---
title: "QuickSeuratClystering_MixedSpecies"
author: "Nina-Lydia Kazakou"
date: "04/02/2022"
output: html_document
---

# Set-up
```{r output-code, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r set-up, message=FALSE, warning=FALSE}
library(Seurat)
library(devtools)
library(dplyr)
library(ggsci)
library(tidyverse)
library(Matrix)
library(scales)
library(here)
```

```{r}
srt_70 <- readRDS(here("Processed", "filter_70", "srt_objects", file = "srt_combined_filter_70.rds"))
srt_80 <- readRDS(here("Processed", "filter_80", "srt_objects", file = "srt_combined_filter_80.rds"))
srt_90 <- readRDS(here("Processed", "filter_90", "srt_objects", file = "srt_combined_filter_90.rds"))
```

```{r Add TreatmentGroup}
# Filter_70
ids_70 <- colnames(srt_70)
str_sub(ids_70, -17, -1) = ""
srt_70@meta.data$DrugGroup <- ids_70
head(srt_70)

# Filter_80
ids_80 <- colnames(srt_80)
str_sub(ids_80, -17, -1) = ""
srt_80@meta.data$DrugGroup <- ids_80
head(srt_80)

# Filter_70
ids_90 <- colnames(srt_90)
str_sub(ids_90, -17, -1) = ""
srt_90@meta.data$DrugGroup <- ids_90
head(srt_90)
```

*#Filter_70*
# Seurat Clustering
```{r QuickSeurat, Clustering, fig.height=6, fig.width=10}
memory.limit(50000)

srt_70 <- NormalizeData(srt_70, verbose = FALSE) %>% 
  ScaleData(verbose = FALSE) %>% 
  FindVariableFeatures(verbose = FALSE)

srt_70 <- RunPCA(srt_70, verbose = FALSE, npcs = 30)
srt_70 <- RunUMAP(srt_70, dims = 1:20, check_duplicates = TRUE)
DimPlot(srt_70, reduction = "pca", pt.size = 0.2, group.by = "species")
DimPlot(srt_70, reduction = "umap", pt.size = 0.1, group.by = "species")
```

```{r fig.height=6, fig.width=10}
DimPlot(srt_70, reduction = "umap", pt.size = 0.1, group.by = "DrugGroup", split.by = "species")
```

```{r fig.height=6, fig.width=10}
DimPlot(srt_70, reduction = "umap", pt.size = 0.1, group.by = "species", split.by = "outlier")
```




*#Filter_80*
# Seurat Clustering
```{r QuickSeurat, Clustering, fig.height=6, fig.width=10}
# memory.limit(50000)

srt_80 <- NormalizeData(srt_80, verbose = FALSE) %>% 
  ScaleData(verbose = FALSE) %>% 
  FindVariableFeatures(verbose = FALSE)

srt_80 <- RunPCA(srt_80, verbose = FALSE, npcs = 30)
srt_80 <- RunUMAP(srt_80, dims = 1:20, check_duplicates = TRUE)
DimPlot(srt_80, reduction = "pca", pt.size = 0.2, group.by = "species")
DimPlot(srt_80, reduction = "umap", pt.size = 0.1, group.by = "species")
```

```{r fig.height=6, fig.width=10}
DimPlot(srt_80, reduction = "umap", pt.size = 0.1, group.by = "DrugGroup", split.by = "species")
```

```{r fig.height=6, fig.width=10}
DimPlot(srt_80, reduction = "umap", pt.size = 0.1, group.by = "species", split.by = "outlier")
```




*#Filter_90*
# Seurat Clustering
```{r QuickSeurat, Clustering, fig.height=6, fig.width=10}
# memory.limit(50000)

srt_90 <- NormalizeData(srt_90, verbose = FALSE) %>% 
  ScaleData(verbose = FALSE) %>% 
  FindVariableFeatures(verbose = FALSE)

srt_90 <- RunPCA(srt_90, verbose = FALSE, npcs = 30)
srt_90 <- RunUMAP(srt_90, dims = 1:20, check_duplicates = TRUE)
DimPlot(srt_90, reduction = "pca", pt.size = 0.2, group.by = "species")
DimPlot(srt_90, reduction = "umap", pt.size = 0.1, group.by = "species")
```

```{r fig.height=6, fig.width=10}
DimPlot(srt_90, reduction = "umap", pt.size = 0.1, group.by = "DrugGroup", split.by = "species")
```

```{r fig.height=6, fig.width=10}
DimPlot(srt_90, reduction = "umap", pt.size = 0.1, group.by = "species", split.by = "outlier")
```


```{r saveUpdatedObjects}
saveRDS(srt_70, here("Processed", "filter_70", "srt_objects", file = "srt_combined_filter_70_withOutlier.rds"))
saveRDS(srt_80, here("Processed", "filter_80", "srt_objects", file = "srt_combined_filter_80_withOutlier.rds"))
saveRDS(srt_90, here("Processed", "filter_90", "srt_objects", file = "srt_combined_filter_90_withOutlier.rds"))
```

```{r}
sessionInfo()
```