================================================================= Please cite: coming soon
This project assess the utility of data integration from multiple drug information layers.
Hypothesis: Better characterization of compound mechanism of action (MoA) from integrative pharmacogenomics.
Impact: Identifying mechanism for compounds with unknown MoA in early stages of drug development without the need of sophisticated info (side effects or pharmacological indications...)
http://www.ncbi.nlm.nih.gov/pubmed/24464287
The following steps will reproduce the output files (figure, tables...) mentioned in the main manuscript. The script will be using data files such as:
- Drug-target (benchmark) from CHEMBL and CTRP
- Sensitivity measurements (drugs x cell lines) from CTRPv2 and NCI-60
- Transcriptomic data from the LINCS database http://lincs.hms.harvard.edu/ created using PharmacoGx package https://cran.r-project.org/web/packages/PharmacoGx/index.html
- main-ctrpv-lincs.R and
- main-nci60-lincs.R
(this will execute the following R codes):
# process the raw data, find common drugs...
preprocessInput.R
# remove problematic drugs and missing data
sensitivityData.R
# get the structural fingerprints (extended-connectivity descriptors from RCDK)
structureData.R
# remove problematic drugs and missing data and find drug names from LINCS metadata
perturbationData.R
# Get the similarity matrix from structure (tanimoto metric)
constStructureLayer.R
# Get the similarity matrix from sensitivity (pearson metric), rescale to 0-1 with function in SNF
constSensitivityLayer.R
# Get the similarity matrix from perturbation (pearson metric), rescale to 0-1 with SNF affinitymatrix
constPerturbationLayer.R
# Integrate all 3 layers using SNFtools
integrateStrctSensPert.R
# ATC code gold standard benchmark from CHEMBL
ATCbench.R
# Drug-target benchmarks from CHEMBL and CTRPv2
drugTargetBench.R
# get the pairs of drugs sharing the same target (0 or 1)
generateDrugPairs.R
# Compute concordance index between benchmark data and similarity network data
compConcordIndx.R
# Get the p-value between concordance indices
predPerf.R
# Generate ROC plots and AUC values
generateRocPlot.R
# Community clustering from apcluster package in R
communityGen.R
# Network based on exemplar drugs from apcluster
main-network-generation.R
A Docker Terminal Environment has been setup with all dependencies installed. Access it by installing Docker Engine and running the following lines of code:
#clone image
docker pull gosuzombie/dnf
#run image in interactive command line
docker run -it gosuzombie/dnf
Alternatively, one can setup the Docker environment by using the provided Dockerfile. Instructions can be found here