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Loading packages
```{r}
library(colorspace)
library(Seurat)
library(mclust)
library(dplyr)
library(sctransform)
library(DESeq2)
library(cowplot)
library(patchwork)
library(harmony)
library(Rcpp)
library(tuple)
library(tidyr)
library(tidyverse)
library(ggtree)
library(stringdist)
library(ape)
library(data.table)
library(fastcluster)
library(ggplot2)
library(circlize)
library(pheatmap)
library(slingshot)
library(princurve)
```
Data read in 1 - Read10X
```{r}
covidold1798_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5921798/filtered_feature_bc_matrix/")
covidold1840_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5921840/filtered_feature_bc_matrix/")
covidyoung1925_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5921925/filtered_feature_bc_matrix/")
covidold2051_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5922051/filtered_feature_bc_matrix/")
covidyoung2423_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5922423/filtered_feature_bc_matrix/")
covidyoung2425_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5922425/filtered_feature_bc_matrix/")
covidyoung2427_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5922427/filtered_feature_bc_matrix/")
covidold2453_all.data <- Read10X(data.dir = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_GEX_analysis/Both_flowcell_alignments/5922453/filtered_feature_bc_matrix/")
```
Optional: Deletion of genes
```{r}
features_to_delete <- geneTCRBCR(Data = covidold1798_all.data)
covidold1798_all.data <- covidold1798_all.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = covidold1840_all.data)
covidold1840_all.data <- covidold1840_all.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = covidyoung1925_all.data)
covidyoung1925_all.data <- covidyoung1925_all.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = covidold2051_all.data)
covidold2051_all.data <- covidold2051_all.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = covidyoung2423_all.data)
covidyoung2423_all.data <- covidyoung2423_all.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = covidyoung2425_all.data)
covidyoung2425_all.data <- covidyoung2425_all.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = covidyoung2427_all.data)
covidyoung2427_all.data <- covidyoung2427_all.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = covidold2453_all.data)
covidold2453_all.data <- covidold2453_all.data[-features_to_delete,]
# healthy
features_to_delete <- geneTCRBCR(Data = h_donor.data)
h_donor.data <- h_donor.data[-features_to_delete,]
features_to_delete <- geneTCRBCR(Data = h_donor10k.data)
h_donor10k.data <- h_donor10k.data[-features_to_delete,]
geneTCRBCR <- function(Data = covidold1798.data){
TCRBCR.features <- c(grep('^TRAV', rownames(Data)),
grep('^TRAJ', rownames(Data)),
grep('^TRBV', rownames(Data)),
grep('^TRBD', rownames(Data)),
grep('^TRBJ', rownames(Data)),
grep('^IGHV', rownames(Data)),
grep('^IGHA', rownames(Data)),
grep('^IGHG', rownames(Data)),
grep('^IGHM', rownames(Data)),
grep('^IGHD', rownames(Data)),
grep('^IGHJ', rownames(Data)),
grep('^IGK', rownames(Data)),
grep('^IGL', rownames(Data)),
grep('^JCHAIN',rownames(Data)))
return(TCRBCR.features)
}
covid.all.BCRonly_nodoub_sct[grep('^IGH', rownames(covid.all.BCRonly_nodoub_sct@assays$RNA)),]
# TRAC, TRBC1, TRBC2, IGHE, JCHAIN
```
Data read in 2 - CreateSeuratObject/merge/QC
```{r}
covidold1798_all <- CreateSeuratObject(counts = covidold1798_all.data, project = "old1798", min.cells = 3, min.features = 200)
covidold1840_all <- CreateSeuratObject(counts = covidold1840_all.data, project = "old1840", min.cells = 3, min.features = 200)
covidyoung1925_all <- CreateSeuratObject(counts = covidyoung1925_all.data, project = "young1925", min.cells = 3, min.features = 200)
covidold2051_all <- CreateSeuratObject(counts = covidold2051_all.data, project = "old2051", min.cells = 3, min.features = 200)
covidyoung2423_all <- CreateSeuratObject(counts = covidyoung2423_all.data, project = "young2423", min.cells = 3, min.features = 200)
covidyoung2425_all <- CreateSeuratObject(counts = covidyoung2425_all.data, project = "young2425", min.cells = 3, min.features = 200)
covidyoung2427_all <- CreateSeuratObject(counts = covidyoung2427_all.data, project = "young2427", min.cells = 3, min.features = 200)
covidold2453_all <- CreateSeuratObject(counts = covidold2453_all.data, project = "old2453", min.cells = 3, min.features = 200)
covid.all <- merge(covidold1798_all, y = c(covidold1840_all, covidyoung1925_all, covidold2051_all,
covidyoung2423_all, covidyoung2425_all, covidyoung2427_all,
covidold2453_all),
add.cell.ids = c("old1798", "old1840", "young1925", "old2051", "young2423",
"young2425", "young2427", "old2453"),
project = "covidlateall")
# QC
covid.all[["percent.mt"]] <- PercentageFeatureSet(object = covid.all, pattern = "^MT-")
head([email protected], 5)
VlnPlot(covid.all.TCR_SCT_PCA, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
plot1 <- FeatureScatter(object = covid.all.BCRonly_nodoub16062020, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(object = covid.all, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
CombinePlots(plots = list(plot1, plot2))
#nFeature_RNA: genes per cell; nCount_RNA: molecules detected per cell
covid.all <- subset(x = covid.all, subset = nFeature_RNA > 150 & nFeature_RNA < 3500 & percent.mt < 10)
```
1. Dataset Preparation
- 1. Integrate TCR and BCR data to split into T and B cell subsets:
```{r}
covid.all.T <- covid.all
[email protected][[email protected]$orig.ident == "old1798",]
#old1798 old1840 young1925 old2051 young2423 young2425 young2427 old2453
covid.all.VDJ.1798_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921798/5921798_TC/outs/",
seurat_obj = covid.all.T,
prefix = "old1798_")
covid.all.VDJ.1840_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921840/5921840_TC/outs/",
seurat_obj = covid.all.T,
prefix = "old1840_")
covid.all.VDJ.1925_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921925/5921925_TC/outs/",
seurat_obj = covid.all.T,
prefix = "young1925_")
covid.all.VDJ.2051_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922051/5922051_TC/outs/",
seurat_obj = covid.all.T,
prefix = "old2051_")
covid.all.VDJ.2423_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922423/5922423_TC/outs/",
seurat_obj = covid.all.T,
prefix = "young2423_")
covid.all.VDJ.2425_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922425/5922425_TC/outs/",
seurat_obj = covid.all.T,
prefix = "young2425_")
covid.all.VDJ.2427_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922427/5922427_TC/outs/",
seurat_obj = covid.all.T,
prefix = "young2427_")
covid.all.VDJ.2453_TCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922453/5922453_TC/outs/",
seurat_obj = covid.all.T,
prefix = "old2453_")
all_VDJ_metadata <- rbind(covid.all.VDJ.1798_TCR, covid.all.VDJ.1840_TCR, covid.all.VDJ.1925_TCR, covid.all.VDJ.2051_TCR,
covid.all.VDJ.2423_TCR, covid.all.VDJ.2425_TCR, covid.all.VDJ.2427_TCR, covid.all.VDJ.2453_TCR)
rownames(all_VDJ_metadata) <- c(rownames(covid.all.VDJ.1798_TCR),rownames(covid.all.VDJ.1840_TCR), rownames(covid.all.VDJ.1925_TCR), rownames(covid.all.VDJ.2051_TCR), rownames(covid.all.VDJ.2423_TCR), rownames(covid.all.VDJ.2425_TCR), rownames(covid.all.VDJ.2427_TCR), rownames(covid.all.VDJ.2453_TCR))
covid.all.T <- AddMetaData(object=covid.all.T, metadata=all_VDJ_metadata)
Idents(covid.all.T) <- "TCR"
covid.all.T <- AddMetaData(covid.all.T, metadata =!is.na([email protected]$clonotype_id), col.name = "TCR")
covid.all.TCRonly <- subset(covid.all.T, subset = TCR == TRUE)
barplot(table([email protected]$clonotype_id))
Idents(covid.all.TCRonly) <- "Age"
FindMarkers(covid.all.TCRonly, ident.1 = "old", ident.2 = "young")
Idents(covid.all.T) <- "SCT_snn_res.0.4"
FindMarkers(covid.all.T, ident.1 = 3, min.pct = 0.25, logfc.threshold = 0.5)
```
- 1.2 BCR data integration:
```{r}
covid.all.B <- covid.all
[email protected][[email protected]$orig.ident == "old1798",]
#old1798 old1840 young1925 old2051 young2423 young2425 young2427 old2453
covid.all.VDJ.1798_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921798/5921798_BC/outs/",
seurat_obj = covid.all.B,
prefix = "old1798_")
covid.all.VDJ.1840_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921840/5921840_BC/outs/",
seurat_obj = covid.all.B,
prefix = "old1840_")
covid.all.VDJ.1925_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921925/5921925_BC/outs/",
seurat_obj = covid.all.B,
prefix = "young1925_")
covid.all.VDJ.2051_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922051/5922051_BC/outs/",
seurat_obj = covid.all.B,
prefix = "old2051_")
covid.all.VDJ.2423_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922423/5922423_BC/outs/",
seurat_obj = covid.all.B,
prefix = "young2423_")
covid.all.VDJ.2425_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922425/5922425_BC/outs/",
seurat_obj = covid.all.B,
prefix = "young2425_")
covid.all.VDJ.2427_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922427/5922427_BC/outs/",
seurat_obj = covid.all.B,
prefix = "young2427_")
covid.all.VDJ.2453_BCR <- add_clonotype(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922453/5922453_BC/outs/",
seurat_obj = covid.all.B,
prefix = "old2453_")
all_VDJ_metadata <- rbind(covid.all.VDJ.1798_BCR, covid.all.VDJ.1840_BCR, covid.all.VDJ.1925_BCR, covid.all.VDJ.2051_BCR,
covid.all.VDJ.2423_BCR, covid.all.VDJ.2425_BCR, covid.all.VDJ.2427_BCR, covid.all.VDJ.2453_BCR)
rownames(all_VDJ_metadata) <- c(rownames(covid.all.VDJ.1798_BCR),rownames(covid.all.VDJ.1840_BCR), rownames(covid.all.VDJ.1925_BCR), rownames(covid.all.VDJ.2051_BCR), rownames(covid.all.VDJ.2423_BCR), rownames(covid.all.VDJ.2425_BCR), rownames(covid.all.VDJ.2427_BCR), rownames(covid.all.VDJ.2453_BCR))
covid.all.B <- AddMetaData(object=covid.all.B, metadata=all_VDJ_metadata)
covid.all.B <- AddMetaData(covid.all.B, metadata =!is.na([email protected]$clonotype_id), col.name = "BCR")
covid.all.BCRonly <- subset(covid.all.B, subset = BCR == TRUE)
barplot(table([email protected]$clonotype_id))
Idents(covid.all.BCRonly) <- "Age"
FindMarkers(covid.all.BCRonly, ident.1 = 'old', ident.2 = 'young')
Idents(covid.all.BCRonly) <- "SCT_snn_res.0.4"
FindMarkers(covid.all.BCRonly, ident.1 = 3, min.pct = 0.25, logfc.threshold = 0.5)
```
- 2. Get rid of doublets:
```{r}
covid.all.TCRonly
covid.all.BCRonly
# Define cells that have TCR and BCR:
doublet_TCR_index <- rownames([email protected]) %in% rownames([email protected])
doublet_barcodes <- rownames([email protected])[doublet_TCR_index]
doublet_BCR_index <- rownames([email protected]) %in% doublet_barcodes
# Add metadata column to each object that marks doublets with TRUE:
covid.all.TCRonly <- AddMetaData(covid.all.TCRonly, metadata = doublet_TCR_index, col.name = "Doublets")
covid.all.BCRonly <- AddMetaData(covid.all.BCRonly, metadata = doublet_BCR_index, col.name = "Doublets")
# Subset data to get rid of doublets:
covid.all.TCRonly_nodoub <- subset(covid.all.TCRonly, subset = Doublets == FALSE)
covid.all.BCRonly_nodoub <- subset(covid.all.BCRonly, subset = Doublets == FALSE)
```
- 3. SCTransform and PCA on Euler cluster
```{r}
# Made on euler
seurat_object <- covid.all.TCRonly_nodoub
seurat_object <- covid.all.BCRonly_nodoub
# Split merged object to normalise separately:
seurat_object.list <- SplitObject(seurat_object, split.by = "orig.ident")
for (i in 1:length(seurat_object.list)) {
seurat_object.list[[i]] <- SCTransform(seurat_object.list[[i]], verbose = FALSE)
}
# Save variable features:
covid.features <- SelectIntegrationFeatures(object.list = seurat_object.list, nfeatures = 3000)
# Merge data and run PCA:
seurat_object.merged <- merge(seurat_object.list[[1]], y = seurat_object.list[2:length(seurat_object.list)], project = "ova.all", merge.data = TRUE)
VariableFeatures(seurat_object.merged) <- covid.features
seurat_object.merged <- RunPCA(object = seurat_object.merged, assay = "SCT", npcs = 50)
# Read in R object from euler:
```
- 3.1 Harmony and UMAP:
```{r}
seurat_object <- covid.all.TCRonly_nodoub_sct
seurat_object <- covid.all.BCRonly_nodoub_sct
seurat_object <- RunHarmony(object = seurat_object,
assay.use = "SCT",
reduction = "pca",
dims.use = 1:50,
group.by.vars = "orig.ident",
plot_convergence = TRUE)
seurat_object <- RunUMAP(object = seurat_object, assay = "SCT", reduction = "harmony", dims = 1:50)
seurat_object <- FindNeighbors(object = seurat_object, assay = "SCT", reduction = "harmony", dims = 1:50)
seurat_object <- FindClusters(object = seurat_object, resolution = .5)
saveRDS(o_ova.all, "/Users/fbieberich/Desktop/o_ova_all4.rds")
```
- 4 Dataset Split and CleanUp - Split T cell dataset into cd4 and cd8 and get rid of CD3 and PCs in B cell dataset:
```{r}
# T cells
covid.all.TCRonly_nodoub_sct.cd4 <- subset(covid.all.TCRonly_nodoub_sct,
cells = WhichCells(covid.all.TCRonly_nodoub_sct, expression = CD4>0 & CD8A==0))
covid.all.TCRonly_nodoub_sct.cd8 <- subset(covid.all.TCRonly_nodoub_sct,
cells = WhichCells(covid.all.TCRonly_nodoub_sct, expression = CD4==0 & CD8A>0))
covid.all.TCRonly_nodoub_sct.dn <- subset(covid.all.TCRonly_nodoub_sct,
cells = WhichCells(covid.all.TCRonly_nodoub_sct, expression = CD4==0 & CD8A==0 & CD8B==0))
seurat_object <- covid.all.TCRonly_nodoub_sct.cd8
seurat_object.list <- SplitObject(seurat_object, split.by = "orig.ident")
for (i in 1:length(seurat_object.list)) {
seurat_object.list[[i]] <- SCTransform(seurat_object.list[[i]], verbose = FALSE)
}
# Save variable features:
covid.features <- SelectIntegrationFeatures(object.list = seurat_object.list, nfeatures = 3000)
# Merge data and run PCA:
seurat_object.merged <- merge(seurat_object.list[[1]], y = seurat_object.list[2:length(seurat_object.list)], project = "covid_late_TCR", merge.data = TRUE)
VariableFeatures(seurat_object.merged) <- covid.features
# B cells
# Filter out CD3+ cells from B cell subset:
seurat_object <- covid.all.BCRonly_nodoub_sct
bcell.nocd3 <- subset(seurat_object, cells=WhichCells(seurat_object, expression = CD3E==0))
bcell.nocd3nopc <- subset(bcell.nocd3, cells=WhichCells(bcell.nocd3, idents = c(0,1,2,3,4,5,6,8)))
# PCA, Harmony, UMAP for both:
seurat_object <- bcell.nocd3nopc
seurat_object <- RunPCA(object = seurat_object, assay = "SCT", npcs = 50)
#seurat_object <- seurat_object.merged
seurat_object <- RunHarmony(object = seurat_object,
assay.use = "SCT",
reduction = "pca",
dims.use = 1:50,
group.by.vars = "orig.ident",
plot_convergence = TRUE)
seurat_object <- RunUMAP(object = seurat_object, assay = "SCT", reduction = "harmony", dims = 1:50)
seurat_object <- FindNeighbors(object = seurat_object, assay = "SCT", reduction = "harmony", dims = 1:50)
seurat_object <- FindClusters(object = seurat_object, resolution = 0.3)
bcell.nocd3nopc <- seurat_object
```
2. VDJ read in and preparation
- 1.1 Read in, rename and redefine BCR clonotypes:
```{r}
old5921798_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921798/5921798_BC/outs/", prefix = "old1798_", VDJinfo = c("IGH", "IGK", "IGL"))
old5921840_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921840/5921840_BC/outs/", prefix = "old1840_", VDJinfo = c("IGH", "IGK", "IGL"))
young5921925_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921925/5921925_BC/outs/", prefix = "young1925_", VDJinfo = c("IGH", "IGK", "IGL"))
old5922051_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922051/5922051_BC/outs/", prefix = "old2051_", VDJinfo = c("IGH", "IGK", "IGL"))
young5922423_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922423/5922423_BC/outs/", prefix = "young2423_", VDJinfo = c("IGH", "IGK", "IGL"))
young5922425_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922425/5922425_BC/outs/", prefix = "young2425_", VDJinfo = c("IGH", "IGK", "IGL"))
young5922427_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922427/5922427_BC/outs/", prefix = "young2427_", VDJinfo = c("IGH", "IGK", "IGL"))
old5922453_BCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922453/5922453_BC/outs/", prefix = "old2453_", VDJinfo = c("IGH", "IGK", "IGL"))
```
- 1.2 Read in, rename and redefine TCR clonotypes:
```{r}
old5921798_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921798/5921798_TC/outs/", prefix = "old1798_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
old5921840_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921840/5921840_TC/outs/", prefix = "old1840_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
young5921925_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921925/5921925_TC/outs/", prefix = "young1925_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
old5922051_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922051/5922051_TC/outs/", prefix = "old2051_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
young5922423_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922423/5922423_TC/outs/", prefix = "young2423_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
young5922425_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922425/5922425_TC/outs/", prefix = "young2425_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
young5922427_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922427/5922427_TC/outs/", prefix = "young2427_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
old5922453_TCR <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922453/5922453_TC/outs/", prefix = "old2453_", VDJinfo = c("TRA", "TRB", "TRB"), reversing=T)
```
- 1.3 Read in, rename and redefine Plasmacell clonotypes:
```{r}
old5921798_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921798/5921798_PC/outs/", prefix = "old1798_", VDJinfo = c("IGH", "IGK", "IGL"))
old5921840_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921840/5921840_PC/outs/", prefix = "old1840_", VDJinfo = c("IGH", "IGK", "IGL"))
young5921925_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5921925/5921925_PC/outs/", prefix = "young1925_", VDJinfo = c("IGH", "IGK", "IGL"))
old5922051_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922051/5922051_PC/outs/", prefix = "old2051_", VDJinfo = c("IGH", "IGK", "IGL"))
young5922423_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922423/5922423_PC/outs/", prefix = "young2423_", VDJinfo = c("IGH", "IGK", "IGL"))
young5922425_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922425/5922425_PC/outs/", prefix = "young2425_", VDJinfo = c("IGH", "IGK", "IGL"))
young5922427_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922427/5922427_PC/outs/", prefix = "young2427_", VDJinfo = c("IGH", "IGK", "IGL"))
old5922453_PC <- filter_clonotypes(tcr_location = "/Users/fbieberich/Documents/ETH_projects/Covid19_PC_BCR_10x_project/Late_timepoint_patients/10X_VDJ_alignments/5922453/5922453_PC/outs/", prefix = "old2453_", VDJinfo = c("IGH", "IGK", "IGL"))
```
- 2. Prepare data:
```{r}
old1798_B <- clonoype.def(old5921798_BCR)
old1840_B <- clonoype.def(old5921840_BCR)
old2051_B <- clonoype.def(old5922051_BCR)
old2453_B <- clonoype.def(old5922453_BCR)
young1925_B <- clonoype.def(young5921925_BCR)
young2423_B <- clonoype.def(young5922423_BCR)
young2425_B <- clonoype.def(young5922425_BCR)
young2427_B <- clonoype.def(young5922427_BCR)
old1798_T <- clonoype.def(old5921798_TCR, whichColumn=6)
old1840_T <- clonoype.def(old5921840_TCR, whichColumn=6)
old2051_T <- clonoype.def(old5922051_TCR, whichColumn=6)
old2453_T <- clonoype.def(old5922453_TCR, whichColumn=6)
young1925_T <- clonoype.def(young5921925_TCR, whichColumn=6)
young2423_T <- clonoype.def(young5922423_TCR, whichColumn=6)
young2425_T <- clonoype.def(young5922425_TCR, whichColumn=6)
young2427_T <- clonoype.def(young5922427_TCR, whichColumn=6)
old1798_T_exp <- expand.barc(old1798_T)
old1840_T_exp <- expand.barc(old1840_T)
old2051_T_exp <- expand.barc(old2051_T)
old2453_T_exp <- expand.barc(old2453_T)
young1925_T_exp <- expand.barc(young1925_T)
young2423_T_exp <- expand.barc(young2423_T)
young2425_T_exp <- expand.barc(young2425_T)
young2427_T_exp <- expand.barc(young2427_T)
old1798_B_exp <- expand.barc(old1798_B)
old1840_B_exp <- expand.barc(old1840_B)
old2051_B_exp <- expand.barc(old2051_B)
old2453_B_exp <- expand.barc(old2453_B)
young1925_B_exp <- expand.barc(young1925_B)
young2423_B_exp <- expand.barc(young2423_B)
young2425_B_exp <- expand.barc(young2425_B)
young2427_B_exp <- expand.barc(young2427_B)
tcr_all.list <- list(old1798_T,
old1840_T,
old2051_T,
old2453_T,
young1925_T,
young2423_T,
young2425_T,
young2427_T)
bcr_all.list <- list(old1798_B,
old1840_B,
old2051_B,
old2453_B,
young1925_B,
young2423_B,
young2425_B,
young2427_B)
tcr_all.final <- rbind(old1798_T,
old1840_T,
old2051_T,
old2453_T,
young1925_T,
young2423_T,
young2425_T,
young2427_T)
bcr_all.final <- rbind(old1798_B,
old1840_B,
old2051_B,
old2453_B,
young1925_B,
young2423_B,
young2425_B,
young2427_B)
```
3. Merge VDJ info as GEX metadata
- 1. Expansion level:
```{r}
un_exp <- c(old1798_T_exp[[3]],
old1840_T_exp[[3]],
old2051_T_exp[[3]],
old2453_T_exp[[3]],
young1925_T_exp[[3]],
young2423_T_exp[[3]],
young2425_T_exp[[3]],
young2427_T_exp[[3]])
low_exp <- c(old1798_B_exp[[2]],
old1840_B_exp[[2]],
old2051_B_exp[[2]],
old2453_B_exp[[2]],
young1925_B_exp[[2]],
young2423_B_exp[[2]],
young2425_B_exp[[2]],
young2427_B_exp[[2]])
rownames([email protected])
explevel <- rep(NA, length(rownames([email protected])))
explevel[rownames([email protected]) %in% high_exp] <- "high_exp"
explevel[rownames([email protected]) %in% low_exp] <- "low_exp"
explevel[rownames([email protected]) %in% un_exp] <- "un_exp"
table(explevel)
bcell.nocd3nopc <- AddMetaData(bcell.nocd3nopc, metadata = explevel, col.name = "Explevel")
```
- 2. Isotype:
```{r}
isotype_patient.list <- list(
old1798_B,
old1840_B,
old2051_B,
old2453_B,
young1925_B,
young2423_B,
young2425_B,
young2427_B)
# Prepare to add to patient GEX metadata:
datalist = list()
for(i in 1:8){
object.filt <- isotype_patient.list[[i]][isotype_patient.list[[i]]$same_clone,]
object.filt.iso <- as.data.frame(object.filt[!is.na(object.filt$c_gene),c("c_gene")])
rownames(object.filt.iso) <- as.data.frame(object.filt[!is.na(object.filt$c_gene),c("barcode")])[,1]
datalist[[i]] <- object.filt.iso
}
coercdata <- do.call(rbind, datalist)
coercdata$c_gene
coercdata$c_gene[grep('^IGHA', coercdata$c_gene)] <- "IGHA"
coercdata$c_gene[grep('^IGHG', coercdata$c_gene)] <- "IGHG"
# Add this to metadata to patient GEX:
bcell.nocd3nopc <- AddMetaData(bcell.nocd3nopc, metadata = coercdata, col.name = "isotype")
[email protected]$isotype[[email protected]$isotype == "None"] <- NA
table([email protected]$isotype)
```
-3. Variabel usage of CD4 and CD8 TCR sequences:
```{r}
cd4tcrs <- tcr_all.final[tcr_all.final$barcode %in% rownames([email protected]),]
cd8tcrs <- tcr_all.final[tcr_all.final$barcode %in% rownames([email protected]),]
cd4tcrs$barcode
table(cd8tcrs[,c(1,3)])/max(table(cd8tcrs[,c(1,3)]))
# Without all possible gene names - no uniform heatmap axes:
table(cd4tcrs[,c(1,3)]) %>%
as.data.frame() %>%
ggplot() +
aes(x=v_gene_a, y=v_gene_b, fill=Freq) %>%
geom_tile(colour="white",size=0.15) +
scale_fill_gradient(
low = "white",
high = "blue",
space = "Lab",
na.value = "white",
guide = "colourbar",
aesthetics = "fill"
) +
theme(axis.text.x = element_text(angle = 45, hjust = 1), plot.title = element_text(hjust = 0.5)) +
ggtitle("VH-Vl gene usage young2427_B")
####
```
4. GEX analysis
- 1. Add cell state information to GEX metadata:
```{r}
# Rename clusters to make cell phenotype plots:
Ball <- c("FCER2", "CD27","TNFRSF13B","CD83", "FCRL3", "CD1C")
T8all <- c('SELL', 'TCF7', 'IL7R', 'CD40LG', 'GZMB', 'NKG7', "PDCD1", "LAG3")
T4all <- c('SELL', 'LEF1', 'S100A4', 'CCL5', 'GZMK', 'FOXP3')
t8naive <- c(0,3,5)
t8mem <- c(2,4)
t8eff <- c(1,6)
t4naive <- c(0,2,7)
t4mem <- c(1,3,5)
t4eff <- c(4)
t4reg <- c(6)
bnaive <- c(3)
bmem <- c(1,2,8)
bact <- c(0,4,7)
bmz <- c(5,6)
seurat_object <- bcell.nocd3nopc
seurat_object <- covid.all.TCRonly_nodoub_sct.cd8
seurat_object <- covid.all.TCRonly_nodoub_sct.cd4
state <- rep(NA, length([email protected]$SCT_snn_res.0.5))
state[[email protected]$SCT_snn_res.0.5 %in% t8naive] <- "Naive"
state[[email protected]$SCT_snn_res.0.5 %in% t8mem] <- "Memory"
state[[email protected]$SCT_snn_res.0.5 %in% t8eff] <- "Effector"
#state[[email protected]$SCT_snn_res.0.5 %in% t4reg] <- "Regulatory"
seurat_object <- AddMetaData(seurat_object, metadata = state, col.name = "State")
Idents(seurat_object) <- "State"
# Cell state colors:
# Naive - blue = #6D94CD
# Memory - green = #7CAE2A
# Activated - red = #EE756E
# Treg - purple = #A080B9
# MZ -teal = #2AB6BE
DimPlot(seurat_object, reduction = "umap", pt.size = .1, label = F, label.size = 5,
group.by = "State", cols = c("#EE756E", "#7CAE2A", "#6D94CD"))
covid.all.TCRonly_nodoub_sct.cd8 <- seurat_object
```
- 2. T and B cell marker analysis:
```{r}
sum(rownames([email protected]) %in% tcr_all.final$barcode)
sum(tcr_all.final$barcode %in% rownames([email protected]))
seurat_object <- bcell.nocd3nopc
seurat_object <- covid.all.TCRonly_nodoub_sct.cd8
seurat_object <- covid.all.TCRonly_nodoub_sct.cd4
DimPlot(seurat_object, reduction = "umap", pt.size = .4, group.by = "State")
Ball <- c("FCER2", "CD27","TNFRSF13B","CD83", "FCRL3", "CD1C")
T8all <- c('SELL', 'TCF7', 'IL7R', 'CD40LG', 'GZMB', 'NKG7', "PDCD1", "LAG3")
T4all <- c('SELL', 'LEF1', 'S100A4', 'CCL5', 'GZMK', 'FOXP3')
VlnPlot(seurat_object, features = c("CD44", "IL7R"))
Idents(bcell.nocd3nopc) <- "State"
eff8 <- subset(covid.all.TCRonly_nodoub_sct.cd8, cells = WhichCells(covid.all.TCRonly_nodoub_sct.cd8, idents = "Effector"))
Idents(eff8) <- "orig.ident"
FeaturePlot(seurat_object, features = c("NFATC1"), split.by = 'State')#, order = T, pt.size = 1)
#FeaturePlot(covid.all.BCRonly_nodoub, features = c("VIM","PPIB", "HSP90B1","CALR","DUSP1", "HLA-DRA", "CD74", "HLA-DQA1", "HLA-DRB1"))
DimPlot(seurat_object, group.by = "seurat_clusters", label = T)
FindAllMarkers(seurat_object)
exhausted <- c('HAVCR2', 'LAG3', 'ITGAE', 'PDCD1', 'CTLA4')
predys <- c('CXCL13', 'GZMK')
naive <- c('CCR7', 'TCF7', 'SELL', 'LEF1')
memory <- c('IL7R', 'CCR7', 'TCF7')
cytotoxic <- c('PRF1', 'GZMB', 'NKG7', 'KLRG1')
prolif <- c("STMN1", "MKI67")
# Exhaustion TF's: "PRDM1", "MAF"
#Heatmap:
Idents(seurat_object) <- "seurat_clusters"
seurat_object.marker <- FindAllMarkers(seurat_object, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
top10 <- seurat_object.marker %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(seurat_object, features = top10$gene) + NoLegend()
# Barplot of patient distribution of different cell states:
datalist = list()
patients <- c('old1798', 'old1840', 'old2051', 'old2453', 'young1925', 'young2423', 'young2425', 'young2427')
for(i in 1:8){
rowlocation <- [email protected]$orig.ident == patients[i]
state_count <- table([email protected]$State[rowlocation])#/sum(rowlocation)
patient <- rep(patients[i],4)
datalist[[i]] <- data.frame(patient, state_count)
names(datalist[[i]]) <-c("Patient", "CellTypes", "Frequency")
}
coercdata <- do.call(rbind, datalist)
ggplot(coercdata, aes(fill=CellTypes, y=Frequency, x=Patient)) +
geom_bar(position="fill", stat="identity") + ggtitle("B Cell Type Ratio per Patient")
Idents(covid.all.TCRonly_nodoub_sct.cd8) <- "State"
effsubset <- subset(covid.all.TCRonly_nodoub_sct.cd8, cells = WhichCells(covid.all.TCRonly_nodoub_sct.cd8, idents = "Effector"))
memsubset <- subset(covid.all.TCRonly_nodoub_sct.cd8, cells = WhichCells(covid.all.TCRonly_nodoub_sct.cd8, idents = "Memory"))
effsubset.count <- table([email protected]$orig.ident)
memsubset.count <- table([email protected]$orig.ident)
cd8patient <- table([email protected]$orig.ident)
effsubset.count/cd8patient
memsubset.count/cd8patient
# Barplot of patient distribution of the main cell states:
cd4patient <- table([email protected]$orig.ident)
cd8patient <- table([email protected]$orig.ident)
bpatient <- table([email protected]$orig.ident)
Patient <- c(rep(patients,2))
CellType <- c(rep("CD8 T cell",8), rep("CD4 T cell",8))
Frequency <- c(cd8patient, cd4patient)
df <- data.frame(Patient, CellType, Frequency)
df[order(df$Patient),]
ggplot(df[order(df$Patient),], aes(fill=CellType, y=Frequency, x=Patient)) +
geom_bar(position="fill", stat="identity") + ggtitle("Ratio of CD4 to CD8 T cells per Patient")
cd4patient/(cd8patient+cd4patient)
seurat_object <- bcell.nocd3nopc
seurat_object <- covid.all.TCRonly_nodoub_sct.cd8
seurat_object <- covid.all.TCRonly_nodoub_sct.cd4
datalist = list()
patients <- c('old1798', 'old1840', 'old2051', 'old2453', 'young1925', 'young2423', 'young2425', 'young2427')
for (i in 1:8){
datalist[[i]] <- table([email protected]$State[[email protected]$orig.ident == patients[i]])/sum([email protected]$orig.ident == patients[i])
}
table([email protected]$State)/length([email protected]$State)
```
- 3. Effector CD8 T cell sub cell states and interpatient differences:
```{r}
Idents(covid.all.TCRonly_nodoub_sct.cd8) <- "seurat_clusters"
FeaturePlot(covid.all.TCRonly_nodoub_sct.cd8, features = c('LAG3','GZMK', "PRF1", "GZMB","CD44", "IL7R"))#, split.by = "orig.ident")
DimPlot(covid.all.TCRonly_nodoub_sct.cd8, group.by = "seurat_clusters", label = T)
# Effector clusters are 1,2,6: some (in cluster 2) are both effector and memory T cell state-like
cd8eff <- subset(covid.all.TCRonly_nodoub_sct.cd8, cells = WhichCells(covid.all.TCRonly_nodoub_sct.cd8, idents = c(1,2,6)))
cd8eff <- RunUMAP(object = cd8eff, assay = "SCT", reduction = "harmony", dims = 1:50)
cd8eff <- FindNeighbors(object = cd8eff, assay = "SCT", reduction = "harmony", dims = 1:50)
cd8eff <- FindClusters(object = cd8eff, resolution = .5)
table([email protected]$orig.ident)
DimPlot(cd8eff, group.by = "seurat_clusters", label = T, split.by = "orig.ident")
FeaturePlot(cd8eff, features = c('LAG3','GZMK', "PRF1", "GZMB","CD44", "IL7R"), split.by = "orig.ident")
Idents(cd8eff) <- "seurat_clusters"
seurat_object.marker <- FindAllMarkers(cd8eff, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
top10 <- seurat_object.marker %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
DoHeatmap(cd8eff, features = top10$gene, group.by = "orig.ident")
Idents(cd8eff) <- "orig.ident"
markergenes <- FindMarkers(cd8eff, ident.1 = c('old1798', 'old1840', 'old2051', 'old2453'), ident.2 = c('young1925', 'young2423', 'young2425', 'young2427'))
Idents(covid.all.TCRonly_nodoub_sct.cd8) <- "State"
cd8eff <- subset(covid.all.TCRonly_nodoub_sct.cd8, cells = WhichCells(covid.all.TCRonly_nodoub_sct.cd8, idents = "Effector"))
FindMarkers(covid.all.TCRonly_nodoub_sct.cd8, ident.1 = "Effector", ident.2 = "Naive")
DoHeatmap(cd8eff, features = c("NFKBIA", "HLA-DQB1", "GZMA", "NKG7", "GZMH", "TOB1", "PLEK", "PRF1", "GZMB", "LGALS1", "CST7", "LTB", "GNLY", "TNF"), group.by = "orig.ident")
VlnPlot(cd8eff, features = c('ISG15', 'IFI44', 'IFI27', 'CXCL10', 'RSAD2', 'IFIT1', 'IFI44L', 'CCL8', 'XAF1', 'GBP1', 'IRF7', 'CEACAM1'), group.by = "orig.ident")
# Heatmap of age and expansion markers for all patients in the CD8eff subset:
seurat_object.marker <- FindMarkers(cd8eff,
ident.1 = c('old1798', 'old1840', 'old2051', 'old2453'),
ident.2 = c('young1925', 'young2423', 'young2425', 'young2427'),
only.pos = F, min.pct = 0.3, logfc.threshold = 0.3, max.cells.per.ident
= 500)
features.sick <- rownames(seurat_object.marker[order(-seurat_object.marker$avg_logFC),])
#top10 <- seurat_object.marker %>% group_by(cluster) %>% top_n(n = 10, wt = avg_logFC)
features.sick.filt <- features.sick[c(-grep(features.sick, pattern = "^M"))]
p1 <- RidgePlot(cd8eff, features = features.sick.filt, log=T)
features = features.sick.filt
datalist <- list()
for(i in 1:length(features)){
datalist[[i]] <- aggregate(p1[[i]]$data[,1], by=list(p1[[i]]$data[,2]), FUN=mean)[,2]
}
coercdata <- do.call(rbind, datalist)
colnames(coercdata) <- names(table(p1[[1]]$data[,2]))[table(p1[[1]]$data[,2]) >0]
rownames(coercdata) <- features
coercdata <- coercdata[,c(1,2,4,8,3,5,6,7)]
expmarkers <- coercdata[rownames(coercdata) %in% c('GZMB', 'GZMH', 'LGALS1', 'PRF1', 'ADGRG1', 'FGFBP2'),]
agemarkers <- coercdata[!rownames(coercdata) %in% c('GZMB', 'GZMH', 'LGALS1', 'PRF1', 'ADGRG1', 'FGFBP2'),]
expmarkers <- expmarkers[,c(2,8,3,7,5,6,1,4)]
pheatmap(expmarkers, scale = "row",main = "DGE by clonal expansion level",
color = viridis(100),
cluster_rows = T,
cluster_cols =F)
# Gene ontology score
FeaturePlot(cd8eff, features = c("GZMB", "CD8A"))
Idents(cd8eff) <- "orig.ident"
cd8eff.markers <- FindMarkers(cd8eff, ident.1 = c('old1798', 'old1840', 'old2051', 'old2453'), ident.2 = c('young1925', 'young2423', 'young2425', 'young2427'))
cd8eff.markers <- cd8eff.markers[order(cd8eff.markers$avg_logFC),]
cd8eff.markers$group <- c(rep("young", 6), rep("old" ,23))
dplyr::count(cd8eff.markers, group)
m_df<- msigdbr(species = "Homo sapiens", category = "C7")
fgsea_sets<- m_df %>% split(x = .$gene_symbol, f = .$gs_name)
names(fgsea_sets)[grep("CD8" ,names(fgsea_sets))]
cd8eff.markers$gene <- rownames(cd8eff.markers)
cd8eff.markers %>%
arrange(desc(avg_logFC), desc(pct.1)) %>%
head(n = 10)
o.genes<- cd8eff.markers %>%
arrange(desc(pct.1)) %>%
dplyr::select(gene, pct.1)
ranks<- deframe(o.genes)
fgseaRes<- fgsea(fgsea_sets, stats = ranks, nperm = 1000)
fgseaResTidy <- fgseaRes %>%
as_tibble() %>%
arrange(desc(NES))
fgseaResTidy %>%
dplyr::select(-leadingEdge, -ES, -nMoreExtreme) %>%
arrange(padj) %>%
head()
# only plot the top 20 pathways
ggplot(fgseaResTidy %>% filter(pval < 0.05) %>% head(n= 20), aes(reorder(pathway, NES), NES)) +
geom_col(aes(fill= NES < 7.5)) +
coord_flip() +
labs(x="Pathway", y="Normalized Enrichment Score",
title="Hallmark pathways NES from GSEA") +
theme_minimal()
plotEnrichment(fgsea_sets[["GSE24081_CONTROLLER_VS_PROGRESSOR_HIV_SPECIFIC_CD8_TCELL_UP"]],
ranks) + labs(title="GSE30962_ACUTE_VS_CHRONIC_LCMV_PRIMARY_INF_CD8_TCELL_UP")
#"GSE24081_CONTROLLER_VS_PROGRESSOR_HIV_SPECIFIC_CD8_TCELL_DN"
#"GSE24081_CONTROLLER_VS_PROGRESSOR_HIV_SPECIFIC_CD8_TCELL_UP"
#[219] "GSE25846_IL10_POS_VS_NEG_CD8_TCELL_DAY7_POST_CORONAVIRUS_BRAIN_DN"
#[220] "GSE25846_IL10_POS_VS_NEG_CD8_TCELL_DAY7_POST_CORONAVIRUS_BRAIN_UP"
#[315] "GSE30962_ACUTE_VS_CHRONIC_LCMV_PRIMARY_INF_CD8_TCELL_DN"
#[316] "GSE30962_ACUTE_VS_CHRONIC_LCMV_PRIMARY_INF_CD8_TCELL_UP"
#[317] "GSE30962_ACUTE_VS_CHRONIC_LCMV_SECONDARY_INF_CD8_TCELL_DN"
#[318] "GSE30962_ACUTE_VS_CHRONIC_LCMV_SECONDARY_INF_CD8_TCELL_UP"
#[319] "GSE30962_PRIMARY_VS_SECONDARY_ACUTE_LCMV_INF_CD8_TCELL_DN"
#[320] "GSE30962_PRIMARY_VS_SECONDARY_ACUTE_LCMV_INF_CD8_TCELL_UP"
#[321] "GSE30962_PRIMARY_VS_SECONDARY_CHRONIC_LCMV_INF_CD8_TCELL_DN"
#[322] "GSE30962_PRIMARY_VS_SECONDARY_CHRONIC_LCMV_INF_CD8_TCELL_UP"
```
- 4. SingleR celltype comparison:
```{r}
library(SingleR)
library(celldex)
hpca.se <- HumanPrimaryCellAtlasData()
hpca.se
rownames(bcell.nocd3nopc@assays$SCT)
covid.all.TCRonly_nodoub_sct.cd8
covid.all.TCRonly_nodoub_sct.cd4
pred.hesc <- SingleR(test = bcell.nocd3nopc@assays$SCT, ref = hpca.se,
labels = hpca.se$label.main)
```
5. VDJ analysis
- 1. Clonal expansion in Barplot:
```{r}
# Clonotype rate per patient in barplot:
datalist = list()
patients <- c('old1798', 'old1840', 'old2051', 'old2453', 'young1925', 'young2423', 'young2425', 'young2427')
for(i in 1:8){
bcr <- bcr_all.list[[i]]
clono_count <- sort(table(bcr$clonotype_new[bcr$clonotype_size > 1]), decreasing = T)#/sum(rowlocation)
patient <- rep(patients[i],length(clono_count))
print(paste0("patient",i,": ",length(patient)))
patientdf <- data.frame(patient, clono_count)
patientdf$Clone <- seq(1,nrow(patientdf))
datalist[[i]] <- patientdf[order(patientdf[,3], decreasing = T),]
#datalist[[i]] <- data.frame(patient, clono_count)
names(datalist[[i]]) <-c("Patient", "Clonotype", "Frequency","Clone")
}
coercdata <- do.call(rbind, datalist)
coercdata[coercdata$Frequency>=5,]-> subset.expanded
filter(subset.expanded, Patient == "young1925") %>% pull(Frequency) %>% sum
ggplot(coercdata[order(coercdata$Clone, decreasing = T),], aes(Patient, Frequency, fill = Clone)) +
geom_bar(position = position_fill(reverse = F),stat = "identity", col = "black", show.legend = F) + ggtitle("Clonal expansion of T Cells")
ggplot(coercdata[order(coercdata$Clone, decreasing = T),], aes(Patient, Frequency, fill = Clone)) +
geom_bar(stat = "identity", col = "black", show.legend = F) + ggtitle("Clonal expansion of T Cells")
p1 <- ggplot(coercdata[order(coercdata$Clone, decreasing = T),], aes(Patient, Frequency, fill = Clone)) +
geom_bar(stat = "identity", col = "black", show.legend = F)
p1
p1 + scale_y_continuous(limits=c(0, 600), n.breaks = 10)
```
- 2. SHM rate calculation:
```{r}
bcr_all_patients <- list(old5921798_BCR,
old5921840_BCR,
old5922051_BCR,
old5922453_BCR,
young5921925_BCR,
young5922423_BCR,
young5922425_BCR,
young5922427_BCR)
bcr_patient_clones <- list(old1798_B,
old1840_B,
old2051_B,
old2453_B,
young1925_B,
young2423_B,
young2425_B,
young2427_B)
b_exp.list <- list(
old1798_B_exp,
old1840_B_exp,
old2051_B_exp,
old2453_B_exp,
young1925_B_exp,
young2423_B_exp,
young2425_B_exp,
young2427_B_exp)
##########
###25.08.2020
##########
###Final figure for paper
# Generate SHM dataframe with barcodes as rownames:
patients <- c(5921798,5921840,5922051,5922453,5921925,5922423,5922425,5922427)
ig_prefix_patient <- c("old1798_","old1840_","old2051_","old2453_","young1925_","young2423_","young2425_","young2427_")
i <- 1
shm.1798 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.1798$patient <- rep("old1798",nrow(shm.1798))
shm.1798.clonal <- shm.1798[!duplicated(shm.1798$clonotype_new),c(6,7,8,9)]
i <- 2
shm.1840 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.1840$patient <- rep("old1840",nrow(shm.1840))
shm.1840.clonal <- shm.1840[!duplicated(shm.1840$clonotype_new),c(6,7,8,9)]
i <- 3
shm.2051 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.2051$patient <- rep("old2051",nrow(shm.2051))
shm.2051.clonal <- shm.2051[!duplicated(shm.2051$clonotype_new),c(6,7,8,9)]
i <- 4
shm.2453 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.2453$patient <- rep("old2453",nrow(shm.2453))
shm.2453.clonal <- shm.2453[!duplicated(shm.2453$clonotype_new),c(6,7,8,9)]
i <- 5
shm.1925 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.1925$patient <- rep("young1925",nrow(shm.1925))
shm.1925.clonal <- shm.1925[!duplicated(shm.1925$clonotype_new),c(6,7,8,9)]
i <- 6
shm.2423 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.2423$patient <- rep("young2423",nrow(shm.2423))
shm.2423.clonal <- shm.2423[!duplicated(shm.2423$clonotype_new),c(6,7,8,9)]
i <- 7
shm.2425 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.2425$patient <- rep("young2425",nrow(shm.2425))
shm.2425.clonal <- shm.2425[!duplicated(shm.2425$clonotype_new),c(6,7,8,9)]
i <- 8
shm.2427 <- makeSHMdf(bcr_loc = patients[i],
prefix.pat = ig_prefix_patient[i],
bcr_VDJ = bcr_patient_clones[[i]])
shm.2427$patient <- rep("young2427",nrow(shm.2427))
shm.2427.clonal <- shm.2427[!duplicated(shm.2427$clonotype_new),c(6,7,8,9)]
shm.all <- rbind(shm.1798,
shm.1840,
shm.2051,
shm.2453,
shm.1925,
shm.2423,
shm.2425,
shm.2427)
shm.alllist <- list(shm.1798,
shm.1840,
shm.2051,
shm.2453,
shm.1925,
shm.2423,
shm.2425,
shm.2427)
clonal.shm.all <- rbind(shm.1798.clonal,
shm.1840.clonal,
shm.2051.clonal,
shm.2453.clonal,
shm.1925.clonal,
shm.2423.clonal,
shm.2425.clonal,
shm.2427.clonal)
shm.all$SHM <- shm.all$SHM/2
clonal.shm.all$clonal_SHM <- clonal.shm.all$clonal_SHM/2
```
- 3. SHM rate boxplots:
```{r}
#Idents(bcell.nocd3nopc) <- "Explevel"
#shm.high_exp <- shm.all[rownames(shm.all) %in% WhichCells(bcell.nocd3nopc, idents = "high_exp"),]
#shm.high_exp$Exp <- "3_high_exp"
#shm.low_exp <- shm.all[rownames(shm.all) %in% WhichCells(bcell.nocd3nopc, idents = "low_exp"),]
#shm.low_exp$Exp <- "2_low_exp"
#shm.un_exp <- shm.all[rownames(shm.all) %in% WhichCells(bcell.nocd3nopc, idents = "un_exp"),]
#shm.un_exp$Exp <- "1_un_exp"
###
# By clonal expansion level: (1=hexp, 2=exp, 3=unexp)
datalist.final =NULL
for (i in 1:8){
datalist <- b_exp.list[[i]][[3]]
datalist.final <- c(datalist.final,datalist)
}
datalist.final.hexp <- datalist.final
datalist.final.exp <- datalist.final
datalist.final.unexp <- datalist.final
clonal.shm.all_VH <- clonal.shm.all[,c(1,4)]
clonal.shm.all_VL <- clonal.shm.all[,c(2,4)]
clonal.shm.all_VHVL <- clonal.shm.all[,c(3,4)]
clonal.shm <- clonal.shm.all_VHVL
shm.high_exp <- clonal.shm[rownames(clonal.shm) %in% datalist.final.hexp,]