---
title: '[RC17]Subset for Oligodendroglia'
author: "Nina-Lydia Kazakou"
date: "09/03/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Set-up
### Load libraries
```{r message=FALSE, warning=FALSE}
library(SingleCellExperiment)
library(Seurat)
library(tidyverse)
library(Matrix)
library(scales)
library(cowplot)
library(RCurl)
library(scDblFinder)
library(Matrix)
library(ggplot2)
library(edgeR)
library(dplyr)
library(ggsci)
library(here)
```
### Colour Palette
```{r load_palette}
mypal <- pal_npg("nrc", alpha = 0.7)(10)
mypal2 <-pal_tron("legacy", alpha = 0.7)(7)
mypal3<-pal_lancet("lanonc", alpha = 0.7)(9)
mypal4<-pal_simpsons(palette = c("springfield"), alpha = 0.7)(16)
mypal5 <- pal_rickandmorty(palette = c("schwifty"), alpha = 0.7)(6)
mypal6 <- pal_futurama(palette = c("planetexpress"), alpha = 0.7)(5)
mypal7 <- pal_startrek(palette = c("uniform"), alpha = 0.7)(5)
mycoloursP<- c(mypal, mypal2, mypal3, mypal4)
```
### Load object
```{r}
RC17_norm <- readRDS(here("data", "Processed", "AllCells", "RC17_AllCells_srt.rds"))
```
```{r fig.height=4, fig.width=8}
Idents(RC17_norm) <- "RNA_snn_res.0.8"
FeaturePlot(RC17_norm, features = c("OLIG2", "SOX10"), label = TRUE) & NoLegend()
```
```{r fig.height=6, fig.width=12}
VlnPlot(RC17_norm, features = c("OLIG1", "OLIG2", "SOX10"), cols = mycoloursP, group.by = "RNA_snn_res.0.8", pt.size = 0.2, ncol = 2)
```
## Subset for Oligodendroglia
I will subset my dataset based on OLIG2 & SOX10 expression. I will subset for clusters 4, 12, 14, 18, 19, 20 as oligodendroglia.
```{r}
oligos <- subset(RC17_norm, idents = c(4,12,14,18,19,20))
```
#### Some extra plots
```{r fig.height=4, fig.width=14}
Idents(oligos) <- "Sample"
VlnPlot(oligos, features = c("nFeature_RNA",
"nCount_RNA",
"subsets_Mito_percent"), pt.size = 0.2, cols = mycoloursP)
```
```{r fig.height=4, fig.width=8}
plot3 <- FeatureScatter(oligos,
feature1 = "nCount_RNA",
feature2 = "subsets_Mito_percent",
cols = mycoloursP)
plot4 <- FeatureScatter(oligos,
feature1 = "nCount_RNA",
feature2 = "nFeature_RNA",
cols = mycoloursP)
plot3 + plot4
```
### Re-analyze subsetted srt
```{r FindVariableFeatures, message=FALSE, warning=FALSE}
oligos <- FindVariableFeatures(oligos,
selection.method = "vst",
nfeatures = 2000)
```
```{r top10_VariableFeatures}
top10 <- head(VariableFeatures(oligos), 10)
```
```{r}
plot5 <- VariableFeaturePlot(oligos, cols = mycoloursP[4:5])
plot6 <- LabelPoints(plot = plot5,
points = top10,
repel = TRUE, xnudge = 0, ynudge = 0)
plot6
```
```{r ScaleData, message=FALSE, warning=FALSE}
all_genes <- rownames(oligos)
oligos <- ScaleData(oligos, features= all_genes)
```
```{r Linear_Dimensional_Reduction}
oligos <- RunPCA(oligos, features = VariableFeatures(object = oligos))
```
```{r PCA_plot, fig.height=4, fig.width=6}
DimPlot(oligos, reduction = "pca", cols = mycoloursP, pt.size = 1)
```
```{r ElbowPlot, fig.height=4, fig.width=6}
ElbowPlot(oligos)
```
Load original object:
```{r}
new_oligos <- oligos
rm(oligos)
dim(new_oligos)
```
```{r Load_Original_OL_object}
oligos <- readRDS(here("data", "Processed", "Original_Objects", "RC17_oligos.rds"))
dim(oligos)
```
```{r}
oligos@meta.data$RC17_AllCell_ClusterID <- new_oligos@meta.data$ClusterID
oligos@meta.data$Original_Dataset <- new_oligos@meta.data$Dataset
oligos@meta.data$RC17_AllCell_RNA_snn_res.0.8 <- new_oligos@meta.data$RNA_snn_res.0.8
oligos@meta.data$outlier <- oligos@meta.data$discard
oligos@meta.data$Dataset <- oligos@meta.data$RC17_Monolayer_OL
oligos@meta.data$percent.mito <- NULL
oligos@meta.data$LibSize_low <- NULL
oligos@meta.data$outlier <- oligos@meta.data$discard
oligos@meta.data$Dataset <- oligos@meta.data$RC17_Monolayer_OL
oligos@meta.data$LibSize_high <- NULL
oligos@meta.data$low_nFeatures <- NULL
oligos@meta.data$high_nFeatures <- NULL
oligos@meta.data$discard <- NULL
oligos@meta.data$ScaterQC <- NULL
oligos@meta.data$scDblFinder.sample <- NULL
oligos@meta.data$scDblFinder.cluster <- NULL
oligos@meta.data$scDblFinder.ratio <- NULL
oligos@meta.data$scDblFinder.weighted <- NULL
oligos@meta.data$scDblFinder.nearestClass <- NULL
oligos@meta.data$scDblFinder.difficulty <- NULL
oligos@meta.data$scDblFinder.cxds_score <- NULL
oligos@meta.data$scDblFinder.mostLikelyOrigin <- NULL
oligos@meta.data$scDblFinder.originAmbiguous <- NULL
oligos@meta.data$high_subsets_MitoPerc <- NULL
oligos@meta.data$label <- NULL
oligos@meta.data$RNA_snn_res.0.5 <- NULL
oligos@meta.data$RNA_snn_res.0.6 <- NULL
oligos@meta.data$RNA_snn_res.0.8 <- NULL
oligos@meta.data$RNA_snn_res.0.1 <- NULL
oligos@meta.data$RNA_snn_res.0.2 <- NULL
oligos@meta.data$RNA_snn_res.0.3 <- NULL
oligos@meta.data$RNA_snn_res.0.4 <- NULL
oligos@meta.data$RNA_snn_res.0.7 <- NULL
oligos@meta.data$RNA_snn_res.0.9 <- NULL
oligos@meta.data$RNA_snn_res.1 <- NULL
oligos@meta.data$seurat_clusters <- NULL
oligos@meta.data$CelltypeOL <- NULL
oligos@meta.data$Cluster_id <- NULL
oligos@meta.data$Tissue <- NULL
head(oligos@meta.data, 2)
```
```{r Clustering, message=FALSE, warning=FALSE}
oligos <- FindNeighbors(oligos, dims = 1:12, return.neighbor = TRUE, reduction = "pca")
oligos <- FindClusters(oligos, resolution = c(0.01, 0.05, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1))
head((oligos), 2)
```
```{r Non-Linear_Dimensional_Reduction, message=FALSE}
oligos <- RunUMAP(oligos, dims = 1:12)
```
Here, I will plot the different resolution calculated. I start with a resolution that is really low, just enough to differentiate between oligodendrocytes and OPCs.Then, I increase the resolutions incrementaly and only keep resolutions that are associated with the appearance of a new cluster. I stop at a resolutions where I have completely overclustered the cells; I will continue until resolution 1 is reached.
```{r fig.height=4, fig.width=6}
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.01", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.05", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.1", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.2", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.3", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.4", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.5", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.6", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.7", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.8", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.0.9", label = T, label.size = 6) + NoLegend()
DimPlot(oligos, reduction = "umap", cols= mycoloursP[6:40], group.by = "RNA_snn_res.1", label = T, label.size = 6) + NoLegend()
```
```{r eval=FALSE}
dir.create(here("Data", "Processed", "Oligodendroglia"))
saveRDS(oligos, here("Data", "Processed", "Oligodendroglia", "oligos_initial_srt.rds"))
```
```{r}
sessionInfo()
```