guides(fill= guide_legend(override.aes = list(size=3,alpha=1))) +
+  theme(axis.text.y = element_text(size=13)) +
+  theme(axis.text.x = element_text(size=13, angle=90, hjust=1)) +
+  theme(legend.text = element_text(size=13),
+        legend.title = element_text(size=13),
+        axis.title = element_text( size = 14)) +
+ theme(axis.text.x = ) +
+  theme(panel.grid.minor = element_blank())
   #scale_fill_manual(values=c("red","green"))+
   #scale_shape_manual(name = 'Significance', guide = 'legend',values=c(21,23),labels = c("True","False"))
-p=p+theme_bw() +theme(axis.text.x = element_text(angle = 90, hjust = 1)) + theme(panel.grid.minor = element_blank())
 
-outname.p=paste(REPODIR,"Combined/data/Prot_RNA_comparisons_fig4_all.pdf",sep="/")
+
+outname.p=paste(REPODIR,"Combined/data/Prot_RNA_comparisons_fig4_onlysignif.pdf",sep="/")
 
 pdf(file = outname.p,width=12,height=8)
 p

Combined/src/plot_single_genes.R

+library(tidyverse)
+library(reshape2)
+library(plyr)
+
+REPODIR="/home/mh/Uni_Lux/REPOS/LF_omics_analysis"
+
+incompl=paste(REPODIR,"Combined/data/combined_full_table.tsv",sep="/")
+
+compl=read.csv(incompl,sep="\t")
+
+tpm_all_c=compl[c(1,2,3,4)]
+tpm_all_m=compl[c(1,8,9)]
+
+lfq_scaled_c=compl[c(1,13,14,15,16,17)]
+lfq_scaled_m=compl[c(1,21,22,23)]
+
+lfq_raw_c=compl[c(1,27,28,29,30,31)]
+lfq_raw_m=compl[c(1,35,36,37)]
+
+anno=compl[c(1,seq(42,66))]
+
+plot_single_gene<-function(anno,genelist,tab_c,tab_m,mod_names=T){
+  m_c=melt(tab_c)
+  m_c$variable="continuous"
+  m_m=melt(tab_m)
+  m_m$variable="mineral"
+  mer=rbind(m_c,m_m)
+  
+  #select genes from list
+  ff=mer[mer$Protein.IDs %in% genelist,]
+  
+  ff=join(ff,anno)
+  if(mod_names==T){
+    ff$PROKKA_productname=gsub("alpha|beta|delta|gamma|subunit|,","",ff$PROKKA_productname)  
+    ff$PROKKA_productname=gsub("putative|large","",ff$PROKKA_productname) 
+    ff$PROKKA_productname=gsub(":"," ",ff$PROKKA_productname) 
+    ff$PROKKA_productname=gsub("^\\s|\\s$","",ff$PROKKA_productname)  
+    ff$PROKKA_productname=tolower(ff$PROKKA_productname)
+  }
+  #TODO add list of gene names
+  #p<-ggplot(ff,aes(y=value,x=PROKKA_productname))+
+  p<-ggplot(ff,aes(y=value,x=PROKKA_productname))+  
+    geom_boxplot(aes(fill=variable)) +
+    geom_jitter(aes(color=variable,fill=variable,group=variable),alpha=0.3,position = position_dodge(width=0.75))+
+    theme_bw()+
+    theme(axis.text.x = element_text(angle = 45, hjust = 1))
+  return(p)
+}
+
+pp=plot_single_gene(anno,c("PROKKA_00005","PROKKA_00006"),tpm_all_c,tpm_all_m)
+
+#pp+facet_wrap(~ variable)
+
+
+list_of_choice=mot
+
+outf.dist.tpm="/home/mh/Dropbox/phd_project/Lepto_Paper/submission_final/rtca_tpm.pdf"
+outf.dist.lfq="/home/mh/Dropbox/phd_project/Lepto_Paper/submission_final/rtca_lfq.pdf"
+pdf(file = outf.dist.tpm,width=12,height=8)
+plot_single_gene(anno,list_of_choice,tpm_all_c,tpm_all_m,T)
+dev.off()
+pdf(file = outf.dist.lfq,width=12,height=8)
+plot_single_gene(anno,list_of_choice,lfq_raw_c,lfq_raw_m,T)
+dev.off()
+
+
+pp=plot_single_gene(anno,list_of_choice,tpm_all_c,tpm_all_m)
+plot_single_gene(anno,list_of_choice,lfq_scaled_c,lfq_scaled_m)
+plot_single_gene(anno,list_of_choice,lfq_raw_c,lfq_raw_m)
+
+
+###protstats
+p=join(lfq_raw_c,lfq_raw_m)
+ff=lfq_raw_c[complete.cases(lfq_raw_m),]
+
+ff=lfq_raw_c[-1]
+ff=lfq_raw_m[-1]
+ff=lfq_scaled_c[-1]
+ff=lfq_scaled_m[-1]
+
+ff[!is.na(ff)]<-1
+ff[is.na(ff)]<-0
+
+table(colSums(ff))
+
+###sort by TPM, LFQ_mean
+tpm_arr_c <- compl %>% arrange(desc(tpm_continuous.means))
+tpm_arr_m <- compl %>% arrange(desc(tpm_mineral.means))
+lfq_arr_c <- compl %>% arrange(desc(lfq_cont_raw.means))
+lfq_arr_m <- compl %>% arrange(desc(lfq_min_raw.means))
+
+##plot dists
+plot_dist <- function(tab_c,tab_m){
+  m_c=melt(tab_c)
+  m_c$variable="continuous"
+  m_m=melt(tab_m)
+  m_m$variable="mineral"
+  mer=rbind(m_c,m_m)
+  print(dim(m_c))
+  print(dim(m_m))
+  print(dim(mer))
+  p <- ggplot(mer,aes(x=log2(value),fill=variable))+
+    geom_density(alpha=0.5)+
+    theme_bw()
+  return(p)
+}
+plot_dist(tpm_all_c,tpm_all_m)
+plot_dist(lfq_scaled_c,lfq_scaled_m)
+
+pp=plot_dist(lfq_raw_c,lfq_raw_m)
+
+outf.dist.tpm="/home/mh/Dropbox/phd_project/Lepto_Paper/submission_final/dist_tpm.pdf"
+outf.dist.lfq="/home/mh/Dropbox/phd_project/Lepto_Paper/submission_final/dist_lfq.pdf"
+pdf(file = outf.dist.tpm,width=12,height=8)
+p
+dev.off()
+pdf(file = outf.dist.lfq,width=12,height=8)
+pp
+dev.off()
+
+###prepare input for circos
+out.tpm.c="/home/mh/Uni_Lux/software/circos/circos-0.69/lepto/data/tpm_cont.tsv"
+out.tpm.m="/home/mh/Uni_Lux/software/circos/circos-0.69/lepto/data/tpm_min.tsv"
+out.lfq.c="/home/mh/Uni_Lux/software/circos/circos-0.69/lepto/data/lfq_cont.tsv"
+out.lfq.m="/home/mh/Uni_Lux/software/circos/circos-0.69/lepto/data/lfq_min.tsv"
+
+#get coordinates
+coords=read.csv("/home/mh/Uni_Lux/software/circos/circos-0.69/lepto/data/coords.genes.txt",sep="\t",header=F)
+coords=cbind(Protein.IDs=compl$Protein.IDs,coords)
+
+write.table(file=out.tpm.c,cbind(coords[-1],compl$tpm_continuous.means),sep="\t",col.names = F,row.names=F,quote=F)
+write.table(file=out.tpm.m,data.frame(coords[-1],compl$tpm_mineral.means),sep="\t",col.names = F,row.names=F,quote=F)
+
+#for proteins instead of compl use really unfiltered
+infile.lf.cont.scaled=paste(REPODIR,"Proteomics/data/LFQs_continuous_scaled_unfilt.tsv",sep="/")
+infile.lf.min.scaled=paste(REPODIR,"Proteomics/data/LFQs_mineral_scaled_unfilt.tsv",sep="/")
+#optionally filter
+
+lf.c=read.csv(infile.lf.cont.scaled,sep="\t")
+lf.m=read.csv(infile.lf.min.scaled,sep="\t")
+
+lf.cf=lf.c[apply(lf.c[,c(2,3,4,5,6)],1,function(x) length(which(is.na(x)))=<3),]
+lf.mf=lf.m[apply(lf.m[,c(2,3,4)],1,function(x) length(which(is.na(x)))<2),]
+lf.mf=lf.m
+lf.cf=lf.c
+
+lf.mf=lf.mf[!is.na(lf.mf$means),]
+lf.cf=lf.cf[!is.na(lf.cf$means),]
+
+lf.m=lf.mf
+lf.c=lf.cf
+
+
+
+lf.mm=join(coords,lf.m)
+lf.cc=join(coords,lf.c)
+
+write.table(file=out.lfq.c,data.frame(lf.cc[c(2,3,4)],lf.cc$means),sep="\t",col.names = F,row.names=F,quote=F)
+write.table(file=out.lfq.m,data.frame(lf.mm[c(2,3,4)],lf.mm$means),sep="\t",col.names = F,row.names=F,quote=F)
+
+
+#write out
+#write.table()
+
+
+###GENELISTS
+#for cytochromes bd
+cyt=c("PROKKA_00439",
+      "PROKKA_00440",
+      "PROKKA_00822")
+
+#cytochrome c
+cyt=c("PROKKA_00766",
+      "PROKKA_02381",
+      "PROKKA_02382",
+      "PROKKA_01388",
+      "PROKKA_00732",
+      "PROKKA_00740",
+      "PROKKA_00940",
+      "PROKKA_00730",
+      "PROKKA_00731",
+      "PROKKA_00741",
+      "PROKKA_02378",
+      "PROKKA_00732")
+
+#cytochrome c c551 c552
+cyt=c("PROKKA_01388",
+)
+
+#rubisco
+rub="PROKKA_02524"
+
+#reductive TCA
+rtca=c(
+  "PROKKA_02486",
+  "PROKKA_00876",
+  "PROKKA_01556",
+  "PROKKA_00585",
+  "PROKKA_00579",
+  "PROKKA_00587",
+  "PROKKA_00586",
+  "PROKKA_02458",
+  "PROKKA_02457",
+  "PROKKA_02451",
+  "PROKKA_02456",
+  "PROKKA_00965",
+  "PROKKA_00966",
+  "PROKKA_00581",
+  "PROKKA_00582",
+  "PROKKA_00578",
+  "PROKKA_02453",
+  "PROKKA_02452",
+  "PROKKA_02455",
+  "PROKKA_02450",
+  "PROKKA_02143"
+)
+
+#copper resistance + copper/silver resistance
+cop=c(
+  "PROKKA_02050",
+  "PROKKA_00313",
+  "PROKKA_00752",
+  "PROKKA_00684",
+  "PROKKA_00919",
+  "PROKKA_00166", 
+  "PROKKA_02435",
+  "PROKKA_00920",
+  "PROKKA_01521",
+  "PROKKA_02071",
+  "PROKKA_00918",
+  "PROKKA_02048"
+)
+
+#"heavy metal resistance"
+heav=c(
+  "PROKKA_00076",
+  "PROKKA_00755",
+  "PROKKA_01059",
+  "PROKKA_02594"
+)
+
+
+sulf=c("PROKKA_00960",
+       "PROKKA_00427",
+       "PROKKA_00958",
+       "PROKKA_01477",
+       "PROKKA_00429"
+)
+
+mot=c("PROKKA_00196",
+      "PROKKA_00198",
+      "PROKKA_00199",
+      "PROKKA_00200",
+      "PROKKA_00201",
+      "PROKKA_00202",
+      "PROKKA_00203",
+      "PROKKA_00204",
+      "PROKKA_00205",
+      "PROKKA_00206",
+      "PROKKA_00207",
+      "PROKKA_00209",
+      "PROKKA_00210",
+      "PROKKA_00211",
+      "PROKKA_00212",
+      "PROKKA_00213",
+      "PROKKA_00214",
+      "PROKKA_00215",
+      "PROKKA_00216",
+      "PROKKA_00217",
+      "PROKKA_00218",
+      "PROKKA_00219",
+      "PROKKA_00220",
+      "PROKKA_00221",
+      "PROKKA_00222",
+      "PROKKA_02334",
+      "PROKKA_02335",
+      "PROKKA_02336",
+      "PROKKA_02337",
+      "PROKKA_02339",
+      "PROKKA_02340",
+      "PROKKA_02341",
+      "PROKKA_02342",
+      "PROKKA_02343",
+      "PROKKA_02344",
+      "PROKKA_02345",
+      "PROKKA_02346",
+      "PROKKA_02347",
+      "PROKKA_02348",
+      "PROKKA_02349"
+)
+
+ctax=c("PROKKA_00183",
+       "PROKKA_00227",
+       "PROKKA_01445",
+       "PROKKA_01455",
+       "PROKKA_01548",
+       "PROKKA_01731",
+       "PROKKA_02147",
+       "PROKKA_00223",
+       "PROKKA_00224",
+       "PROKKA_00225",
+       "PROKKA_00226",
+       "PROKKA_00228",
+       "PROKKA_00229",
+       "PROKKA_00230",
+       "PROKKA_00710",
+       "PROKKA_00799",
+       "PROKKA_02318"
+)

Combined/src/plot_tpm_lfqs_new.R

@@ -68,7 +68,7 @@ fff$general_cats=fff$Manually_assigned_category
 fff[grep("Nitra|Nitro|Ammonia",fff$Manually_assigned_category,ignore.case=T),]$general_cats="Nitrogen metabolism"
 #cytochromes not specified as left out of plot for now
 groupcats_cytochromes="Cytochrome"
-fff[grep(groupcats_cytochromes,fff$Manually_assigned_categor,ignore.case=T),]$general_cats="Cytochromes"
+fff[grep(groupcats_cytochromes,fff$Manually_assigned_category,ignore.case=T),]$general_cats="Cytochromes"
 groupcats_polysaccharides=paste("Cellulose production",
                                 "Extracellular polysaccharide production and export",
                                 "Lipopolysaccharide synthesis",