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library(oncoPredict)
#>
#This script provides an example of how to use calcPhenotype() for drug response prediction as well as its optional #parameters.
#Set the seed for reproducibility.
set.seed(12345)
#Determine parameters for calcPhenotype() function.
#Read training data for GDSC (expression and response)
#_______________________________________________________
#GDSC1 Data
#Read GDSC training expression data. rownames() are genes and colnames() are samples (cell lines/cosmic ids).
#trainingExprData=(readRDS("GDSC1_Expr.rds"))
#dim(trainingExprData) #17419 958
#Read GDSC1 response data. rownames() are samples (cell lines, cosmic ids), colnames() are drugs.
#trainingPtype = (readRDS("GDSC1_Res.rds"))
#dim(trainingPtype) #958 367 For GDSC1
#trainingPtype<-trainingPtype[,1:2] #Just 2 drugs for the vignette.
#GDSC2 Data
#Read GDSC training expression data. rownames() are genes and colnames() are samples.
#trainingExprData=readRDS(file='GDSC2_Expr.rds')
#dim(trainingExprData) #17419 805
#Read GDSC2 response data. rownames() are samples, colnames() are drugs.
trainingPtype = readRDS(file = "GDSC2_Res.rds")
#dim(trainingPtype) #805 198
#GDSC2 expression data for the vignette (it's a much smaller sampling)
trainingExprData=readRDS(file='GDSC2_Expr_short.rds')
#dim(trainingExprData) #1000 400
#IMPORTANT note: here I do e^IC50 since the IC50s are actual ln values/log transformed already, and the calcPhenotype function Paul #has will do a power transformation (I assumed it would be better to not have both transformations)
trainingPtype<-exp(trainingPtype)
#Or read training data for CTRP (expression and response)
#_______________________________________________________
#Read CTRP training expression data. rownames() are genes and colnames() are samples (cell lines/cosmic ids).
#trainingExprData = readRDS(file = "CTRP2_Expr.rds")
#dim(trainingExprData) #51847 829
#Read CTRP training response data. rownames() are samples (cell lines, cosmic ids), colnames() are drugs.
#trainingPtype = readRDS(file = "CTRP2_Res.rds")
#dim(trainingPtype) #829 545
#Test data.
#_______________________________________________________
#Read testing data as a matrix with rownames() as genes and colnames() as samples.
testExprData=as.matrix(read.table('prostate_test_data.txt', header=TRUE, row.names=1))
#dim(testExprData) #20530 550
#Additional parameters.
#_______________________________________________________
#batchCorrect options: "eb" for ComBat, "qn" for quantiles normalization, "standardize", or "none"
#"eb" is good to use when you use microarray training data to build models on microarray testing data.
#"standardize is good to use when you use microarray training data to build models on RNA-seq testing data (this is what Paul used in the 2017 IDWAS paper that used GDSC microarray to impute in TCGA RNA-Seq data, see methods section of that paper for rationale)
batchCorrect<-"eb"
#Determine whether or not to power transform the phenotype data.
#Default is TRUE.
powerTransformPhenotype<-TRUE
#Determine percentage of low varying genes to remove.
#Default is 0.2 (seemingly arbitrary).
removeLowVaryingGenes<-0.2
#Determine method to remove low varying genes.
#Options are 'homogenizeData' and 'rawData'
#homogenizeData is likely better if there is ComBat batch correction, raw data was used in the 2017 IDWAS paper that used GDSC microarray to impute in TCGA RNA-Seq data.
removeLowVaringGenesFrom<-"homogenizeData"
#Determine the minimum number of training samples required to train on.
#Note: this shouldn't be an issue if you train using GDSC or CTRP because there are many samples in both training datasets.
#10, I believe, is arbitrary and testing could be done to get a better number.
minNumSamples=10
#Determine how you would like to deal with duplicate gene IDs.
#Sometimes based on how you clean the data, there shouldn't be any duplicates to deal with.
#Options are -1 for ask user, 1 for summarize by mean, and 2 for disregard duplicates
selection<- 1
#Determine if you'd like to print outputs.
#Default is TRUE.
printOutput=TRUE
#Indicate whether or not you'd like to use PCA for feature/gene reduction. Options are 'TRUE' and 'FALSE'.
#Note: If you indicate 'report_pca=TRUE' you need to also indicate 'pca=TRUE'
pcr=FALSE
#Indicate whether you want to output the principal components. Options are 'TRUE' and 'FALSE'.
report_pc=FALSE
#Indicate if you want correlation coefficients for biomarker discovery. These are the correlations between a given gene of interest across all samples vs. a given drug response across samples.
#These correlations can be ranked to obtain a ranked correlation to determine highly correlated drug-gene associations.
cc=FALSE
#Indicate whether or not you want to output the R^2 values for the data you train on from true and predicted values.
#These values represent the percentage in which the optimal model accounts for the variance in the training data.
#Options are 'TRUE' and 'FALSE'.
rsq=FALSE
#Indicate percent variability (of the training data) you'd like principal components to reflect if pcr=TRUE. Default is .80
percent=80
#Run the calcPhenotype() function using the parameters you specified above.
#__________________________________________________________________________________________________________________________________
wd<-tempdir()
savedir<-setwd(wd)
calcPhenotype(trainingExprData=trainingExprData,
trainingPtype=trainingPtype,
testExprData=testExprData,
batchCorrect=batchCorrect,
powerTransformPhenotype=powerTransformPhenotype,
removeLowVaryingGenes=removeLowVaryingGenes,
minNumSamples=minNumSamples,
selection=selection,
printOutput=printOutput,
pcr=pcr,
removeLowVaringGenesFrom=removeLowVaringGenesFrom,
report_pc=report_pc,
cc=cc,
percent=percent,
rsq=rsq)
#>
#> 20 gene identifiers overlap between the supplied expression matrices...
#>
#> Found2batches
#> Adjusting for0covariate(s) or covariate level(s)
#> Standardizing Data across genes
#> Fitting L/S model and finding priors
#> Finding parametric adjustments
#> Adjusting the Data
#>
#> 4 low variabilty genes filtered.
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 1 of 198
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#> Fitting Ridge Regression model...
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#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 145 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 146 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 147 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 148 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 149 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 150 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 151 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 152 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 153 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 154 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 155 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 156 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 157 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 158 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 159 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 160 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 161 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 162 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 163 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 164 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 165 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 166 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 167 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 168 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 169 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 170 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 171 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 172 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 173 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 174 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 175 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 176 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 177 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 178 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 179 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 180 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 181 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 182 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 183 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 184 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 185 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 186 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 187 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 188 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 189 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 190 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 191 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 192 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 193 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 194 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 195 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 196 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 197 of 198
#>
#> Fitting Ridge Regression model...
#>
#> Calculating predicted phenotype...
#>
#> Done making prediction for drug 198 of 198
#> Warning in write.csv(DrugPredictions_mat, file =
#> "./calcPhenotype_Output/DrugPredictions.csv", : attempt to set 'col.names'
#> ignored
#If pcr is performed, you can view a drug's first two principal components (and so on) using the code below.
#View(load('./calcPhenotype_Output/Vinblastine_1004.RData'))
#View(pcs[,1,1]) #The first pc.
#View(pcs[,1,2]) #The second pc. These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.