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Part3_ML_modelling.Rmd
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---
title: "Applying ML-based approachesto identify predictors of survival - Case study using Benedicte's cohort"
author: "Dimitrios Kleftogiannis"
date: "2024-03-12"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
### Utility
This code is part of the study titled "Automated cell type annotation and exploration of single-cell signalling dynamics using mass cytometry".
The utility of this code is to apply different supervised machine learning based approaches on the data described at "Early response evaluation by single cell signaling profiling in acute myeloid leukemia", doi: 10.1038/s41467-022-35624-4
The aim is to predict long-term survivors from short-term survivors at time of diagnosis using signaling data.
### Contact
Comments and bug reports are welcome, please email: Dimitrios Kleftogiannis ([email protected])
We are also interested to know about how you have used our framework, including any improvements that you have implemented.
You are free to modify, extend or distribute our source codes, as long as our copyright notice remains unchanged and included in its entirety.
### License
This code is licensed under the MIT License.
Copyright 2023, University of Bergen (UiB) and NeuroSysMed, Norway
## Load R libraries.
```{r load packages, echo=TRUE, eval=TRUE, error=TRUE, warning=FALSE,cache=TRUE}
library(ggplot2)
library(readxl)
library(dplyr)
library(ggridges)
library(MASS)
library(matrixStats)
library(reshape2)
library(ggthemes)
library(RColorBrewer)
library(ggbeeswarm)
library(DMwR)
library(xgboost)
library(pROC)
library(caret)
library(glmnet)
```
## Setting workspace and loading in-house functions
```{r initialise workspace,echo=TRUE,eval=TRUE,error=TRUE, warning=FALSE,cache=TRUE}
#load the functions
setwd("/Users/kleftogi/Desktop/CyTOF_paper_corrections")
set.seed(1234)
```
## Load the annotated patient data from Benedicte's cohort and generate feature vector using medians
The signaling markers are used to generate features.
We use the median expression of all signaling markers in the panel. This is a pretty standard approach, and although naive it is our baseline approach.
```{r load patient dataset and compute medians,echo=TRUE,eval=FALSE,error=TRUE, warning=FALSE,cache=TRUE}
#patient data annotated before treatment
load('patient_annotated.RDa')
patient_data <- data_clust
#retrieve the markers of interest - for this example we focus on signaling markers only
signal.col.names <- c("pAxl","CyclinB1","pNFkB","pErk","pSTAT1",
"pP38","pSTAT3","pCREB","pHist3","Casp3",
"pSTAT5","p4EBP1","pAkt","pRB","pS6")
patient_data <- patient_data[,c(signal.col.names,"Sample","cell_id","labels")]
####################################################################################################
#the following piece of code computes the median value per marker per patient, which is essential the feature vector that will be used for experimentation with machine learning approaches
#load the clinical info from external files --> we are not allowed to share the clinical info; please contact the authors
filename <- "AML_Benedicte_cohort/barcode_metadata_leukemia_cohort.xlsx"
#read the samples
md <- read_excel(filename)
patient_samples <- md[md$condition=='0h',]
file_names_patients <- patient_samples$file_name
file_names_patients <- paste('AML_Benedicte_cohort/raw_data/',file_names_patients,sep='')
file_barcode_patients <- patient_samples$barcode
metadata_filename <- "AML_Benedicte_cohort/leukemia_cohort_info.xlsx"
md_0h <- read_excel(metadata_filename)
start_time = Sys.time()
patient_feature_vector <- data.frame()
for(currentPatient in 1:nrow(patient_samples)){
myPatientID <- patient_samples[currentPatient,'patient_id']
a <- which(md_0h$Patient_nr_cytobank==myPatientID$patient_id)
if(length(a)>0){
survivalTime <- md_0h[a,"5-year survival (days)"]
chromAbber <- md_0h[a,'Karyotype']
chromAbber <- chromAbber$Karyotype
if(chromAbber=='NA'){
chromAbber <- NA
}
mutFLT3 <- md_0h[a,'FLT3-ITD']
mutFLT3 <- mutFLT3$`FLT3-ITD`
if(mutFLT3=='Present'){
mutFLT3 <- 'Mutated'
}else{
mutFLT3 <- 'Wt'
}
#fetch the patient cells
promptKey <- paste('P',currentPatient,sep='')
data <- patient_data[patient_data$Sample==promptKey,]
#parse the cell types one by one
#estimate p values
cellTypes <- c('B','CD4_T','CD8_T','HSCs_MPPs', 'Monocytes','NK','pDCs')
DT <- data.frame()
for(currentCellType in cellTypes){
currentDataValues <- as.matrix(data[data$labels==currentCellType,signal.col.names])
if(is.vector(currentDataValues)==FALSE){
medianValues <- colMedians(currentDataValues)
dt <- data.frame(t(medianValues))
dt$labels <- currentCellType
DT <- rbind(DT,dt)
}else{
medianValues <- currentDataValues
dt <- data.frame(t(medianValues))
dt$labels <- currentCellType
DT <- rbind(DT,dt)
}
}
if(survivalTime$`5-year survival (days)`=='Alive'){
survivalTime <- 5*365
}else{
survivalTime <- survivalTime$`5-year survival (days)`
}
DT$PatientNr <- promptKey
DT$PatientID <- myPatientID$patient_id
DT$SurvivalTime <- survivalTime
DT$karyotype <- chromAbber
DT$FLT3 <- mutFLT3
patient_feature_vector <- rbind(patient_feature_vector,DT)
}
}
end_time = Sys.time()
end_time-start_time
#add survival data
patient_feature_vector$SurvivalTime <- as.numeric(patient_feature_vector$SurvivalTime)
patient_feature_vector$SurvivalStatus <- ifelse(patient_feature_vector$SurvivalTime<5*365,1,0)
#first stratify patients based on survival time and compare their relative cellular abundances
patient_feature_vector$SurvivalGroup <- ifelse(patient_feature_vector$SurvivalTime<5*365,'STS','LTS')
```
## Perform comparison analysis using different approaches
To predict short from long term survival using as input the median values of signaling markers, we will benchmark different machine learning approaches namely:
1) XGBoost,
2) lasso regression
3) ridge regression
both lasso and ridge are part of the glmnet package by adjusting the alpha value.
Since the data are imbalanced with STS class having almost double the size of LTS we will also use the Synthetic Minority Oversampling Technique, often abbreviated SMOTE to generate synthetic datasets with different ratios between positive and negative classes.
```{r benchmark ML methods using medians,echo=TRUE,eval=FALSE,error=TRUE, warning=FALSE,cache=TRUE}
####################################################################################################
#xgboost
#remove CyclinB1,pP38,pS6 because they have 0 median everywhere
patient_feature_vector <- subset(patient_feature_vector, select = -c(2,6,15))
signal.col.names <- signal.col.names[-c(2,6,15)]
#perform cell type specific modeling
cellTypes <- c('B','CD4_T','CD8_T','HSCs_MPPs', 'Monocytes','NK','pDCs')
totalPerformanceXGBoostMedians <- data.frame()
#repeat the same multiple times with random sets, this is to assess reproducibility using independent training,validation and testing sets
N <- 100
for(iter in 1:N){
for(currentCellType in cellTypes){
#fetch the data
currentDataValues <- patient_feature_vector[patient_feature_vector$labels==currentCellType,]
#generate random disjoint sets for training, validation and testing
cut_point <- round(nrow(currentDataValues)*0.8)
a <- sample(nrow(currentDataValues))
asel <- a[1:cut_point]
train_valid_Set <- currentDataValues[asel,]
asel <- a[(cut_point+1):length(a)]
testSet <- currentDataValues[asel,]
#if the test set has only labels from one class AUC cannot be built, thus we ship
check1 <- which(testSet$SurvivalStatus==0)
check2 <- which(testSet$SurvivalStatus==1)
if(length(check1)>0 & length(check2)>0){
#split the train_valid_Set to validation and actual train with ratios again 60-40
cut_point <- round(nrow(train_valid_Set)*0.6)
a <- sample(nrow(train_valid_Set))
asel <- a[1:cut_point]
trainSet <- train_valid_Set[asel,]
asel <- a[(cut_point+1):length(a)]
validationSet <- train_valid_Set[asel,]
depthTunning <- data.frame()
for(depthVal in c(2,4,6,8,10,12,14,16,18,20,30,40,50,60)){
Model <- xgboost(data = as.matrix(trainSet[,signal.col.names]),
label = trainSet$SurvivalStatus,
max_depth = depthVal,
eta = 0.3,
nthread = 2,
nrounds = 4,
objective = "binary:logistic",
verbose = 0)
pred <- predict(Model, as.matrix(validationSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
#if you want to estimate error
#err <- mean(as.numeric(pred > 0.5) != validationSet$SurvivalStatus)
#print(paste("test-error=", err))
#assess performance
prediction <- factor(prediction,levels = c(0,1))
validationSet$SurvivalStatus <- factor(validationSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, validationSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
perf$depthVal <- depthVal
depthTunning <- rbind(depthTunning,perf)
}
#we visualise results for the parameter optimisation process
if(iter==88 & (currentCellType=='CD4_T' | currentCellType=='CD8_T') ){
if(currentCellType=='CD4_T'){
optFigure_1 <- ggplot(depthTunning,aes(x=depthVal,y=F1))+
geom_point(size=2,color='royalblue',alpha=0.8)+
ylim(0,1)+
theme_bw()+
ggtitle('XGBoost - CD4_T cells')+
ylab('F1')+
xlab('Maximum depth of a tree')+
theme(axis.text.y = element_text(size = 12),
axis.text.x = element_text(angle = 0, vjust = 0.05, hjust = 0.95, size = 12),
axis.title.x = element_text( size = 12 ),
axis.title.y = element_text( size = 12 ),
strip.text = element_text(size = 10,face='bold',lineheight=1),
legend.position = "bottom",aspect.ratio = 0.8,
legend.text=element_text(size=12))
}
if(currentCellType=='CD8_T'){
optFigure_2 <- ggplot(depthTunning,aes(x=depthVal,y=F1))+
geom_point(size=2,color='firebrick',alpha=0.8)+
ylim(0,1)+
theme_bw()+
ggtitle('XGBoost - CD8_T cells')+
ylab('F1')+
xlab('Maximum depth of a tree')+
theme(axis.text.y = element_text(size = 12),
axis.text.x = element_text(angle = 0, vjust = 0.05, hjust = 0.95, size = 12),
axis.title.x = element_text( size = 12 ),
axis.title.y = element_text( size = 12 ),
strip.text = element_text(size = 10,face='bold',lineheight=1),
legend.position = "bottom",aspect.ratio = 0.8,
legend.text=element_text(size=12))
}
}
#find the depthVal that maximises F1 in the validation set
a <- which.max(depthTunning$F1)
if(length(a)>0){
bestdepthVal <- depthTunning[a,'depthVal']
}else{
bestdepthVal <- 4
}
#final model after tuning, tested on the test set
bstModel <- xgboost(data = as.matrix(train_valid_Set[,signal.col.names]),
label = train_valid_Set$SurvivalStatus,
max_depth = bestdepthVal,
eta = 0.3,
nthread = 2,
nrounds = 4,
objective = "binary:logistic",
verbose = 0)
pred <- predict(bstModel, as.matrix(testSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
prediction <- factor(prediction,levels = c(0,1))
testSet$SurvivalStatus <- factor(testSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, testSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
#ROC using pROC package
tS <- as.numeric(testSet$SurvivalStatus)-1
pR <- as.numeric(prediction)-1
a <- roc(tS, pR,levels=c("1","0"))
perf$AUC <- a$auc
perf$depthVal <- bestdepthVal
perf$CellType <- currentCellType
perf$Iter <- iter
totalPerformanceXGBoostMedians <- rbind(totalPerformanceXGBoostMedians,perf)
}
}
}
#save the optimisation plots
myfile <- paste('XGBoost_optimisation_CD8T.pdf',sep ='')
pdf(myfile)
print(optFigure_2)
dev.off()
myfile <- paste('XGBoost_optimisation_CD4T.pdf',sep ='')
pdf(myfile)
print(optFigure_1)
dev.off()
####################################################################################################
#SMOTE XGBoost 1:1 ratio
totalPerformanceXGBoostSMOTEMedians_1_1 <- data.frame()
N <- 100
for(iter in 1:N){
for(currentCellType in cellTypes){
#fetch the data
currentDataValues <- patient_feature_vector[patient_feature_vector$labels==currentCellType,]
currentDataValues_filt <- currentDataValues[,c(1:12,20)]
currentDataValues_filt$SurvivalGroup <- as.factor(currentDataValues_filt$SurvivalGroup)
#1000 vs 110 gives balanced data
currentDataValues_new <- SMOTE(SurvivalGroup ~ ., currentDataValues_filt, perc.over = 1000, perc.under = 110)
currentDataValues_new$SurvivalStatus <- ifelse(currentDataValues_new$SurvivalGroup=='STS',1,0)
#generate random disjoint sets for training, validation and testing
cut_point <- round(nrow(currentDataValues_new)*0.8)
a <- sample(nrow(currentDataValues_new))
asel <- a[1:cut_point]
train_valid_Set <- currentDataValues_new[asel,]
asel <- a[(cut_point+1):length(a)]
testSet <- currentDataValues_new[asel,]
#if the test set has only labels from one class AUC cannot be built, thus we ship
check1 <- which(testSet$SurvivalStatus==0)
check2 <- which(testSet$SurvivalStatus==1)
if(length(check1)>0 & length(check2)>0){
#split the train_valid_Set to validation and actual train with ratios again 60-40
cut_point <- round(nrow(train_valid_Set)*0.6)
a <- sample(nrow(train_valid_Set))
asel <- a[1:cut_point]
trainSet <- train_valid_Set[asel,]
asel <- a[(cut_point+1):length(a)]
validationSet <- train_valid_Set[asel,]
depthTunning <- data.frame()
for(depthVal in c(2,4,6,8,10,12,14,16,18,20,30,40,50,60)){
Model <- xgboost(data = as.matrix(trainSet[,signal.col.names]),
label = trainSet$SurvivalStatus,
max_depth = depthVal,
nthread = 2,
nrounds = 100,
objective = "binary:logistic",
verbose = 0)
pred <- predict(Model, as.matrix(validationSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
#if you want to estimate error
#err <- mean(as.numeric(pred > 0.5) != validationSet$SurvivalStatus)
#print(paste("test-error=", err))
#assess performance
prediction <- factor(prediction,levels = c(0,1))
validationSet$SurvivalStatus <- factor(validationSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, validationSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
perf$depthVal <- depthVal
depthTunning <- rbind(depthTunning,perf)
}
#find the depthVal that maximises F1 in the validation set
a <- which.max(depthTunning$F1)
if(length(a)>0){
bestdepthVal <- depthTunning[a,'depthVal']
}else{
bestdepthVal <- 4
}
#final model after tuning, tested on the test set
bstModel <- xgboost(data = as.matrix(train_valid_Set[,signal.col.names]),
label = train_valid_Set$SurvivalStatus,
max_depth = bestdepthVal,
eta = 0.3,
nthread = 2,
nrounds = 4,
objective = "binary:logistic",
verbose = 0)
pred <- predict(bstModel, as.matrix(testSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
prediction <- factor(prediction,levels = c(0,1))
testSet$SurvivalStatus <- factor(testSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, testSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
#ROC using pROC package
tS <- as.numeric(testSet$SurvivalStatus)-1
pR <- as.numeric(prediction)-1
a <- roc(tS, pR,levels=c("1","0"))
perf$AUC <- a$auc
perf$depthVal <- bestdepthVal
perf$CellType <- currentCellType
perf$Iter <- iter
totalPerformanceXGBoostSMOTEMedians_1_1 <- rbind(totalPerformanceXGBoostSMOTEMedians_1_1,perf)
}
}
}
gc()
####################################################################################################
#SMOTE XGBoost 1:2 ratio
totalPerformanceXGBoostSMOTEMedians_1_2 <- data.frame()
N <- 100
for(iter in 1:N){
for(currentCellType in cellTypes){
#fetch the data
currentDataValues <- patient_feature_vector[patient_feature_vector$labels==currentCellType,]
currentDataValues_filt <- currentDataValues[,c(1:12,20)]
currentDataValues_filt$SurvivalGroup <- as.factor(currentDataValues_filt$SurvivalGroup)
#1000 vs 110 gives balanced data
currentDataValues_new <- SMOTE(SurvivalGroup ~ ., currentDataValues_filt, perc.over = 1000, perc.under = 220)
currentDataValues_new$SurvivalStatus <- ifelse(currentDataValues_new$SurvivalGroup=='STS',1,0)
#generate random disjoint sets for training, validation and testing
cut_point <- round(nrow(currentDataValues_new)*0.8)
a <- sample(nrow(currentDataValues_new))
asel <- a[1:cut_point]
train_valid_Set <- currentDataValues_new[asel,]
asel <- a[(cut_point+1):length(a)]
testSet <- currentDataValues_new[asel,]
#if the test set has only labels from one class AUC cannot be built, thus we ship
check1 <- which(testSet$SurvivalStatus==0)
check2 <- which(testSet$SurvivalStatus==1)
if(length(check1)>0 & length(check2)>0){
#split the train_valid_Set to validation and actual train with ratios again 60-40
cut_point <- round(nrow(train_valid_Set)*0.6)
a <- sample(nrow(train_valid_Set))
asel <- a[1:cut_point]
trainSet <- train_valid_Set[asel,]
asel <- a[(cut_point+1):length(a)]
validationSet <- train_valid_Set[asel,]
depthTunning <- data.frame()
for(depthVal in c(2,4,6,8,10,12,14,16,18,20,30,40,50,60)){
Model <- xgboost(data = as.matrix(trainSet[,signal.col.names]),
label = trainSet$SurvivalStatus,
max_depth = depthVal,
nthread = 2,
nrounds = 100,
objective = "binary:logistic",
verbose = 0)
pred <- predict(Model, as.matrix(validationSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
#if you want to estimate error
#err <- mean(as.numeric(pred > 0.5) != validationSet$SurvivalStatus)
#print(paste("test-error=", err))
#assess performance
prediction <- factor(prediction,levels = c(0,1))
validationSet$SurvivalStatus <- factor(validationSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, validationSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
perf$depthVal <- depthVal
depthTunning <- rbind(depthTunning,perf)
}
#find the depthVal that maximises F1 in the validation set
a <- which.max(depthTunning$F1)
if(length(a)>0){
bestdepthVal <- depthTunning[a,'depthVal']
}else{
bestdepthVal <- 4
}
#final model after tuning, tested on the test set
bstModel <- xgboost(data = as.matrix(train_valid_Set[,signal.col.names]),
label = train_valid_Set$SurvivalStatus,
max_depth = bestdepthVal,
eta = 0.3,
nthread = 2,
nrounds = 4,
objective = "binary:logistic",
verbose = 0)
pred <- predict(bstModel, as.matrix(testSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
prediction <- factor(prediction,levels = c(0,1))
testSet$SurvivalStatus <- factor(testSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, testSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
#ROC using pROC package
tS <- as.numeric(testSet$SurvivalStatus)-1
pR <- as.numeric(prediction)-1
a <- roc(tS, pR,levels=c("1","0"))
perf$AUC <- a$auc
perf$depthVal <- bestdepthVal
perf$CellType <- currentCellType
perf$Iter <- iter
totalPerformanceXGBoostSMOTEMedians_1_2 <- rbind(totalPerformanceXGBoostSMOTEMedians_1_2,perf)
}
}
}
gc()
####################################################################################################
#SMOTE XGBoost 1:3 ratio
totalPerformanceXGBoostSMOTEMedians_1_3 <- data.frame()
N <- 100
for(iter in 1:N){
for(currentCellType in cellTypes){
#fetch the data
currentDataValues <- patient_feature_vector[patient_feature_vector$labels==currentCellType,]
currentDataValues_filt <- currentDataValues[,c(1:12,20)]
currentDataValues_filt$SurvivalGroup <- as.factor(currentDataValues_filt$SurvivalGroup)
#1000 vs 110 gives balanced data
currentDataValues_new <- SMOTE(SurvivalGroup ~ ., currentDataValues_filt, perc.over = 1000, perc.under = 330)
currentDataValues_new$SurvivalStatus <- ifelse(currentDataValues_new$SurvivalGroup=='STS',1,0)
#generate random disjoint sets for training, validation and testing
cut_point <- round(nrow(currentDataValues_new)*0.8)
a <- sample(nrow(currentDataValues_new))
asel <- a[1:cut_point]
train_valid_Set <- currentDataValues_new[asel,]
asel <- a[(cut_point+1):length(a)]
testSet <- currentDataValues_new[asel,]
#if the test set has only labels from one class AUC cannot be built, thus we ship
check1 <- which(testSet$SurvivalStatus==0)
check2 <- which(testSet$SurvivalStatus==1)
if(length(check1)>0 & length(check2)>0){
#split the train_valid_Set to validation and actual train with ratios again 60-40
cut_point <- round(nrow(train_valid_Set)*0.6)
a <- sample(nrow(train_valid_Set))
asel <- a[1:cut_point]
trainSet <- train_valid_Set[asel,]
asel <- a[(cut_point+1):length(a)]
validationSet <- train_valid_Set[asel,]
depthTunning <- data.frame()
for(depthVal in c(2,4,6,8,10,12,14,16,18,20,30,40,50,60)){
Model <- xgboost(data = as.matrix(trainSet[,signal.col.names]),
label = trainSet$SurvivalStatus,
max_depth = depthVal,
nthread = 2,
nrounds = 100,
objective = "binary:logistic",
verbose = 0)
pred <- predict(Model, as.matrix(validationSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
#if you want to estimate error
#err <- mean(as.numeric(pred > 0.5) != validationSet$SurvivalStatus)
#print(paste("test-error=", err))
#assess performance
prediction <- factor(prediction,levels = c(0,1))
validationSet$SurvivalStatus <- factor(validationSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, validationSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
perf$depthVal <- depthVal
depthTunning <- rbind(depthTunning,perf)
}
#find the depthVal that maximises F1 in the validation set
a <- which.max(depthTunning$F1)
if(length(a)>0){
bestdepthVal <- depthTunning[a,'depthVal']
}else{
bestdepthVal <- 4
}
#final model after tuning, tested on the test set
bstModel <- xgboost(data = as.matrix(train_valid_Set[,signal.col.names]),
label = train_valid_Set$SurvivalStatus,
max_depth = bestdepthVal,
eta = 0.3,
nthread = 2,
nrounds = 4,
objective = "binary:logistic",
verbose = 0)
pred <- predict(bstModel, as.matrix(testSet[,signal.col.names]))
prediction <- as.numeric(pred > 0.5)
prediction <- factor(prediction,levels = c(0,1))
testSet$SurvivalStatus <- factor(testSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, testSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
#ROC using pROC package
tS <- as.numeric(testSet$SurvivalStatus)-1
pR <- as.numeric(prediction)-1
a <- roc(tS, pR,levels=c("1","0"))
perf$AUC <- a$auc
perf$depthVal <- bestdepthVal
perf$CellType <- currentCellType
perf$Iter <- iter
totalPerformanceXGBoostSMOTEMedians_1_3 <- rbind(totalPerformanceXGBoostSMOTEMedians_1_3,perf)
}
}
}
gc()
####################################################################################################
#LASSO
totalPerformanceLASSOMedians <- data.frame()
#repeat the same multiple times with random sets, this is to assess reproducibility using independent training,validation and testing sets
N <- 100
for(iter in 1:N){
for(currentCellType in cellTypes){
#fetch the data
currentDataValues <- patient_feature_vector[patient_feature_vector$labels==currentCellType,]
#generate random disjoint sets for training, validation and testing
cut_point <- round(nrow(currentDataValues)*0.8)
a <- sample(nrow(currentDataValues))
asel <- a[1:cut_point]
train_valid_Set <- currentDataValues[asel,]
asel <- a[(cut_point+1):length(a)]
testSet <- currentDataValues[asel,]
#if the test set has only labels from one class AUC cannot be built, thus we ship
check1 <- which(testSet$SurvivalStatus==0)
check2 <- which(testSet$SurvivalStatus==1)
if(length(check1)>0 & length(check2)>0){
#split the train_valid_Set to validation and actual train with ratios again 60-40
cut_point <- round(nrow(train_valid_Set)*0.6)
a <- sample(nrow(train_valid_Set))
asel <- a[1:cut_point]
trainSet <- train_valid_Set[asel,]
asel <- a[(cut_point+1):length(a)]
validationSet <- train_valid_Set[asel,]
fit <- glmnet(as.matrix(trainSet[,signal.col.names]),
trainSet$SurvivalStatus,
family = "binomial",
alpha=0)
pred <- predict(fit, newx = as.matrix(validationSet[,signal.col.names]),
type = "class", s = fit$lambda)
pred <- matrix(as.numeric(pred), ncol = ncol(pred))
lambdaTunning <- data.frame()
for(idx in 1:ncol(pred)){
tmp <- pred[,idx]
prediction <- factor(tmp,levels = c(0,1))
validationSet$SurvivalStatus <- factor(validationSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, validationSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
perf$lamda <- fit$lambda[idx]
lambdaTunning <- rbind(lambdaTunning,perf)
}
#we visualise results for the parameter optimisation process
if(iter==88 & (currentCellType=='CD4_T' | currentCellType=='CD8_T') ){
if(currentCellType=='CD4_T'){
optFigure_1 <- ggplot(lambdaTunning,aes(x=as.character(lamda),y=F1))+
geom_point(size=2,color='royalblue',alpha=0.8)+
ylim(0,1)+
theme_bw()+
ggtitle('Lasso - CD4_T cells')+
ylab('F1')+
xlab('Lamda values')+
theme(axis.text.y = element_text(size = 12),
axis.text.x = element_blank(),
axis.title.x = element_text( size = 12 ),
axis.title.y = element_text( size = 12 ),
strip.text = element_text(size = 10,face='bold',lineheight=1),
legend.position = "bottom",aspect.ratio = 0.8,
legend.text=element_text(size=12))
}
if(currentCellType=='CD8_T'){
optFigure_2 <- ggplot(lambdaTunning,aes(x=as.character(lamda),y=F1))+
geom_point(size=2,color='firebrick',alpha=0.8)+
ylim(0,1)+
theme_bw()+
ggtitle('Lasso - CD8_T cells')+
ylab('F1')+
xlab('Lamda values')+
theme(axis.text.y = element_text(size = 12),
axis.text.x = element_blank(),
axis.title.x = element_text( size = 12 ),
axis.title.y = element_text( size = 12 ),
strip.text = element_text(size = 10,face='bold',lineheight=1),
legend.position = "bottom",aspect.ratio = 0.8,
legend.text=element_text(size=12))
}
}
#find the best lamda that maximises F1 in the validation set
a <- which.max(depthTunning$F1)
if(length(a)>0){
bestlamda <- lambdaTunning[a,'lamda']
}else{
bestlamda <- min(fit$lambda)
}
bestFit <- glmnet(as.matrix(train_valid_Set[,signal.col.names]),
train_valid_Set$SurvivalStatus,
family = "binomial",
alpha=0)
pred <- predict(bestFit, newx = as.matrix(testSet[,signal.col.names]),
type = "class", s = bestlamda)
pred <- as.numeric(pred)
prediction <- factor(pred,levels = c(0,1))
testSet$SurvivalStatus <- factor(testSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, testSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
#ROC using pROC package
tS <- as.numeric(testSet$SurvivalStatus)-1
pR <- as.numeric(prediction)-1
a <- roc(tS, pR,levels=c("1","0"))
perf$AUC <- a$auc
perf$lambda <- bestlamda
perf$CellType <- currentCellType
perf$Iter <- iter
totalPerformanceLASSOMedians <- rbind(totalPerformanceLASSOMedians,perf)
}
}
if(iter %% 10==0){
gc()
}
}
#save the optimisation plots
myfile <- paste('Lasso_optimisation_CD8T.pdf',sep ='')
pdf(myfile)
print(optFigure_2)
dev.off()
myfile <- paste('Lasso_optimisation_CD4T.pdf',sep ='')
pdf(myfile)
print(optFigure_1)
dev.off()
####################################################################################################
#Ridge
totalPerformanceRIDGEMedians <- data.frame()
#repeat the same multiple times with random sets, this is to assess reproducibility using independent training,validation and testing sets
N <- 100
for(iter in 1:N){
for(currentCellType in cellTypes){
#fetch the data
currentDataValues <- patient_feature_vector[patient_feature_vector$labels==currentCellType,]
#generate random disjoint sets for training, validation and testing
cut_point <- round(nrow(currentDataValues)*0.8)
a <- sample(nrow(currentDataValues))
asel <- a[1:cut_point]
train_valid_Set <- currentDataValues[asel,]
asel <- a[(cut_point+1):length(a)]
testSet <- currentDataValues[asel,]
#if the test set has only labels from one class AUC cannot be built, thus we ship
check1 <- which(testSet$SurvivalStatus==0)
check2 <- which(testSet$SurvivalStatus==1)
if(length(check1)>0 & length(check2)>0){
#split the train_valid_Set to validation and actual train with ratios again 60-40
cut_point <- round(nrow(train_valid_Set)*0.6)
a <- sample(nrow(train_valid_Set))
asel <- a[1:cut_point]
trainSet <- train_valid_Set[asel,]
asel <- a[(cut_point+1):length(a)]
validationSet <- train_valid_Set[asel,]
fit <- glmnet(as.matrix(trainSet[,signal.col.names]),
trainSet$SurvivalStatus,
family = "binomial",
alpha=1)
pred <- predict(fit, newx = as.matrix(validationSet[,signal.col.names]),
type = "class", s = fit$lambda)
pred <- matrix(as.numeric(pred), ncol = ncol(pred))
lambdaTunning <- data.frame()
for(idx in 1:ncol(pred)){
tmp <- pred[,idx]
prediction <- factor(tmp,levels = c(0,1))
validationSet$SurvivalStatus <- factor(validationSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, validationSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
perf$lamda <- fit$lambda[idx]
lambdaTunning <- rbind(lambdaTunning,perf)
}
#we visualise results for the parameter optimisation process
if(iter==88 & (currentCellType=='CD4_T' | currentCellType=='CD8_T') ){
if(currentCellType=='CD4_T'){
optFigure_1 <- ggplot(lambdaTunning,aes(x=as.character(lamda),y=F1))+
geom_point(size=2,color='royalblue',alpha=0.8)+
ylim(0,1)+
theme_bw()+
ggtitle('Ridge - CD4_T cells')+
ylab('F1')+
xlab('Lamda values')+
theme(axis.text.y = element_text(size = 12),
axis.text.x = element_blank(),
axis.title.x = element_text( size = 12 ),
axis.title.y = element_text( size = 12 ),
strip.text = element_text(size = 10,face='bold',lineheight=1),
legend.position = "bottom",aspect.ratio = 0.8,
legend.text=element_text(size=12))
}
if(currentCellType=='CD8_T'){
optFigure_2 <- ggplot(lambdaTunning,aes(x=as.character(lamda),y=F1))+
geom_point(size=2,color='firebrick',alpha=0.8)+
ylim(0,1)+
theme_bw()+
ggtitle('Ridge - CD8_T cells')+
ylab('F1')+
xlab('Lamda values')+
theme(axis.text.y = element_text(size = 12),
axis.text.x = element_blank(),
axis.title.x = element_text( size = 12 ),
axis.title.y = element_text( size = 12 ),
strip.text = element_text(size = 10,face='bold',lineheight=1),
legend.position = "bottom",aspect.ratio = 0.8,
legend.text=element_text(size=12))
}
}
#find the depthVal that maximises F1 in the validation set
a <- which.max(lambdaTunning$F1)
if(length(a)>0){
bestlamda <- lambdaTunning[a,'lamda']
}else{
bestlamda <- min(fit$lambda)
}
bestFit <- glmnet(as.matrix(train_valid_Set[,signal.col.names]),
train_valid_Set$SurvivalStatus,
family = "binomial",
alpha=1)
pred <- predict(bestFit, newx = as.matrix(testSet[,signal.col.names]),
type = "class", s = bestlamda)
pred <- as.numeric(pred)
prediction <- factor(pred,levels = c(0,1))
testSet$SurvivalStatus <- factor(testSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, testSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
#ROC using pROC package
tS <- as.numeric(testSet$SurvivalStatus)-1
pR <- as.numeric(prediction)-1
a <- roc(tS, pR,levels=c("1","0"))
perf$AUC <- a$auc
perf$lambda <- bestlamda
perf$CellType <- currentCellType
perf$Iter <- iter
totalPerformanceRIDGEMedians <- rbind(totalPerformanceRIDGEMedians,perf)
}
}
if(iter %% 10==0){
gc()
}
}
#save the optimisation plots
myfile <- paste('Ridge_optimisation_CD8T.pdf',sep ='')
pdf(myfile)
print(optFigure_2)
dev.off()
myfile <- paste('Ridge_optimisation_CD4T.pdf',sep ='')
pdf(myfile)
print(optFigure_1)
dev.off()
######################################################################################################################
#SMOTE - LASSO
totalPerformanceLASSOSMOTEMedians_1_1 <- data.frame()
N <- 100
for(iter in 1:N){
for(currentCellType in cellTypes){
#fetch the data
currentDataValues <- patient_feature_vector[patient_feature_vector$labels==currentCellType,]
currentDataValues_filt <- currentDataValues[,c(1:12,20)]
currentDataValues_filt$SurvivalGroup <- as.factor(currentDataValues_filt$SurvivalGroup)
#1000 vs 110 gives balanced data
currentDataValues_new <- SMOTE(SurvivalGroup ~ ., currentDataValues_filt, perc.over = 1000, perc.under = 110)
currentDataValues_new$SurvivalStatus <- ifelse(currentDataValues_new$SurvivalGroup=='STS',1,0)
#generate random disjoint sets for training, validation and testing
cut_point <- round(nrow(currentDataValues_new)*0.8)
a <- sample(nrow(currentDataValues_new))
asel <- a[1:cut_point]
train_valid_Set <- currentDataValues_new[asel,]
asel <- a[(cut_point+1):length(a)]
testSet <- currentDataValues_new[asel,]
#if the test set has only labels from one class AUC cannot be built, thus we ship
check1 <- which(testSet$SurvivalStatus==0)
check2 <- which(testSet$SurvivalStatus==1)
if(length(check1)>0 & length(check2)>0){
#split the train_valid_Set to validation and actual train with ratios again 60-40
cut_point <- round(nrow(train_valid_Set)*0.6)
a <- sample(nrow(train_valid_Set))
asel <- a[1:cut_point]
trainSet <- train_valid_Set[asel,]
asel <- a[(cut_point+1):length(a)]
validationSet <- train_valid_Set[asel,]
fit <- glmnet(as.matrix(trainSet[,signal.col.names]),
trainSet$SurvivalStatus,
family = "binomial",
alpha=0)
pred <- predict(fit, newx = as.matrix(validationSet[,signal.col.names]),
type = "class", s = fit$lambda)
pred <- matrix(as.numeric(pred), ncol = ncol(pred))
lambdaTunning <- data.frame()
for(idx in 1:ncol(pred)){
tmp <- pred[,idx]
prediction <- factor(tmp,levels = c(0,1))
validationSet$SurvivalStatus <- factor(validationSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, validationSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))
perf$lamda <- fit$lambda[idx]
lambdaTunning <- rbind(lambdaTunning,perf)
}
#find the depthVal that maximises F1 in the validation set
a <- which.max(lambdaTunning$F1)
if(length(a)>0){
bestlamda <- lambdaTunning[a,'lamda']
}else{
bestlamda <- min(fit$lambda)
}
bestFit <- glmnet(as.matrix(train_valid_Set[,signal.col.names]),
train_valid_Set$SurvivalStatus,
family = "binomial",
alpha=0)
pred <- predict(bestFit, newx = as.matrix(testSet[,signal.col.names]),
type = "class", s = bestlamda)
pred <- as.numeric(pred)
prediction <- factor(pred,levels = c(0,1))
testSet$SurvivalStatus <- factor(testSet$SurvivalStatus,levels = c(0,1))
perf <- confusionMatrix(prediction, testSet$SurvivalStatus, positive = c('1'))
perf <- as.data.frame(t(perf$byClass))