---
title: "Integration(comb_ol)_MonolayerOL"
author: "Nina-Lydia Kazakou"
date: "20/09/2021"
output: html_document
---
# Load required libraries
```{r}
library(Seurat)
library(ggsci)
library(dplyr)
library(devtools)
library(tidyverse)
library(here)
library(scales)
library(ggplot2)
```
# Set the colour pallete
```{r}
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, mypal5, mypal6, mypal7)
show_col(mycoloursP, labels =F)
```
# Load objects
```{r}
ol_comb <- readRDS(here("Materials", "GSE115011", "ol_comb_alt.co.oligos_pasca.rds"))
monolayer_RC17 <- readRDS("/Users/s1241040/Desktop/GFP32 vs RC17 Monolayer/Materials/RC17_oligos.rds")
```
# Explore the objects
```{r}
# Monolayer Oligodendrocytes
head(monolayer_RC17@meta.data)
dim(monolayer_RC17@assays$RNA@counts)
dim(monolayer_RC17@assays$RNA@data)
dim(monolayer_RC17@assays$RNA@scale.data)
DimPlot(monolayer_RC17, group.by = "Cluster_id", cols = mycoloursP[10:40], pt.size = 0.7, label = FALSE) & NoAxes()
```
# CleanUp the objects meta.data
```{r}
# Organoids Oligodendrocytes
keep.metadata.ol_comb <- c("orig.ident", "Cells", "Sample", "Phase", "ClusterID", "dataset")
ol_comb@meta.data <- ol_comb@meta.data[, keep.metadata.ol_comb]
head(ol_comb@meta.data)
# Add new column to meta.data to mark these cells
ol_comb@meta.data$Dataset <- "Organoids_combOL"
ol_comb@meta.data$ClusterRename <- ol_comb@meta.data$ClusterID
Idents(ol_comb) <- "ClusterID"
levels(ol_comb)
current.ids <- c("OPC", "mOL1", "pri-OPC", "Cyc", "imOL", "OPAC", "other OPC", "mOL2")
new.ids <- c("co.OPC", "co.mOL1", "co.pri-OPC", "co.Cyc", "co.imOL", "co.OPAC", "co.other OPC", "co.mOL2")
ol_comb@meta.data$ClusterRename <- plyr::mapvalues(x = ol_comb@meta.data$ClusterRename, from = current.ids, to = new.ids)
levels(ol_comb@meta.data$ClusterRename)
# Monolayer Oligodendrocytes
keep.metadata.mono <- c("orig.ident", "Cells", "Sample", "Phase", "Cluster_id")
monolayer_RC17@meta.data <- monolayer_RC17@meta.data[, keep.metadata.mono]
head(monolayer_RC17@meta.data)
# Add new column to meta.data to mark these cells
monolayer_RC17@meta.data$Dataset <- "MonolayerOL_RC17"
monolayer_RC17@meta.data$ClusterRename <- monolayer_RC17@meta.data$Cluster_id
Idents(monolayer_RC17) <- "Cluster_id"
levels(monolayer_RC17)
current.ids <- c("MAO", "opc1", "cyp2", "opc2", "oligo1", "oapc1", "cop", "oligo3", "cyp1", "opc3", "pri-opc", "oligo2")
new.ids <- c("mono.MAO", "mono.OPC1", "mono.Cyp2", "mono.OPC2", "mono.OL1", "mono.OAPC", "mono.COP", "mono.OL3", "mono.Cyp1", "mono.OPC3", "mono.pri-OPC", "mono.OL2")
monolayer_RC17@meta.data$ClusterRename <- plyr::mapvalues(x = monolayer_RC17@meta.data$ClusterRename, from = current.ids, to = new.ids)
head(monolayer_RC17@meta.data)
levels(monolayer_RC17@meta.data$ClusterRename)
```
# Label Transfer
```{r}
lt_anchors <- FindTransferAnchors(reference = monolayer_RC17 , query = ol_comb,
dims = 1:30)
predictions <- TransferData(anchorset = lt_anchors, refdata = monolayer_RC17$ClusterRename,
dims = 1:30)
lt_ol_comb <- AddMetaData(ol_comb, metadata = predictions)
```
```{r fig.width=7, fig.height=8, fig.fullwidth=TRUE}
DimPlot(lt_ol_comb, group.by = "predicted.id", split.by = "predicted.id",
cols = mycoloursP[6:40], ncol = 2,
pt.size = 1)
DimPlot(lt_ol_comb, group.by = "dataset", split.by = "predicted.id",
cols = mycoloursP[6:40], ncol = 2,
pt.size = 1)
```
# RPCA: a slightly modified workflow for the integration of scRNA-seq datasets.
```{r}
srt_list <- list(ol_comb, monolayer_RC17)
# Normalize and identify variable features for each dataset independently
srt_list<- lapply(X = srt_list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# select features that are repeatedly variable across datasets for integration run PCA on each dataset using these features
rpca_features <- SelectIntegrationFeatures(object.list = srt_list)
srt_list <- lapply(X = srt_list, FUN = function(x) {
x <- ScaleData(x, features = rpca_features, verbose = FALSE)
x <- RunPCA(x, features = rpca_features, verbose = FALSE)
})
```
```{r}
# Identify common anchors
rpca_anchors <- FindIntegrationAnchors(object.list = srt_list, anchor.features = rpca_features, reduction = "rpca", k.anchor = 5)
## To increase the strength of alignment I can increase the k.anchor, i.e. to 20
# this command creates an 'integrated' data assay
rpca_combined <- IntegrateData(anchorset = rpca_anchors)
```
```{r}
# Run a single integrated analysis on all cells
DefaultAssay(rpca_combined) <- "integrated"
# Run the standard workflow for visualization and clustering
rpca_combined <- ScaleData(rpca_combined, verbose = FALSE)
rpca_combined <- RunPCA(rpca_combined, npcs = 30, verbose = FALSE)
rpca_combined <- RunUMAP(rpca_combined, reduction = "pca", dims = 1:30)
rpca_combined <- FindNeighbors(rpca_combined, reduction = "pca", dims = 1:30)
rpca_combined <- FindClusters(rpca_combined, resolution = 0.5)
saveRDS(rpca_combined, here("Materials", "HumanOL", "MonolayerOL", file = "rpca_combined_MonolayerOL(RC17)_ol_comb.rds"))
```
# Vizualize RPCA
```{r}
DimPlot(rpca_combined, pt.size = 0.7, cols = mycoloursP[5:40], group.by = "Dataset") & NoAxes()
DimPlot(rpca_combined, group.by = "Dataset", split.by = "Dataset", pt.size = 0.7, cols = mycoloursP[5:40]) & NoAxes()
```
```{r fig.width=12, fig.height=6, fig.fullwidth=TRUE}
DimPlot(rpca_combined, group.by = "ClusterRename", pt.size = 0.7, cols = mycoloursP[3:40], label = TRUE) & NoAxes() & NoLegend()
DimPlot(rpca_combined, group.by = "ClusterRename", split.by = "Dataset", pt.size = 0.7, cols = mycoloursP[3:40], label = TRUE) & NoAxes() & NoLegend()
```
# Run CCA
```{r}
object.list <- list(ol_comb, monolayer_RC17)
# Normalize and identify variable features for each dataset independently
object.list<- lapply(X = object.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
# Select features that are repeatedly variable across datasets for integration
cca_features <- SelectIntegrationFeatures(object.list = object.list)
# Create anchors to integrate the datasets
cca_anchors <- FindIntegrationAnchors(object.list = object.list, anchor.features = cca_features, k.anchor = 5)
# Create an 'integrated' data assay
cca_combined <- IntegrateData(anchorset = cca_anchors)
```
# Perform integrated analysis
```{r}
# Specify that we will perform downstream analysis on the corrected data; set the assay to "integrated"
DefaultAssay(cca_combined) <- "integrated"
# Run the standard workflow for visualization and clustering
cca_combined <- ScaleData(cca_combined, verbose = FALSE)
cca_combined <- RunPCA(cca_combined, verbose = FALSE)
# Choose the number of PCs
ElbowPlot(cca_combined, ndims = 50) #20
cca_combined <- RunUMAP(cca_combined, reduction = "pca", dims = 1:30)
cca_combined <- FindNeighbors(cca_combined, reduction = "pca", dims = 1:30)
cca_combined <- FindClusters(cca_combined, resolution = 0.5)
saveRDS(cca_combined, here("Materials", "HumanOL", "MonolayerOL", file = "cca_combined_MonolayerOL_ol_comb.rds"))
```
# Plot Cells
```{r}
DimPlot(cca_combined, pt.size = 0.7, cols = mycoloursP[5:40]) & NoAxes()
DimPlot(cca_combined, pt.size = 0.7, cols = mycoloursP[5:40], group.by = "Dataset") & NoAxes()
DimPlot(cca_combined, group.by = "Dataset", split.by = "Dataset", pt.size = 0.7, cols = mycoloursP[5:40]) & NoAxes()
```
```{r fig.width=12, fig.height=6, fig.fullwidth=TRUE}
DimPlot(cca_combined, group.by = "ClusterRename", pt.size = 0.7, cols = mycoloursP[3:40], label = TRUE) & NoAxes() & NoLegend()
DimPlot(cca_combined, group.by = "ClusterRename", split.by = "Dataset", pt.size = 0.7, cols = mycoloursP[3:40], label = TRUE) & NoAxes() & NoLegend()
```
```{r}
sessionInfo()
```