Installation

source("http://bioconductor.org/biocLite.R")
biocLite("rcellminer")
biocLite("rcellminerData")

Getting Started

Load rcellminer and rcellminerData packages:

library(rcellminer)
library(rcellminerData)

A list of all accessible vignettes and methods is available with the following command.

help.search("rcellminer")

Drug Visualization

Because the values of within rcellminer have been z-score transformed this allows different data within the package to be compared. Often, it is useful for researchers to visualize multiple data profiles next to each other in order to visually identify patterns. Below are examples for the visualization of various profiles: single drugs and multiple drugs, as well as, molecular profiles and combinations of drug and molecular profiles.

# Get Cellminer data
drugAct <- exprs(getAct(rcellminerData::drugData))
molData <- getMolDataMatrices()

# Two drugs 
nsc <- c("3284", "739")
plots <- c("drug", "drug") 
plotCellMiner(drugAct, molData, plots, nsc, NULL)

# Just drug
nsc <- "94600"
plots <- c("drug") 
plotCellMiner(drugAct, molData, plots, nsc, NULL)

# Just expression
gene <- "TP53"
plots <- c("exp") 
plotCellMiner(drugAct, molData, plots, NULL, gene)

# Two genes 
# NOTE: subscript out of bounds Errors likely mean the gene is not present for that data type
gene <- c("TP53", "MDM2")
plots <- c("exp", "mut", "exp") 
plotCellMiner(drugAct, molData, plots, NULL, gene)

# Gene and drug to plot 
nsc <- "94600"
gene <- "TP53"
plots <- c("mut", "drug", "cop") 
plotCellMiner(drugAct, molData, plots, nsc, gene)

# Get CellMiner data
drugAct <- exprs(getAct(rcellminerData::drugData))

# Select drugs using NSC IDs
drugs <- "26273 39367 39368 105546 120958 255523 284751 289900 736740 743891 752330"

drugs <- strsplit(drugs, " ")[[1]]
drugAct <- drugAct[drugs,]
mainLabel <- paste("Drug Set: 1, Drugs:", length(drugs), sep=" ")

plotDrugSets(drugAct, drugs, mainLabel) 

Structure Visualization

The structures of the CellMiner compounds are visualized using the plotStructuresFromNscs method. This is a basic method that is a wrapper for functionality in the rcdk package. The first parameter is a user-defined string that will serve as a label for the compound, and the second parameter is a SMILES string for the componud of interest. Here we use the getSmiles method to retrieve the SMILES string for topotecan, a well-known topoisomerase inhibitor.

plotStructuresFromNscs("Topotecan", getSmiles("609699"))

Generate drug set

Generate a set of drugs to be pairwise compared. Here we will compare a set of 100 compounds to a drug of interest MK2206 (an AKT inhibitor). We provide a name “MK2206” and the SMILES-based structure retrieved from PubChem.

# Load sqldf
library(sqldf)

# Set up necessary data
## Compound annotations
df <- as(featureData(getAct(rcellminerData::drugData)), "data.frame")
## Drug activities 
drugAct <- exprs(getAct(rcellminerData::drugData))
## Molecular profiling data
molData <- getMolDataMatrices() 

# Example filter on particular properties of the compounds
tmpDf <- sqldf("SELECT NSC, SMILES 
                             FROM df 
                             WHERE SMILES != ''")

# Compare against the 100 NSCs for demonstration
ids <- head(tmpDf$NSC, 100)
smiles <- head(tmpDf$SMILES, 100)

# All public
#ids <- tmpDf$nsc
#smiles <- tmpDf$smiles

drugOfInterest <- "MK2206"
smilesOfInterest <- "C1CC(C1)(C2=CC=C(C=C2)C3=C(C=C4C(=N3)C=CN5C4=NNC5=O)C6=CC=CC=C6)N"

# Make a vector of all the compounds to be pairwise compared 
ids <- c(drugOfInterest, ids)
smiles <- c(smilesOfInterest, smiles)

Run the comparison

# Run fingerprint comparison 
results <- compareFingerprints(ids, smiles)

Visualize structures

View the similar structures. The first drug in the results will be the drug of interest and the subsequent drugs will compounds related by structure in decreasing similarity.

NOTE: All compounds in CellMiner are uniquely identified by NSC identifiers.

# Plot top 2 results 
resultsIdx <- sapply(names(results)[2:3], function(x) { which(tmpDf$NSC == x) })
resultsIds <- names(results)[2:3]
resultsSmiles <- tmpDf$SMILES[resultsIdx]

resultsIds <- c(drugOfInterest, resultsIds)
resultsSmiles <- c(smilesOfInterest, resultsSmiles)

plotStructuresFromNscs(resultsIds, resultsSmiles, 
                                             titleCex=0.5, mainLabel="Fingerprint Results")

Visualize drug activity

Plot the activity of the compounds across the NCI-60

nscs <- names(results)[2:3]
plotCellMiner(drugAct=drugAct, molData=molData, plots=rep("drug", length(nscs)), nscs, NULL)

Mechanism of action (MOA)

rcellminer provides information on the mechanism of action (MOA) for a number of compounds in the database. This information gives users information on the specific biochemical interactions that a given compound participates in to produce its effect.

drugAnnot <- as(featureData(getAct(rcellminerData::drugData)), "data.frame")

knownMoaDrugs <- unique(c(getMoaToCompounds(), recursive = TRUE))

knownMoaDrugInfo <- data.frame(NSC=knownMoaDrugs, stringsAsFactors = FALSE)

knownMoaDrugInfo$Name <- drugAnnot[knownMoaDrugInfo$NSC, "NAME"]

knownMoaDrugInfo$MOA <- vapply(knownMoaDrugInfo$NSC, getMoaStr, character(1))

# Order drugs by mechanism of action.
knownMoaDrugInfo <- knownMoaDrugInfo[order(knownMoaDrugInfo$MOA), ]

Working with GI50 (Growth Inhibition 50) data

Additionally, rcellminer provides GI50 (growth inhibition 50%) values for the compounds in the database. GI50 values are similar to IC50 values, which are the concentrations that cause 50% growth inhibition, but have been renamed to emphasize the correction for the cell count at time zero. Further discussion on the assay used available on the DTP website.

negLogGI50Data <- getDrugActivityData(nscSet = knownMoaDrugInfo$NSC)

gi50Data <- 10^(-negLogGI50Data)

knownMoaDrugAct <- as.data.frame(cbind(knownMoaDrugInfo, gi50Data), stringsAsFactors = FALSE)

# This table can be written out to a file
#write.table(knownMoaDrugAct, file="knownMoaDrugAct.txt", quote=FALSE, sep="\t", row.names=FALSE, col.names=TRUE, na="NA")