# Processing binny and CoAssembly results

The previous tutorial [here]() ended with the generation of bins and co Assembly but what now?

Now we can look at how the bins are distributed at each time point.

For that we use `R` with the multitool library `tidyverse`

library(tidyverse)

## Loading the files

We will be loading the individual library mapping to the coassembly first:

covlist<-list.files("Covstats_FS2C_B1/", full.names = T)

cov<-list()
for (i in 1:length(covlist)){
  cov[[i]]<-read_tsv(covlist[i]) %>% 
    rename(ID=`#ID`) %>% 
    separate(ID, into = "ID", sep=" ",extra = "drop") %>% 
    mutate(Lib=paste0("D", i-1))
}

cov<-reduce(cov, rbind)

Then the contig distribution summary generated by binny

bin<-read_tsv("contig_data.tsv")%>% 
  separate(bin, into=c("binID1","binID2"),sep="\\.") %>% 
  type_convert

Then the GTDB-Tk summary results

tax<-read_tsv("gtdbtk/gtdbtk.bac120.summary.tsv") %>% 
  separate(classification, into=c("K","P","C","O","F","G","S"), sep=";") %>% 
  mutate(user_genome=gsub("binny_", "", user_genome)) %>% 
  separate(user_genome, into=c("binID1","binID2","C","P"), extra="drop") %>% 
  mutate(binID2=as.numeric(binID2),
         binID1=gsub("R0","R",binID1),
         binID1=gsub("I0","I",binID1))

## Plotting

What a metagenome looks like

cov %>% 
  rename(contig=ID) %>% 
  left_join(bin) %>% 
  left_join(tax) %>% 
  ggplot(aes(y=Avg_fold,x=Ref_GC,col=O))+
  geom_point()+
  facet_wrap(~Lib)+
  scale_y_log10()


How bin distribution varies accross libraries (here they don't varie much)

cov %>% 
  rename(contig=ID) %>% 
  select(contig,Lib,Avg_fold) %>% 
  spread(Lib,Avg_fold) %>% 
  left_join(bin) %>% 
  left_join(tax) %>% 
  ggplot(aes(x=D0,y=D1,col=O))+
  geom_point()+
  scale_y_log10()+
  scale_x_log10()


Then the average bin coverage (average fold) mapping within each library

cov %>% 
  rename(contig=ID) %>% 
  left_join(bin) %>% 
  left_join(tax) %>% 
  ggplot(aes(x=paste(binID1,binID2),y=Avg_fold,col=O))+
  geom_boxplot()+
  scale_y_log10()+
  facet_wrap(~Lib)


And the average coverage for each bin accross time

cov %>% 
  rename(contig=ID) %>% 
  left_join(bin) %>% 
  left_join(tax) %>% 
  group_by(Lib) %>% 
  mutate(prop_cov=prop.table(Avg_fold)*100) %>% 
  ggplot(aes(x=Lib,y=prop_cov,col=O))+
  geom_boxplot()+
  facet_wrap(~S, scales = "free")


###### tags: `tutorials`,`R`, `Metagenomics`,`Binning`