How to calculate ROC curves

I will make a short tutorial about how to generate ROC curves and other statistics after running rDock molecular docking (for other programs such as Vina or Glide, just a little modification on the way dataforR_uq.txt file is interpreted will make it work, see below).

I assume all of you are familiar with what ROC curves are, what are they for and how they are made.
Just in case, a very brief summary would be:
  • ROC curves are graphic representations of the relation existing between the sensibility and the specificity of a test. It is generated by plotting the fraction of true positives out of the total actual positives versus the fraction of false positives out of the total actual negatives.
  • In our case, we will use it for checking whether a docking program is able to select active ligands with respect to inactive ligands (decoys) and whether it is able to select these active ligands in the top % of a ranked database.
  • R Library ROCR is mandatory (try with command install.packages("ROCR") in R before downloading from source).

The example selected for this tutorial is a system from the DUD benchmark set, "hivpr" or "hiv protease".
These are the files you will need (all can be downloaded in this Dropbox shared folder):
  • List of active ligands (ligands.txt)
  • List of inactive ligands (decoys.txt).
  • Output file with the docked poses of each ligand with the corresponding docking scores (hivpr_all_results.sd.gz).
  • R script with all the R commands in this tutorial (ROC_curves.R).
Before getting into R, the resulted docked poses have to be filtered out for only having the best pose for each ligand (the smallest score - or highest in negative value). To do so run:

NOTE: sdsort and sdreport are really useful tools for managing sd formatted compound collections. They are very user-friendly and free to download. They are provided along with rDock software in rDock website.

gunzip hivpr_all_results.sd.gz
sdsort -n -s -fSCORE hivpr_all_results.sd | sdfilter -f'$_COUNT == 1' >  hivpr_1poseperlig.sd

#sdsort with -n and -s flags will sort internally each ligand by increasing score and sdfilter will get only the first entry of each ligand.

sdreport -t hivpr_1poseperlig.sd | awk '{print $2,$3,$4,$5,$6,$7}' > dataforR_uq.txt
#sdreport will print all the scores of the output in a tabular format and, with command awk, we will format the results

This dataforR_uq.txt (also in Dropbox folder) file must contain one entry per ligand with the docked scores (what R will use to rank and plot the ROC curves).
Then, run the following commands in R for plotting the ROC curves:

#load ROCR
library(ROCR);
 
#load ligands and decoys
lig <- unique(read.table("ligands.txt")[,1]);
dec <- unique(read.table("decoys.txt")[,1]);
 
#load data file from docking
uniqRes <- read.table("dataforR_uq.txt",header=T);
 
#change colnames
colnames(uniqRes)[1]="LigandName";
 
#add column with ligand/decoy info
uniqRes$IsActive <- as.numeric(uniqRes$LigandName %in% lig)

#define ROC parameters 

#here INTER is selected to compare between ligands using rDock SCORE.INTER
#this could be changed for also running with other programs
predINTERuq <- prediction(uniqRes$INTER*-1, uniqRes$IsActive)
perfINTERuq <- performance(predINTERuq, 'tpr','fpr')

#plot in jpg format with a grey line with theoretical random results

jpeg("hivpr_Rinter_ROC.jpg")
plot(perfINTERuq,main="hivpr - ROC Curves",col="blue")
abline(0,1,col="grey")
dev.off()

Which will give us the following plot:


Afterwards, other useful statistics such as AUC or Enrichment factors can also be calculated:

#AUC (area under the curve)
auc_rdock <- performance(predINTERuq, "auc")
auc.area_rdock <- slot(auc_rdock, "y.values")[[1]]
cat("AUC: \n")
cat(auc.area_rdock)
cat("\n\n")


AUC: 
0.7700965
 
#Enrichment Factors
EF_rdock <- perfINTERuq@y.values[[1]]/perfINTERuq@x.values[[1]]
EF_rdock_1 <- EF_rdock[which(perfINTERuq@x.values[[1]] > 0.01)[1]]
EF_rdock_20 <- EF_rdock[which(perfINTERuq@x.values[[1]] > 0.2)[1]]
cat("Enrichment Factor top1%:\n")
cat(EF_rdock_1)
cat("\n\n")


Enrichment Factor top1%:
11.11817
 

cat("Enrichment Factor top20%:\n")
cat(EF_rdock_20)
cat("\n\n")

Enrichment Factor top20%:
3.200686

 
Moreover, a good analysis of these curves is to re-plot them in semilogarithmic scale (x axis in logarithmic scale). This way, one can focus on the early enrichment of the database and have a more detailed view of the selected actives in the top % of all the ligands.

jpeg("hivpr_semilog_ROC.jpg")
rdockforsemilog=perfINTERuq@x.values[[1]]
rdockforsemilog[rdockforsemilog < 0.0005]=0.0005
plot(rdockforsemilog,perfINTERuq@y.values[[1]],type="l",xlab="False Positive Rate", ylab="True Positive Rate",xaxt="n", log="x", col="blue",main="hivpr - Semilog ROC Curves")
axis(1, c(0,0.001,0.01,0.1,1))
x<-seq(0,1,0.001)
points(x,x,col="gray",type="l")
dev.off()

Obtaining the following semi-logarithmic ROC curves: