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integration_harmony_3rd_no_ciliated.Rmd
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---
title: "Batch effect correction with Harmony"
author: "Reka_Toth"
date: '`r format(Sys.time(), "%d %B, %Y")`'
output:
# html_notebook: default
workflowr::wflow_html:
toc: false
code_folding: "hide"
#output: workflowr::wflow_html:
editor_options:
chunk_output_type: console
---
```{r include=FALSE}
knitr::opts_chunk$set(message=FALSE, warning=FALSE)
```
```{r message=FALSE, warning=FALSE}
library(Seurat)
library(dplyr)
library(ggplot2)
library(cowplot)
library(patchwork)
library(harmony)
library(cerebroApp)
library(biomaRt)
library(future)
###########libraries and functions#############
if (grepl("Windows", Sys.getenv("OS"))){
PATH ="V:/"} else {
PATH ="/C010-projects/"}
if (grepl("Windows", Sys.getenv("OS"))){
PATH_Y="N:/"} else {
PATH_Y="/C010-datasets/"}
PATH_ICGC <- "/icgc/"
plan("multiprocess", workers = 2)
options(future.globals.maxSize= 5000*1024^2)
DATA = paste0(PATH, "Reka/33_CoO_lung/scRNASeq/data")
DATA_datasets = paste0(PATH_Y, "External/Sotillo_mouse_lung_cell/scRNASeq_CoO_lung/scrnaseq_analysis/")
RESULT = "./output/"
CODE = "./code/"
CALLS = paste0(PATH_Y, "External/Sotillo_mouse_lung_cell/scRNASeq_CoO_lung/cellranger_output_30_07/")
CALLS2 = paste0(PATH_Y, "External/Sotillo_mouse_lung_cell/scRNASeq_CoO_lung/cellranger_output_22_11/")
# Basic function to convert human to mouse gene names
convertHumanGeneList <- function(x){
require("biomaRt")
human = useMart("ensembl", dataset = "hsapiens_gene_ensembl")
mouse = useMart("ensembl", dataset = "mmusculus_gene_ensembl")
genesV2 = getLDS(attributes = c("hgnc_symbol"), filters = "hgnc_symbol", values = x , mart = human, attributesL = c("mgi_symbol"), martL = mouse, uniqueRows=T)
humanx <- unique(genesV2[, 2])
# Print the first 6 genes found to the screen
return(humanx)
}
```
# Reading in the individual datasets
## control01_tam
```{r control01_tam, message=F, warning=F, eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS, "control01_tam/filtered_feature_bc_matrix/"))
control01_tam <- CreateSeuratObject(counts = sc.data, project = "control01_tam", min.cells = 3, min.features = 200)
control01_tam[["percent.mt"]] <- PercentageFeatureSet(control01_tam, pattern = "^mt")
control01_tam <- subset(control01_tam, subset = nFeature_RNA > 1000 & nFeature_RNA < 6000 & percent.mt < 10)
control01_tam <- NormalizeData(control01_tam, normalization.method = "LogNormalize", scale.factor = 10000)
control01_tam <- FindVariableFeatures(control01_tam, selection.method = "vst", nfeatures = 2000)
#colnames(control01_tam) <- paste0("control01_tam_", colnames(control01_tam))
```
## eav1_4wks_1
```{r message=FALSE, warning=FALSE, eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS, "eavl-4wk/filtered_feature_bc_matrix/"))
eav1_4wks <- CreateSeuratObject(counts = sc.data, project = "eav1_4wks_1", min.cells = 3, min.features = 200)
eav1_4wks[["percent.mt"]] <- PercentageFeatureSet(eav1_4wks, pattern = "^mt")
eav1_4wks <- subset(eav1_4wks, subset = nFeature_RNA > 1500 & nFeature_RNA < 6000 & percent.mt < 10)
eav1_4wks <- NormalizeData(eav1_4wks, normalization.method = "LogNormalize", scale.factor = 10000)
eav1_4wks <- FindVariableFeatures(eav1_4wks, selection.method = "vst", nfeatures = 2000)
#colnames(eav1_4wks) <- paste0("eav1_4wks", colnames(eav1_4wks))
```
## eav1_endpoint
```{r eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS, "eavl-endpoint/filtered_feature_bc_matrix/"))
eav1_endpoint <- CreateSeuratObject(counts = sc.data, project = "eav1_endpoint", min.cells = 3, min.features = 200)
eav1_endpoint[["percent.mt"]] <- PercentageFeatureSet(eav1_endpoint, pattern = "^mt")
eav1_endpoint <- subset(eav1_endpoint, subset = nFeature_RNA > 1000 & nFeature_RNA < 6000 & percent.mt < 10)
eav1_endpoint <- NormalizeData(eav1_endpoint, normalization.method = "LogNormalize", scale.factor = 10000)
eav1_endpoint <- FindVariableFeatures(eav1_endpoint, selection.method = "vst", nfeatures = 2000)
#colnames(eav1_endpoint) <- paste0("eav1_endpoint", colnames(eav1_endpoint))
```
## control01_cas9
```{r, eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS, "control01-cas9/filtered_feature_bc_matrix/"))
control01_cas9 <- CreateSeuratObject(counts = sc.data, project = "control01_cas9", min.cells = 3, min.features = 200)
control01_cas9[["percent.mt"]] <- PercentageFeatureSet(control01_cas9, pattern = "^mt")
control01_cas9 <- subset(control01_cas9, subset = nFeature_RNA > 1000 & nFeature_RNA < 6000 & percent.mt < 10)
control01_cas9 <- NormalizeData(control01_cas9, normalization.method = "LogNormalize", scale.factor = 10000)
control01_cas9 <- FindVariableFeatures(control01_cas9, selection.method = "vst", nfeatures = 2000)
#colnames(control01_cas9) <- paste0("control01_cas9", colnames(control01_cas9))
```
## eav1_2wks_1
```{r, eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS, "eavl-2wk/filtered_feature_bc_matrix/"))
eav1_2wks <- CreateSeuratObject(counts = sc.data, project = "eav1_2wks_1", min.cells = 3, min.features = 200)
eav1_2wks[["percent.mt"]] <- PercentageFeatureSet(eav1_2wks, pattern = "^mt")
eav1_2wks <- subset(eav1_2wks, subset = nFeature_RNA > 1000 & nFeature_RNA < 6000 & percent.mt < 10)
eav1_2wks <- NormalizeData(eav1_2wks, normalization.method = "LogNormalize", scale.factor = 10000)
eav1_2wks <- FindVariableFeatures(eav1_2wks, selection.method = "vst", nfeatures = 2000)
#colnames(eav1_2wks) <- paste0("eav1_2wks", colnames(eav1_2wks))
```
## eav1_2wks_2
```{r eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS2, "2w-v1/filtered_feature_bc_matrix/"))
eav2_2wks <- CreateSeuratObject(counts = sc.data, project = "eav1_2wks_2", min.cells = 3, min.features = 200)
eav2_2wks[["percent.mt"]] <- PercentageFeatureSet(eav2_2wks, pattern = "^mt")
eav2_2wks <- subset(eav2_2wks, subset = nFeature_RNA > 2000 & nFeature_RNA < 6000 & percent.mt < 10)
eav2_2wks <- NormalizeData(eav2_2wks, normalization.method = "LogNormalize", scale.factor = 10000)
eav2_2wks <- FindVariableFeatures(eav2_2wks, selection.method = "vst", nfeatures = 2000)
#colnames(eav1_2wks) <- paste0("eav1_2wks", colnames(eav1_2wks))
```
## eav1_4wks_2
```{r eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS2, "4w-v1/filtered_feature_bc_matrix/"))
eav2_4wks <- CreateSeuratObject(counts = sc.data, project = "eav1_4wks_2", min.cells = 3, min.features = 200)
eav2_4wks[["percent.mt"]] <- PercentageFeatureSet(eav2_4wks, pattern = "^mt")
eav2_4wks <- subset(eav2_4wks, subset = nFeature_RNA > 2000 & nFeature_RNA < 6000 & percent.mt < 10)
eav2_4wks <- NormalizeData(eav2_4wks, normalization.method = "LogNormalize", scale.factor = 10000)
eav2_4wks <- FindVariableFeatures(eav2_4wks, selection.method = "vst", nfeatures = 2000)
#colnames(eav1_2wks) <- paste0("eav1_2wks", colnames(eav1_2wks))
```
## end_SPC
```{r eval=F}
sc.data <- Read10X(data.dir = file.path(CALLS2, "end_SPC/filtered_feature_bc_matrix/"))
end_SPC <- CreateSeuratObject(counts = sc.data, project = "end_SPC", min.cells = 3, min.features = 200)
end_SPC[["percent.mt"]] <- PercentageFeatureSet(end_SPC, pattern = "^mt")
end_SPC <- subset(end_SPC, subset = nFeature_RNA > 2000 & nFeature_RNA < 6000 & percent.mt < 10)
end_SPC <- NormalizeData(end_SPC, normalization.method = "LogNormalize", scale.factor = 10000)
end_SPC <- FindVariableFeatures(end_SPC, selection.method = "vst", nfeatures = 2000)
#colnames(eav1_2wks) <- paste0("eav1_2wks", colnames(eav1_2wks))
```
## Simple merge of the datasets
```{r eval=F}
#sc.list <- list("control01_cas9"=control01_cas9, "control01_tam"=control01_tam, "eav1_2wks"=eav1_2wks, "eav2_2wks"=eav2_2wks,
# "eav1_4wks"=eav1_4wks, "eav2_4wks"=eav2_4wks, "eav1_endpoint"=eav1_endpoint, "end_SPC"=end_SPC)
#for (i in 1:length(sc.list)){
#sc.list[[i]] <- SCTransform(sc.list[[i]], verbose = FALSE, vars.to.regress = "percent.mt")}
#gcdata <- merge(sc.list[["control01_cas9"]], y=c(sc.list[["control01_tam"]], sc.list[["eav1_2wks"]], sc.list[["eav2_2wks"]],
# sc.list[["eav1_4wks"]], sc.list[["eav2_4wks"]], sc.list[["eav1_endpoint"]]), project = "lung_coo")
gcdata <- merge(control01_cas9, y=c(control01_tam, eav1_2wks, eav2_2wks,
eav1_4wks, eav2_4wks, eav1_endpoint), project = "lung_coo")
rm(list = c("control01_cas9", "control01_tam", "eav1_4wks", "eav2_4wks", "eav2_2wks", "eav1_2wks", "eav1_endpoint", "end_SPC"))
gc()
#saveRDS(gcdata, file = paste0(DATA_datasets, "gdc_unscaled.RDS"))
```
```{r eval=F}
#gcdata <- readRDS(file = paste0(DATA, "gdc_uncorrected.RDS"))
Macosko_cell_cycle_genes <- read.delim(file.path(DATA, "Macosko_cell_cycle_genes.txt"))
#m.s.genes <- convertHumanGeneList(cc.genes.updated.2020$s.genes)
#m.g2m.genes <- convertHumanGeneList(cc.genes.updated.20190019$g2m.genes)
#gcdata <- CellCycleScoring(gcdata, s.features = m.s.genes, g2m.features = m.g2m.genes, set.ident = F)
#m.s.genes <- convertHumanGeneList(Macosko_cell_cycle_genes$S)
#m.g2m.genes <- convertHumanGeneList(Macosko_cell_cycle_genes$G2.M)
m.s.genes <- readRDS( file=paste0(DATA, "/cellcyclegenes.RDS"))[[1]]
m.g2m.genes <- readRDS( file=paste0(DATA, "/cellcyclegenes.RDS"))[[2]]
```
## Data without batch effect correction
```{r message=FALSE, warning=FALSE, eval=F}
gcdata <- readRDS( file = paste0(DATA_datasets, "gdc_unscaled.RDS"))
all.genes <- rownames(gcdata)
gcdata <- FindVariableFeatures(gcdata, selection.method = "vst", nfeatures = 4000)
gcdata <- CellCycleScoring(gcdata, s.features = m.s.genes, g2m.features = m.g2m.genes, set.ident = F)
gcdata[["percent.ribo"]] <- PercentageFeatureSet(gcdata, pattern = "^Rb")
#gcdata[["percent.mito"]] <- PercentageFeatureSet(gcdata, pattern = "^mt")
gcdata <- ScaleData(gcdata, verbose = FALSE, features = all.genes)
#gcdata <- RunPCA(gcdata, npcs = 30, verbose = FALSE, assay="SCT")
saveRDS(gcdata, file = paste0(DATA_datasets, "gdc_uncorrected.RDS"))
```
```{r message=FALSE, warning=FALSE}
gcdata <- readRDS( file = paste0(DATA_datasets, "gdc_uncorrected.RDS"))
gcdata <- RunPCA(gcdata, npcs = 30, verbose = FALSE)
gcdata <- RunUMAP(gcdata, reduction = "pca", dims = 1:30)
p1 <- DimPlot(gcdata, reduction = "umap", group.by = "orig.ident")
p1
```
# Harmony
```{r message=FALSE, warning=FALSE}
#set.seed(4552)
#set.seed(453)
set.seed(897)
harmony_data <- gcdata %>%
RunHarmony("orig.ident", plot_convergence = TRUE)
# RunHarmony("orig.ident", plot_convergence = TRUE, assay.use="SCT")
#harmony_data <- readRDS( file=file.path(DATA, "harmony_integrated_2nd.RDS"))
all.genes <- rownames(harmony_data)
harmony_data <- harmony_data %>%
RunUMAP(reduction = "harmony", dims = 1:30) %>%
FindNeighbors(reduction = "harmony", dims = 1:30) %>%
# FindClusters(resolution = 0.5) %>%
FindClusters(resolution = 0.5) %>%
identity()
```
## Sample exclusion
Select samples that are in cluster 11 or 13, most likely ciliated cells.
```{r}
## exclusion
exclude_samples <- harmony_data$seurat_clusters %in% c("11", "13")
```
## Exclude samples and re-run harmony
```{r}
gcdata <- readRDS( file = paste0(DATA_datasets, "gdc_uncorrected.RDS"))
gcdata <- gcdata[,!exclude_samples]
gcdata <- RunPCA(gcdata, npcs = 30, verbose = FALSE)
gcdata <- RunUMAP(gcdata, reduction = "pca", dims = 1:30)
p1 <- DimPlot(gcdata, reduction = "umap", group.by = "orig.ident")
p1
set.seed(897)
harmony_data <- gcdata %>%
RunHarmony("orig.ident", plot_convergence = TRUE)
# RunHarmony("orig.ident", plot_convergence = TRUE, assay.use="SCT")
#harmony_data <- readRDS( file=file.path(DATA, "harmony_integrated_2nd.RDS"))
rm(gcdata)
gc()
```
```{r}
## Plots to decide on how many clusters we have
VizDimLoadings(harmony_data, dims = 1:2, reduction = "pca")
DimPlot(harmony_data, reduction = "pca")
DimHeatmap(harmony_data, dims = 1:3, cells = 500, balanced = TRUE)
DimHeatmap(harmony_data, dims = 4:6, cells = 500, balanced = TRUE)
DimHeatmap(harmony_data, dims = 7:9, cells = 500, balanced = TRUE)
DimHeatmap(harmony_data, dims = 10:12, cells = 500, balanced = TRUE)
DimHeatmap(harmony_data, dims = 13:15, cells = 500, balanced = TRUE)
#harmony_data <- JackStraw(harmony_data, num.replicate = 100, reduction = "harmony")
#harmony_data <- ScoreJackStraw(harmony_data, dims = 1:25)
#JackStrawPlot(harmony_data, dims = 1:20)
ElbowPlot(harmony_data)
```
## Finding clusters
```{r, results='asis'}
all.genes <- rownames(harmony_data)
harmony_data <- harmony_data %>%
RunUMAP(reduction = "harmony", dims = 1:30) %>%
FindNeighbors(reduction = "harmony", dims = 1:30) %>%
# FindClusters(resolution = 0.5) %>%
FindClusters(resolution = 0.6) %>%
identity()
#harmony_data[["percent.mt"]] <- PercentageFeatureSet(harmony_data, pattern = "^mt")
DimPlot(harmony_data, reduction = "umap", label = TRUE, pt.size = .1)
DimPlot(harmony_data, reduction = "umap", label = TRUE, pt.size = .1, group.by = "orig.ident")
### Mitochondrial RNA
FeaturePlot(harmony_data, reduction = "umap", features = "percent.mt")
DimPlot(harmony_data, reduction = "umap", label = F, pt.size = .1, group.by = "Phase")
#for (i in seq(from = 1, to = length(m.g2m.genes), by = 3)){
# print(RidgePlot(harmony_data, features = m.g2m.genes[i:(i+2)], ncol=3))
#}
#for (i in seq(from = 1, to = length(m.s.genes), by = 3)){
# print(RidgePlot(harmony_data, features = m.s.genes[i:(i+2)], ncol=3))
#}
#harmony_data.markers <- FindAllMarkers(harmony_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25, slot = )
```
## Identification of cluster specific markers
### Top 5 genes per cluster
```{r}
harmony_data.markers <- FindAllMarkers(harmony_data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
top_markers <- harmony_data.markers %>% group_by(cluster) %>% top_n(n = 5, wt = avg_logFC)
DT::datatable(top_markers)
DT::datatable(harmony_data.markers)
```
## Distribution of marker genes by cluster
```{r message=FALSE, warning=FALSE}
for (i in seq(from = 1, to = length(top_markers$gene), by = 3)){
print(RidgePlot(harmony_data, features = top_markers$gene[i:(i+2)], ncol=3))
}
#RidgePlot(lung.integrated, features = top_markers$gene , ncol = 3)
```
## Heatmap of the gene expression of the top 10 markers by cluster
```{r}
top10 <- harmony_data.markers %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(harmony_data, features = top10$gene) + NoLegend() + RotatedAxis()
```
# Plot genes
```{r}
par(mfrow = c(2, 3))
FeaturePlot(harmony_data, reduction = "umap", features = c("Sftpc", "Foxj1", "Hopx", "Bpifa1", "H2-K1", "Scgb1a1"))
```
# Marker genes
## Cell Stem Cell paper
```{r}
m1 <- c("Sftpc", "Foxj1", "Scgb1a1", "Scgb3a2", "Cd74", "Cd14", "H2-K1", "AW112010")
m2 <- c("Cdkn1c", "Cdkn1a", "Trp53", "Rnm1", "Pcna", "Cdc20", "Cks1b", "Smc4", "Tmpo", "Cdk1", "Slbp", "Tubb4b", "Cks2", "Hmgb2")
DotPlot(harmony_data, features = m1) + RotatedAxis()
DotPlot(harmony_data, features = m2) + RotatedAxis()
```
## Export data
```{r, eval=T}
saveRDS(harmony_data, file=file.path(DATA, "harmony_integrated_3rd_no_ciliated.RDS"))
saveRDS(harmony_data.markers, file=file.path(DATA, "harmony_integrated.markers_3rd_no_ciliated.RDS"))
#harmony_data <- addPercentMtRibo(harmony_data, organism = "mm", gene_nomenclature = "name") %>%
# getMostExpressedGenes(column_cluster = "seurat_clusters", column_sample = NULL) %>%
# getMarkerGenes(organism = "mm", column_cluster = "seurat_clusters", column_sample = "orig.ident") %>%
# getEnrichedPathways(lcolumn_cluster = "seurat_clusters", column_sample = "orig.ident") %>%
# performGeneSetEnrichmentAnalysis(GMT_file = file.path(DATA, 'msigdb.v7.1.symbols.gmt'), column_cluster = "seurat_clusters", column_sample = "orig.ident", min.sz = 2 )
#saveRDS(harmony_data, file=file.path(DATA, "harmony_integrated.RDS"))
#saveRDS(harmony_data.markers, file=file.path(DATA, "harmony_integrated.markers.RDS"))
#exportFromSeurat(harmony_data, file = file.path(DATA, "cerebro_harmony.crb"), experiment_name = "Lung_coo", organism = "mm", column_sample = "orig.ident", column_cluster = "seurat_clusters", column_nGene = "nFeature_RNA", column_nUMI = "nCount_RNA", add_all_meta_data = T)
```
```{r, eval=T}
harmony_data <- addPercentMtRibo(harmony_data, organism = "mm", gene_nomenclature = "name") %>%
getMostExpressedGenes(column_sample = "orig.ident", column_cluster = "seurat_clusters") %>%
getMarkerGenes(organism = "mm", column_cluster = "seurat_clusters", column_sample = "orig.ident") %>%
getEnrichedPathways(column_sample = "orig.ident", column_cluster = "seurat_clusters") %>%
performGeneSetEnrichmentAnalysis(GMT_file = file.path(DATA, 'msigdb.v7.1.symbols.gmt'), column_cluster = "seurat_clusters", column_sample = "orig.ident", min.sz = 2 )
#saveRDS(harmony_data, file=file.path(DATA, "harmony_integrated_3rd.RDS"))
#saveRDS(harmony_data.markers, file=file.path(DATA, "harmony_integrated.markers_3rd.RDS"))
#exportFromSeurat(harmony_data, file = file.path(DATA, "cerebro_harmony_3rd.crb"), experiment_name = "Lung_coo", organism = "mm", column_sample = "orig.ident", column_cluster = "seurat_clusters", column_nGene = "nFeature_RNA", column_nUMI = "nCount_RNA", add_all_meta_data = T)
```