In Minzhe/DIGREsyn: Drug Induced Genomic Residual Effect (DIGRE) model for predicting drug pair synergistic score

knitr::opts_chunk$set(tidy=FALSE, collapse = TRUE, comment = "#>", message=FALSE, error=FALSE, warning=TRUE)

Cite our papers:
"A community computational challenge to predict the activity of pairs of compounds." Nature biotechnology 32.12 (2014): 1213-1222.
"DIGRE: Drug-Induced Genomic Residual Effect Model for Successful Prediction of Multidrug Effects." CPT: pharmacometrics & systems pharmacology 4.2 (2015): 91-97.

Input Data

The DIGRE model takes three forms of input data to predict the compound synergistic effect: the drug treated gene expression data, the drug dose response data and the gene-gene interaction information.

Gene expression profile

This is the gene expression profile (microarray or RNA-Seq) of cell after drug perturbation. User should prepare a dataframe with each column representing one drug, and each row representing one gene. Negative control such as DMSO treatment should be included. The demo data geneExp.demo we provided in the package is from NCI-DREAM challenge competition for predicting drug pairs synergy. OCI-LY3 human diffuse large B-cell lymphoma (DLBCL) cell line was treated by 14 different drugs in its dose of IC20. 24 hours after Perturbation, gene expression level was measured.

library(DIGREsyn)
head(geneExp.demo)

Dose response data

This the dose response data of each drug. User should prepare a dataframe that contains the cell viability reduction values when cell line is treated with drug in two different doses. One drug dose is the IC20 of the drug, therefore the cell viability reduction is always 0.2 for all drugs. The other dose is the double of IC20, this value is infered from the dose response curve of each drug. (This step should done by user himself, DIGRE only accept the processed data not raw dose response curves; currently, the two drug doses are fixed: the IC20 and double of the IC20.) The demo data doseRes.demo we provided in the package contains dose response data of 14 drugs from NCI-DREAM challenge. (Notice: Check the drug name in the dose response data that match exactly the drug name in the gene expression data.)

head(doseRes.demo)

Gene-gene interaction information

This is the gene-gene interaction information need for DIGRE to predict drug interaction effect. User does not need to worry about this if he uses KEGG pathway in DIGREscore function. But if user prefer to use their own knowledge about the gene network involved in the specific cell type, then he will need to prepare this gene-gene interaction table. It should be a dataframe that has two columns with gene SYMBOL names. Each raw represents two connected genes. The interaction is regarded as undirected. The demo data geneNetLymph is a lymphoma-specific gene network refined from lymphoma patients gene expression data.

head(geneNetLymph)

Quick Start

Here we show the basic pipeline of using DIGREsyn to predict drug synergistic score with demo data.

1. Read and profile gene expression data
Read the gene expression data of cells treated with each individual compound, and parse it with the profileGeneExp function. (Notice: Do not need to specify column names and row names by drug names and gene names for gene expression data as duplicated drug names and gene names are common.)

geneExpDiff <- profileGeneExp(geneExp = geneExp.demo)

2. Read and parse gene interaction data (optional)
Read the gene connectivity data to construct the gene network that DIGRE uses to compare compound effects on upstream and downstream genes.

geneNetLymph.mat <- constGeneNet(geneNet = geneNetLymph)

3. Predict compound pair synergistic effect
DIGREscore is the core function that predicts all the possible compound pair synergistic scores and their ranks. (Notice: for the following, we are using a default cut off of 0.6 for gene expression difference, but you can also set your own preferred value (from 0 to 1).)

pred.res <- DIGREscore(geneExpDiff = geneExpDiff, doseRes = doseRes.demo, pathway = "GeneNet", geneNet = geneNetLymph.mat, fold = 0.6)

Visualization

The DIGREvis function is for visualizing prediction results. The heatmap displays the predicted synergistic scores of all the compound pairs. Deep color indicates high synergistic score. The bar plot shows the top predicted synergistic drug pairs.

Heatmap

vis.heat <- DIGREvis(pred.pair = pred.res$scoreRank, type = "heat")
plot(vis.heat)

Bar plot

vis.bar <- DIGREvis(pred.pair = pred.res$scoreRank, type = "bar")
plot(vis.bar)

Session info

sessionInfo()

Minzhe/DIGREsyn documentation built on May 7, 2019, 4:58 p.m.