plotting_correlations.R

# disable scientific notation
options(scipen=999)

# install required libraries
# uncomment if you don't have these installed
#install.packages("data.table")

# load needed libraries
library("data.table")

### SET WORKING DIRECTORY ###

setwd("/media/mlb/Seagate Backup Plus Drive/Good_genes/new")

gene_stats <- read.csv("stats_with_ranks.csv")

names(gene_stats)

to_plot <-subset(gene_stats, select=c(Gene_score,Score_corr,Aln_len,Missing_percent,
                                      Prop_pars_inf,GC_content,
                                      Avg_hetero,Avg_boot,Clocklikeness,
                                      Avg_br_len,Avg_p_dist,Avg_entropy,
                                      Slope))



panel.cor <- function(x, y, digits=2, prefix="", cex.cor) 
{
  usr <- par("usr"); on.exit(par(usr)) 
  par(usr = c(0, 1, 0, 1)) 
  r <- cor(x, y) 
  txt <- format(c(r, 0.123456789), digits=digits)[1] 
  txt <- paste(prefix, txt, sep="") 
  if(missing(cex.cor)) cex <- 0.99/strwidth(txt) 
  
  test <- cor.test(x,y) 
  # borrowed from printCoefmat
  Signif <- symnum(test$p.value, corr = FALSE, na = FALSE, 
                   cutpoints = c(0, 0.001, 0.01, 0.05, 0.1, 1),
                   symbols = c("***", "**", "*", ".", " ")) 
  
  text(0.5, 0.5, txt, cex = cex * abs(r)) 
  text(.8, .8, Signif, cex=cex, col=2)
}


png(file="good_genes_corrs.png",width=20*300,height=16*300,res=300)
par(pch=20)
pairs(to_plot, lower.panel=panel.smooth, upper.panel=panel.cor,cex.labels=2)
dev.off()

pdf(file="good_genes_corrs.pdf",width=24,height=20)
par(pch=20)
pairs(to_plot, lower.panel=panel.smooth, upper.panel=panel.cor)
dev.off()