some more rplots

bin/create_pan_genome_plots.R

@@ -1,8 +1,9 @@
 #!/usr/bin/env Rscript
 # ABSTRACT: Create R plots
 # PODNAME: create_plots.R
-
 # Take the output files from the pan genome pipeline and create nice plots.
+library(ggplot2)
+
 
 mydata = read.table("number_of_new_genes.Rtab")
 boxplot(mydata, data=mydata, main="Number of new genes",
@@ -21,4 +22,38 @@ boxplot(mydata, data=mydata, main="Number of unique genes",
          xlab="Number of genomes", ylab="Number of genes",varwidth=TRUE, ylim=c(0,max(mydata)), outline=FALSE)
 
 mydata = read.table("blast_identity_frequency.Rtab")
-plot(mydata,main="Number of blastp hits with different percentage identity",  xlab="Blast percentage identity", ylab="No. blast results")
+plot(mydata,main="Number of blastp hits with different percentage identity",  xlab="Blast percentage identity", ylab="No. blast results")
+
+
+library(ggplot2)
+conserved = colMeans(read.table("number_of_conserved_genes.Rtab"))
+total = colMeans(read.table("number_of_genes_in_pan_genome.Rtab"))
+
+genes = data.frame( genes_to_genomes = c(conserved,total),
+                    genomes = c(c(1:length(conserved)),c(1:length(conserved))),
+                    Key = c(rep("Conserved genes",length(conserved)), rep("Total genes",length(total))) )
+
+ggplot(data = genes, aes(x = genomes, y = genes_to_genomes, group = Key, linetype=Key)) +geom_line()+
+theme_classic() +
+ylim(c(1,max(total)))+
+xlim(c(1,length(total)))+
+xlab("No. of genomes") +
+ylab("No. of genes")+ theme_bw(base_size = 16) +  theme(legend.justification=c(0,1),legend.position=c(0,1))+
+ggsave(filename="conserved_vs_total_genes.png", scale=1)
+
+######################
+
+unique_genes = colMeans(read.table("number_of_unique_genes.Rtab"))
+new_genes = colMeans(read.table("number_of_new_genes.Rtab"))
+
+genes = data.frame( genes_to_genomes = c(unique_genes,new_genes),
+                    genomes = c(c(1:length(unique_genes)),c(1:length(unique_genes))),
+                    Key = c(rep("Unique genes",length(unique_genes)), rep("New genes",length(new_genes))) )
+
+ggplot(data = genes, aes(x = genomes, y = genes_to_genomes, group = Key, linetype=Key)) +geom_line()+
+theme_classic() +
+ylim(c(1,max(unique_genes)))+
+xlim(c(1,length(unique_genes)))+
+xlab("No. of genomes") +
+ylab("No. of genes")+ theme_bw(base_size = 16) +  theme(legend.justification=c(1,1),legend.position=c(1,1))+
+ggsave(filename="unique_vs_new_genes.png", scale=1)

dist.ini

@@ -1,5 +1,5 @@
 name    = Bio-Roary
-version = 3.2.4
+version = 3.2.5
 author  = Andrew J. Page <ap13@sanger.ac.uk>
 license = GPL_3
 copyright_holder = Wellcome Trust Sanger Institute