General Summary

This plot summarizes the contamination and completion of each individual sample.

# plot the log file with all three variables in the same same graph
data_long <- melt(log, id.vars=c("Sample", "Passed", "Marker_lineage"))  # convert to long format
data_long$value <- as.numeric(as.character(data_long$value))
p<-ggplot(data_long, aes(fill=Passed, x=Sample, y=value))+
  geom_bar(stat="identity")+
  theme_classic()+
  ggtitle("CheckM")+
  xlab("Isolates")+
  ylab("")+
  theme(axis.text.x = element_blank())+
  scale_fill_manual(values=c("yes"="#62b587","no"="grey"))+
  facet_wrap(~variable, scale = "free_y", nrow=3)
ggplotly(p)

Stats Summary

This plot summarizes the assembly stats into a histogram. Samples are colored by whether or not they pass all the filter requirements.

# plot the log file with all three variables in the same same graph
data_long <- melt(checkm, id.vars=c("Sample", "Passed"))  # convert to long format
data_long$value <- as.numeric(as.character(data_long$value))
p <- ggplot(data_long, aes(fill=Passed, x=Sample, y=value))+
  geom_bar(stat="identity")+
  theme_classic()+
  ggtitle("CheckM")+
  xlab("Isolates")+
  ylab("")+
  theme(axis.text.x = element_blank())+
  scale_fill_manual(values=c("yes"="#62b587","no"="grey"))+
  theme(panel.spacing.x=unit(0, "lines") , panel.spacing.y=unit(1,"lines"))+
  facet_wrap(~variable, scale = "free_y", nrow = 5)
ggplotly(p, height = 1500, width=1500)

Sample-by-Sample Statistics

The following plots show information for each individual sample.

checkm$contigs <- as.numeric(as.character(checkm$contigs))
p<-ggplot(checkm, aes(y=contigs, x=Sample, fill=Passed))+
  geom_bar(stat="identity")+
  theme_classic()+
  ggtitle("CheckM: Number of Contigs")+
  xlab("Isolates")+
  ylab("")+
  guides(fill=guide_legend(title="Passed"))+
  theme(axis.text.x = element_blank())+
  scale_fill_manual(values=c("yes"="#62b587","no"="grey"))
ggplotly(p)
checkm$`N50 contigs` <- as.numeric(as.character(checkm$`N50 contigs`))
p<-ggplot(checkm, aes(y=`N50 contigs`, x=Sample, fill=Passed))+
  geom_bar(stat="identity")+
  theme_classic()+
  ggtitle("CheckM: N50 (contigs)")+
  xlab("Isolates")+
  ylab("")+
  guides(fill=guide_legend(title="Passed"))+
  theme(axis.text.x = element_blank())+
  scale_fill_manual(values=c("yes"="#62b587","no"="grey"))
ggplotly(p)
checkm$`Genome size` <- as.numeric(as.character(checkm$`Genome size`))
p<-ggplot(checkm, aes(y=`Genome size`, x=Sample, fill=Passed))+
  geom_bar(stat="identity")+
  theme_classic()+
  ggtitle("CheckM: Genome size")+
  xlab("Isolates")+
  ylab("")+
  guides(fill=guide_legend(title="Oassed"))+
  theme(axis.text.x = element_blank())+
  scale_fill_manual(values=c("yes"="#62b587","no"="grey"))
ggplotly(p)
checkm$GC <- as.numeric(as.character(checkm$GC))
p<-ggplot(checkm, aes(y=GC, x=Sample, fill=Passed))+
  geom_bar(stat="identity")+
  theme_classic()+
  ggtitle("CheckM: GC")+
  xlab("Isolates")+
  ylab("")+
  guides(fill=guide_legend(title="Passed"))+
  theme(axis.text.x = element_blank())+
  scale_fill_manual(values=c("yes"="#62b587","no"="grey"))
ggplotly(p)
checkm$`Mean contig length` <- as.numeric(as.character(checkm$`Mean contig length`))
p<-ggplot(checkm, aes(y=`Mean contig length`, x=Sample, fill=Passed))+
  geom_bar(stat="identity")+
  ggtitle("CheckM: Mean contig length")+
  theme_classic()+
  xlab("Isolates")+
  ylab("")+
  guides(fill=guide_legend(title="Passed"))+
  theme(axis.text.x = element_blank())+
  scale_fill_manual(values=c("yes"="#62b587","no"="grey"))
ggplotly(p)

Statistic tables

The following tables show the details for each of the assembly quality control parameters

datatable(checkm, rownames=F, filter='top')
datatable(log, rownames=F, filter='top')
datatable(ani, rownames=F, filter='top')