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preprocess_ml_stratify_validation.Rmd
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---
title: "R Notebook"
output:
html_document:
df_print: paged
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook, the results appear beneath the code.
Try executing this chunk by clicking the *Run* button within the chunk or by placing your cursor inside it and pressing *Ctrl+Shift+Enter*.
```{r}
set.seed(123)
library(caret)
library(mlbench)
library(MLmetrics)
library(ggplot2)
# data(Sonar)
library(DescTools)
library(magrittr)
```
# input data
## discovery cohort
```{r}
working.dir = "/Users/jiangyanyu/sciebo /Projects/IPF_ownerJY/"
impute_data = read.csv(file = paste0(working.dir,"/IPF_manuscript/meta_data/20221205_imputed_data_7parameters.csv"))
cols.num <- c("FVCorVC_percent","FEV1_percent","TLC_percent","DLCO_percent","pO2","pCO2")
pnt_name = readxl::read_xlsx(path = paste0(working.dir,"/IPF_manuscript/meta_data/pnt_number_in_paper.xlsx"),sheet = "Sheet1")
impute_data = merge(impute_data,pnt_name[,c("Patient2","pnt_in_paper")],by.x = "X",by.y="Patient2")
ipf_meta = read.csv(file = paste0(working.dir,"/IPF_manuscript/meta_data/20230605_scaledvalue_patient_stages.csv"))
impute_data = merge(impute_data,ipf_meta[c("pnt","stage")],by.x="pnt_in_paper",by.y="pnt")
dis_data = impute_data[,c(cols.num,"stage")]
rownames(dis_data) = impute_data$pnt_in_paper
rm(pnt_name,ipf_meta)
dis_data$stage = stringr::str_replace(dis_data$stage,pattern = "-",replacement = "")
```
## validation cohort
```{r}
testing_input = readxl::read_xlsx(path = paste0(working.dir,"/IPF_manuscript/meta_data/20230925_pntstage_testingdata.xlsx"),sheet = "Sheet1")
val_data = testing_input[,cols.num] %>% as.data.frame()
rownames(val_data) = testing_input$pnt
val_data = dplyr::mutate_if(val_data,is.character,as.numeric)
val_data$stage = testing_input$stage
knitr::kable(testing_input)
```
# scale data
```{r}
merge_data = rbind(dis_data,val_data)
library(magrittr)
merge_data = merge_data %>% dplyr::mutate_at(c(1:6),scale)
colnames(merge_data) = c(cols.num,"stage")
write.csv(merge_data,file = paste0(working.dir,"/IPF_manuscript/meta_data/20230926_ml_predict_val.csv"))
```
# assign training/testing
```{r}
# val_ck = rownames(val_data)[rownames(val_data) %like% "%Control%"]
# testing <- merge_data[val_ck,]
## use only IPF as training
training <- merge_data[rownames(dis_data),] %>% subset(.,stage != "Control")
inTraining <- createDataPartition(training$stage, p = .75, list = FALSE)
training <- training[ inTraining,]
testing <- training[-inTraining,]
training$stage = factor(training$stage,levels = c("EarlyIPF","AdvancedIPF"))
# training$stage = factor(training$stage,levels = c("Control","EarlyIPF","AdvancedIPF"))
# rm(val_ck)
```
# test models
## train model
```{r}
ctrl <- trainControl(
method = "boot",
repeats = 3,
classProbs = TRUE,
summaryFunction = multiClassSummary
)
```
<!-- ## svm -->
<!-- support vector machines -->
<!-- ```{r} -->
<!-- plsFit <- train( -->
<!-- stage ~ ., -->
<!-- data = training, -->
<!-- method = "svmLinear3", -->
<!-- trControl = ctrl, -->
<!-- tuneGrid = data.frame(cost = 1, #accounts for over-fitting -->
<!-- Loss = 2) #accounts for misclassifications -->
<!-- ) -->
<!-- plsFit -->
<!-- plsProbs <- predict(plsFit, newdata = testing) -->
<!-- # plsProbs = factor(plsProbs,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- # testing$stage = factor(testing$stage,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- plsProbs = factor(plsProbs,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- testing$stage = factor(testing$stage,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- svm_confusion_matrix <- caret::confusionMatrix(plsProbs, testing$stage, mode = "prec_recall") -->
<!-- svm_confusion_matrix -->
<!-- ``` -->
<!-- ## knn -->
<!-- ```{r} -->
<!-- set.seed(123) -->
<!-- plsFit <- train( -->
<!-- stage ~ ., -->
<!-- data = training, -->
<!-- method = "knn", #the algorithm -->
<!-- trControl = ctrl, #the resampling strategy we will use -->
<!-- tuneGrid = data.frame(k = 2) #the hyperparameter -->
<!-- ) -->
<!-- plsFit -->
<!-- plsProbs <- predict(plsFit, newdata = testing) -->
<!-- # plsProbs = factor(plsProbs,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- # testing$stage = factor(testing$stage,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- plsProbs = factor(plsProbs,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- testing$stage = factor(testing$stage,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- svm_confusion_matrix <- caret::confusionMatrix(plsProbs, testing$stage, mode = "prec_recall") -->
<!-- svm_confusion_matrix -->
<!-- ``` -->
<!-- ## decision tree -->
<!-- ```{r} -->
<!-- plsFit <- train( -->
<!-- stage ~ ., -->
<!-- data = training, -->
<!-- method = "rpart", -->
<!-- trControl = ctrl -->
<!-- ) -->
<!-- plsFit -->
<!-- plsProbs <- predict(plsFit, newdata = testing) -->
<!-- # plsProbs = factor(plsProbs,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- # testing$stage = factor(testing$stage,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- plsProbs = factor(plsProbs,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- testing$stage = factor(testing$stage,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- svm_confusion_matrix <- caret::confusionMatrix(plsProbs, testing$stage, mode = "prec_recall") -->
<!-- svm_confusion_matrix -->
<!-- ``` -->
<!-- ## random forest -->
<!-- ```{r} -->
<!-- plsFit <- train( -->
<!-- stage ~ ., -->
<!-- data = training, -->
<!-- method = "ranger", -->
<!-- trControl = ctrl, -->
<!-- tuneGrid = data.frame(mtry = 3, -->
<!-- splitrule = "extratrees", -->
<!-- min.node.size = 5) -->
<!-- ) -->
<!-- plsFit -->
<!-- plsProbs <- predict(plsFit, newdata = testing) -->
<!-- # plsProbs = factor(plsProbs,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- # testing$stage = factor(testing$stage,levels = c("Control","EarlyIPF","AdvancedIPF")) -->
<!-- plsProbs = factor(plsProbs,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- testing$stage = factor(testing$stage,levels = c("EarlyIPF","AdvancedIPF")) -->
<!-- svm_confusion_matrix <- caret::confusionMatrix(plsProbs, testing$stage, mode = "prec_recall") -->
<!-- svm_confusion_matrix -->
<!-- ``` -->
# use decision tree
```{r}
val_pre = merge_data[merge_data$stage == "IPF",]
plsFit <- train(
stage ~ .,
data = training,
method = "rpart",
trControl = ctrl
)
plsFit
plsProbs <- predict(plsFit, newdata = val_pre)
knitr::kable(cbind(val_pre, prediction = plsProbs))
```
# diffusion map
```{r}
library(destiny)
merge_data = read.csv(file = paste0(working.dir,"/IPF_manuscript/meta_data/20230926_ml_predict_val.csv"))
dm = DiffusionMap(data = merge_data[,c(1:6)])
merge_dm = cbind(merge_data,DC1 = dm$DC1,DC2 = dm$DC2)
merge_dm$cohort = c(rep("dis",62),rep("val",14))
merge_dm$cohort_stage = paste0(merge_dm$cohort,"_",merge_data$stage)
stage_color = c("Control" = "grey",
"EarlyIPF" = "#076B94",
"AdvancedIPF" = "#C6A775")
merge_dm$stage[(nrow(merge_dm)-6):nrow(merge_dm)] = plsProbs
merge_dm$stage = factor(merge_dm$stage,levels = c("Control","EarlyIPF","AdvancedIPF"))
```
## plot
```{r}
pdf(paste0(working.dir,"/IPF_manuscript/manuscriptV3/figures/fig4f.dm_ml_results.pdf"),paper = "a4",width = 4,height = 3)
# png(paste0(working.dir,"/IPF_manuscript/manuscriptV3/figures/fig4f.dm_ml_results.png"),res = 150)
# ggplot(subset(merge_dm,cohort_stage != "val_Control"),aes(DC1,DC2,colour = stage))+
ggplot(merge_dm,aes(DC1,DC2, shape = cohort))+
geom_point(size = 1)+
# scale_color_manual(values = stage_color)+
# geom_point(data = subset(merge_dm,cohort == "val"),aes(DC1,DC2,,shape = cohort),size = 2.5,shape=16)+
theme_classic()+
theme(axis.line = element_blank(),
axis.text.x = element_text(size = 8,colour = "black"),
axis.text.y = element_text(size = 8,colour = "black"),
axis.title.y = element_text(size = 8,colour = "black"),
panel.border = element_rect(color = "black",size=0.5,fill = NA),
legend.text = element_text(size = 8,colour = "black"),
legend.text.align = 0,
legend.key.size = unit(0.5,"cm"),
title = element_text(size = 8,colour = "black")
)+
# scale_color_manual(name = "Stage",
# labels = c("Control","Early-IPF","Advanced-IPF"),
# values = stage_color)+
scale_shape_manual(name = "Cohort",
labels = c("Discovery","Validation"),
values = c(17,16))
dev.off()
```