133 lines (92 with data), 3.0 kB

---
title: "Run GB on Combined T cells - surface"
author: Tobias Roider
date: "Last compiled on `r format(Sys.time(), '%d %B, %Y, %X')`"
output: 
  rmdformats::readthedown: 
  
editor_options: 
  chunk_output_type: console
---

```{r options, include=FALSE, warning = FALSE}

library(knitr)
opts_chunk$set(echo=TRUE, tidy=FALSE, include=TRUE, message=FALSE,
               dpi = 100, cache = FALSE, warning = FALSE)
opts_knit$set(root.dir = "../")
options(bitmapType = "cairo")

```

# Load packages
```{r Load packages and functions}

library(Seurat)
library(tidyverse)
library(caret)
library(rsample)
source("R/Functions.R")

```

# Read data
```{r read data, fig.width=4, fig.height=4}

sobj <- readRDS("output/Tcells_Integrated.rds")
sobj$IdentI <- factor(sobj$IdentI, levels = sobj$IdentI %>% unique %>% sort())

Idents(sobj) <- "IdentI"
DimPlot(sobj, label = T, reduction = "wnn.umap", raster = TRUE, 
        raster.dpi = c(500,500), group.by = "IdentI")+
  NoLegend()

```

# Split data using rsample package
```{r split data}

set.seed(1)
split <- sobj@assays$integratedADT@scale.data %>% t %>% data.frame() %>% 
  cbind(., FetchData(sobj, vars = c("IdentI"))) %>% 
  select(-IdentI) %>% 
  initial_split(prop = 0.35)

train <- training(split) %>% as.matrix()
test <- testing(split) %>% as.matrix()
  
classes_split <- initial_split(as_tibble(Idents(sobj)), prop = 0.35)
classes_split[["in_id"]] <- split[["in_id"]] # Ensures corresponding celltypes
classes_training <- training(classes_split) %>% .$value %>% as.factor()
classes_test  <- testing(classes_split) %>% .$value %>% as.factor()
  
# Ensure all classes are represented
all(levels(classes_training) == levels(classes_test))

```

# Train model
```{r train model, message=FALSE, warning=FALSE, results='hide'}

trControl <- trainControl(method = "cv", number = 10, allowParallel = TRUE,
                          search = "random", verboseIter = FALSE)
  
set.seed(23)
gb_fit <- train(x = train,
                y = classes_training,
                method = "xgbTree",
                importance = TRUE,
                metric = "Accuracy",
                maximize = TRUE,
                trControl = trControl)

```

# Print model
```{r print model}

print(gb_fit)

```

# Predict
```{r predict model}

predict_test <- predict(gb_fit, newdata = test)

confusionMatrix(predict_test, classes_test)

```

# Store classes
```{r store classes}

names(classes_training) <- rownames(train)
names(classes_test) <- rownames(test)
names(predict_test) <- rownames(test)
classes <- list(classes_training, classes_test, predict_test)
names(classes) <- c("train", "test", "predict")

```

# Show most important markers
```{r important markers}

varImp(gb_fit, scale = TRUE, top=40)

```

# Save objects
```{r save}

saveRDS(gb_fit, file = "output/GBresults_Combined_T_surface.rds")
saveRDS(classes, file = "output/GBclasses_Combined_T_surface.rds")

```

# Session info
```{r session info}

sessionInfo()

```