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SS2_fullAnalysis.Rmd
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SS2_fullAnalysis.Rmd
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---
title: "R Notebook"
output: html_notebook
---
# Setup
Load libraries
```{r}
library(Seurat)
library(switchde)
library(patchwork)
source("/nfs/team205/tpcg/bin/scripts/usefulFunctions.R")
gene_names = read.table("../../../10x-runs/ricardo/3324STDY7421492/outs/filtered_gene_bc_matrices/mm10/genes.tsv")
gene_names_human = read.csv("saved_data/gene_names_human_GRCh38_93.csv",
header = T)
colours_mouse = RColorBrewer::brewer.pal(12, "Paired")[c(5,6, 1,2, 9,10)]
colours_mel = rev(RColorBrewer::brewer.pal(11, "RdYlBu")[c(1,11)])
colours_h_ind = c(RColorBrewer::brewer.pal(11, "RdYlBu")[c(9,10)], RColorBrewer::brewer.pal(11, "RdYlGn")[2:4])
colours_h_tc = c(RColorBrewer::brewer.pal(11, "PuOr")[8:10], RColorBrewer::brewer.pal(9, "Greens")[4], RColorBrewer::brewer.pal(9, "BuGn")[6], RColorBrewer::brewer.pal(9, "Greens")[9], RColorBrewer::brewer.pal(9, "YlOrRd")[c(3,4)], RColorBrewer::brewer.pal(12, "Paired")[6])
```
# QC and Seurat formatting
Load pre-QC data
```{r}
load("./saved_data/SS2/preQC_data.RData")
```
Filter cells with technical problems and non-single-cells (not QC)
```{r}
infoTable_list_filtered = list()
filtered_datasets = list()
for(n in names(infoTable_list_unfiltered)){
if("technical_problems" %in% colnames(infoTable_list_unfiltered[[n]])){
good_cells = infoTable_list_unfiltered[[n]]$technical_problems=="no" & infoTable_list_unfiltered[[n]]$nr.of.cells==1
} else{
good_cells = infoTable_list_unfiltered[[n]]$nr.of.cells==1
}
infoTable_list_filtered[[n]] = infoTable_list_unfiltered[[n]][good_cells,]
filtered_datasets[[n]] = list("counts" = raw_datasets_unfiltered[[n]]$counts[,good_cells],
"tpm" = raw_datasets_unfiltered[[n]]$tpm[,good_cells])
}
```
Add GD cell info
```{r}
for(n in names(infoTable_list_filtered)){
notGD = ((infoTable_list_filtered[[n]]$tracer_A_1!="No" &
(infoTable_list_filtered[[n]]$tracer_pG_1=="No" & infoTable_list_filtered[[n]]$tracer_pG_2=="No") |
(infoTable_list_filtered[[n]]$tracer_pD_1=="No" & infoTable_list_filtered[[n]]$tracer_pD_2=="No")) |
(infoTable_list_filtered[[n]]$tracer_B_1!="No" &
(infoTable_list_filtered[[n]]$tracer_pG_1=="No" & infoTable_list_filtered[[n]]$tracer_pG_2=="No") |
(infoTable_list_filtered[[n]]$tracer_pD_1=="No" & infoTable_list_filtered[[n]]$tracer_pD_2=="No")))
infoTable_list_filtered[[n]]$clonotypes = ifelse(notGD, as.character(infoTable_list_filtered[[n]]$tracer_clonotype_simp), "gdTcell")
#print(table(infoTable_list_filtered[[n]]$clonotypes))
}
```
Look at all datasets' QC values distribution by tissue/cell - violin plots
```{r, fig.height=3, fig.width=3.8}
qc_vals = c("total_reads", "p_ercc", "p_mt", "n_genes", "p_unmapped")
qc_thr = c(log10(250000), 25, 10, log10(1750), 30)
names(qc_thr) = qc_vals
qc_violin_list = list()
pdf("./plots/SS2/QC/QC_violin.pdf", paper = "a4", useDingbats = F)
for(n in names(infoTable_list_filtered)){
plot_list = list()
plot_df = infoTable_list_filtered[[n]][,c(qc_vals, "tissue_cell")]
if(n=="human"){
qc_thr = c(log10(100000), 50, 20, log10(1000), 60)
names(qc_thr) = qc_vals
cols_pal = colours_h_tc
} else{
cols_pal = colours_mouse
plot_df$tissue_cell = gsub(".", " ", plot_df$tissue_cell, fixed = T)
plot_df$tissue_cell = factor(plot_df$tissue_cell, levels = c("colon Tmem",
"colon Treg",
"skin Tmem",
"skin Treg",
"LN Tmem",
"LN Treg",
"spleen Tmem",
"spleen Treg"))
}
for(qc in qc_vals){
axistit = c("# Reads", "% ERCC", "% Mt", "# Genes", "% Unmapped")[which(qc_vals==qc)]
if(!grepl("p_", qc)) plot_df[,qc] = log10(plot_df[,qc]+1)
plot_list[[qc]] = ggplot(plot_df, aes_string(x = "tissue_cell", y = qc, fill = "tissue_cell"))+
geom_violin(draw_quantiles = c(0.25, 0.5, 0.75), size = 0.2)+
labs(x = "Tissue/Cell Type", y = axistit)+
geom_hline(yintercept = qc_thr[qc],
linetype = "dashed")+
scale_fill_manual(values = cols_pal)+
theme_classic()+
theme(aspect.ratio = 1,
text = element_text(colour = "black"),
legend.position = "none",
axis.text.x = element_text(size = 4.7, colour = "black",
angle = 33, hjust = 1, vjust = 1),
axis.text.y = element_text(size = 4.7, colour = "black"),
axis.title = element_text(colour = "black", size = 5.5))
print(plot_list[[qc]])
}
plots = plot_list$total_reads + plot_list$p_ercc + plot_list$p_mt + plot_list$n_genes + plot_list$p_unmapped +
plot_layout(nrow = 2, ncol = 3)
qc_violin_list[[n]] = patchworkGrob(plots)
}
dev.off()
save(qc_violin_list, file = "./FINAL_PLOTS/plot_repository/SS2_qc_violin_list.RData")
```
Filter based on QC
```{r}
qc_thr = c(250000, 25, 10, 1750, 30)
names(qc_thr) = c("total_reads", "p_ercc", "p_mt", "n_genes", "p_unmapped")
meta_rejected = list()
exp_rejected = list()
for(n in names(infoTable_list_filtered)){
if(n=="human") qc_thr[1:5] = c(100000, 50, 20, 1000, 60)
cells = rownames(infoTable_list_filtered[[n]])[infoTable_list_filtered[[n]]$total_reads>=qc_thr["total_reads"] &
infoTable_list_filtered[[n]]$p_ercc<=qc_thr["p_ercc"] &
infoTable_list_filtered[[n]]$p_mt<=qc_thr["p_mt"] &
infoTable_list_filtered[[n]]$n_genes>=qc_thr["n_genes"] &
infoTable_list_filtered[[n]]$p_unmapped<=qc_thr["p_unmapped"]]
meta_rejected[[n]] = infoTable_list_filtered[[n]]
meta_rejected[[n]]$qc_out = ifelse(rownames(meta_rejected[[n]]) %in% cells, "No", "Yes")
infoTable_list_filtered[[n]] = infoTable_list_filtered[[n]][cells,]
filtered_datasets[[n]]$counts = filtered_datasets[[n]]$counts[,cells]
exp_genes = rownames(filtered_datasets[[n]]$counts)[apply(filtered_datasets[[n]]$counts, 1, function(x) length(x[x>1])>=3)]
exp_rejected[[n]] = filtered_datasets[[n]]$counts[exp_genes,]
filtered_datasets[[n]]$counts = filtered_datasets[[n]]$counts[exp_genes,]
filtered_datasets[[n]]$tpm = filtered_datasets[[n]]$tpm[,cells]
filtered_datasets[[n]]$tpm = filtered_datasets[[n]]$tpm[exp_genes,]
}
lapply(meta_rejected, function(x) table(x$qc_out)/nrow(x)) # QC removal fractions
```
Filter non ab T cells. Then, clean clonotypes (if only one, cell goes into notAssigned)
```{r}
for(n in names(infoTable_list_filtered)){
good_cells = !(infoTable_list_filtered[[n]]$clonotypes %in% c("gdTcell", "iNKT", "MAIT", "NoTCR", "Multi_recomb") |
is.na(infoTable_list_filtered[[n]]$clonotypes))
meta_rejected[[n]]$tcr_out = ifelse(rownames(meta_rejected[[n]]) %in% rownames(infoTable_list_filtered[[n]])[good_cells], "No", "Yes")
infoTable_list_filtered[[n]] = infoTable_list_filtered[[n]][good_cells,]
filtered_datasets[[n]]$counts = filtered_datasets[[n]]$counts[,good_cells]
exp_genes = rownames(filtered_datasets[[n]]$counts)[apply(filtered_datasets[[n]]$counts, 1, function(x) length(x[x>1])>=3)]
exp_rejected[[n]] = exp_rejected[[n]][exp_genes,]
filtered_datasets[[n]]$counts = filtered_datasets[[n]]$counts[exp_genes,]
filtered_datasets[[n]]$tpm = filtered_datasets[[n]]$tpm[,good_cells]
filtered_datasets[[n]]$tpm = filtered_datasets[[n]]$tpm[exp_genes,]
}
for(dataset in names(infoTable_list_filtered)){
unique_cl = names(table(infoTable_list_filtered[[dataset]]$clonotypes)[table(infoTable_list_filtered[[dataset]]$clonotypes)==1])
infoTable_list_filtered[[dataset]]$clonotypes = ifelse(infoTable_list_filtered[[dataset]]$clonotypes %in% unique_cl,
"notAssigned",infoTable_list_filtered[[dataset]]$clonotypes)
}
lapply(meta_rejected, function(x) table(x[x$qc_out=="No",]$tcr_out)/sum(x$qc_out=="No"))
```
Save all
```{r}
save(infoTable_list_filtered, filtered_datasets,
bm.query_human, bm.query_human_o, bm.query_mouse, bm.query_mouse_o,
file = "./saved_data/SS2/SS2_postQC_data.RData")
# save all cells with removal labels
save(exp_rejected, meta_rejected, file = "./saved_data/SS2/SS2_QCinfo_data.RData")
```
Save Seurat objects for count matrices
```{r}
sr_list = list(
mouse_colon = Seurat::CreateSeuratObject(filtered_datasets$mouse_colon$counts),
mouse_skin = Seurat::CreateSeuratObject(filtered_datasets$mouse_skin$counts),
mouse_mel = Seurat::CreateSeuratObject(filtered_datasets$mouse_mel$counts),
human = Seurat::CreateSeuratObject(filtered_datasets$human$counts)
)
n = names(sr_list)
sr_list = lapply(names(sr_list),
function(x) AddMetaData(sr_list[[x]],
metadata = infoTable_list_filtered[[x]]))
names(sr_list) = n
sr_list_noqc = list(
mouse_colon = Seurat::CreateSeuratObject(exp_rejected$mouse_colon),
mouse_skin = Seurat::CreateSeuratObject(exp_rejected$mouse_skin),
mouse_mel = Seurat::CreateSeuratObject(exp_rejected$mouse_mel),
human = Seurat::CreateSeuratObject(exp_rejected$human)
)
n = names(sr_list_noqc)
sr_list_noqc = lapply(names(sr_list_noqc),
function(x) AddMetaData(sr_list_noqc[[x]],
metadata = meta_rejected[[x]]))
names(sr_list_noqc) = n
```
Prepare normalisations
```{r}
sr_list <- lapply(sr_list, NormalizeData, normalization.method = "LogNormalize",
scale.factor = 10000, display.progress = F)
sr_list <- lapply(sr_list, ScaleData, model.use = "negbinom",
do.center = T, do.scale = F, use.umi = T)
sr_list = lapply(names(sr_list), function(x) {
RunPCA(sr_list[[x]], pc.genes = rownames(sr_list[[x]]@data),
pcs.compute = 50, do.print = FALSE)})
names(sr_list) = n
sr_list_noqc <- lapply(sr_list_noqc, NormalizeData,
normalization.method = "LogNormalize",
scale.factor = 10000, display.progress = F)
sr_list_noqc <- lapply(sr_list_noqc, ScaleData, model.use = "negbinom",
do.center = T, do.scale = F, use.umi = T)
sr_list_noqc = lapply(names(sr_list_noqc), function(x) {
RunPCA(sr_list_noqc[[x]], pc.genes = rownames(sr_list_noqc[[x]]@data),
pcs.compute = 50, do.print = FALSE)})
names(sr_list_noqc) = n
```
Elbow plot and tSNE
```{r}
for(n in names(sr_list)){
plot_df = cbind(sr_list[[n]]@meta.data, sr_list[[n]]@dr$pca@cell.embeddings)
pdf(paste0("./plots/SS2/QC/PCA_plots", n, ".pdf"), useDingbats = F, height = 7.1, width = 7.1)
pca_plot = ggplot(plot_df, aes(x = PC1, y = PC2, colour = tissue_cell))+
geom_point(alpha = 0.77, pch = 19, size = 0.88)+
theme_classic()+
theme(aspect.ratio = 1)
print(pca_plot)
pca_plot = ggplot(plot_df, aes(x = PC2, y = PC3, colour = tissue_cell))+
geom_point(alpha = 0.77, pch = 19, size = 0.88)+
theme_classic()+
theme(aspect.ratio = 1)
print(pca_plot)
# Elbow Plot
elbow_plot = PCElbowPlot(object = sr_list[[n]], num.pc = 50)
print(elbow_plot)
dev.off()
}
sr_list = lapply(sr_list, RunTSNE, dims.use = 1:20, seed.use = 1)
sr_list_noqc = lapply(sr_list_noqc, RunTSNE, dims.use = 1:20, seed.use = 1)
```
Save
```{r}
save(sr_list, file = "saved_data/SS2/sr_list_SS2.RData")
save(sr_list_noqc, file = "saved_data/SS2/sr_list_noqc_SS2.RData")
```
# Clustering
Load data
```{r}
load("saved_data/SS2/sr_list_SS2.RData")
```
Subset data (dataset and cell type/tissue)
```{r}
sub_data_list = list()
for(ds in names(sr_list)[c(1,2,4)]){
for(n in unique(sr_list[[ds]]@meta.data$tissue_cell)){
nn = paste0(ds, "_", n)
print(nn)
cells = rownames(sr_list[[ds]]@meta.data)[sr_list[[ds]]@meta.data$tissue_cell==n]
sub_data_list[[nn]] = Seurat::SubsetData(sr_list[[ds]], cells.use = cells)
sub_data_list[[nn]] = Seurat::FindVariableGenes(sub_data_list[[nn]],
display.progress = F)
sub_data_list[[nn]] <- NormalizeData(object = sub_data_list[[nn]],
normalization.method = "LogNormalize",
scale.factor = 10000, display.progress = F)
sub_data_list[[nn]] <- Seurat::ScaleData(object = sub_data_list[[nn]],
model.use = "negbinom", do.scale = F,
do.center = T, use.umi = T)
sub_data_list[[nn]] = Seurat::RunPCA(sub_data_list[[nn]], pcs.compute = 30,
do.print = F)
}
}
```
PCA elbow plots and run tSNE
```{r}
pdf("./plots/SS2/clustering/elbow_plots_individual.pdf", useDingbats = F,
height = 7.1, width = 7.1)
for(n in names(sub_data_list)[-11]){
elbow_plot = PCElbowPlot(object = sub_data_list[[n]], num.pc = 30)+ggtitle(n)
print(elbow_plot)
sub_data_list[[n]] = Seurat::RunTSNE(sub_data_list[[n]], dims.use = 1:30,
seed.use = 1, perplexity = 10)
}
dev.off()
save(sub_data_list, file = "./saved_data/SS2/SS2_sub_data_list.RData")
```
Run clustering for each partition
```{r}
for(n in names(sub_data_list)){
print(n)
sub_data_list[[n]] <- Seurat::FindClusters(object = sub_data_list[[n]],
reduction.type = "pca",save.SNN = TRUE,
dims.use = 1:30, print.output = 0,
resolution = seq(0.1, 1.2, 0.1))
}
save(sub_data_list, file = "./saved_data/SS2/SS2_sub_data_list.RData")
```
Look into clusters
```{r}
tsne_list_list = list()
for(n in names(sub_data_list)){
tsne_list = list()
for(i in c(5:16)){
tsne_list[[colnames(sub_data_list[[n]]@meta.data)[i]]] = Seurat::TSNEPlot(sub_data_list[[n]], do.label = T,
group.by = colnames(sub_data_list[[n]]@meta.data)[i],
vector.friendly = T, label.size = 14, no.legend = T,
do.return = T, pt.size = 3)+
ggtitle(paste(n, colnames(sub_data_list[[n]]@meta.data)[i]))+
theme(aspect.ratio = 1)
}
tsne_list_list[[n]] = tsne_list
}
pdf("./plots/SS2/clustering/tSNE_plots.pdf", useDingbats = F, paper = "a4")
for(n in names(tsne_list_list)){
tsne_list= tsne_list_list[[n]]
all_plots = cowplot::plot_grid(plotlist = tsne_list[1:4], ncol = 2, align = "hv")
print(all_plots)
all_plots = cowplot::plot_grid(plotlist = tsne_list[5:8], ncol = 2, align = "hv")
print(all_plots)
all_plots = cowplot::plot_grid(plotlist = tsne_list[9:12], ncol = 2, align = "hv")
print(all_plots)
}
dev.off()
```
# DE
## Between sorted populations
Load data and add extra info
```{r}
load("saved_data/SS2/sr_list_SS2.RData")
for(n in names(sr_list)){
sr_list[[n]]@meta.data$is_nlt = c("LT", "NLT")[(grepl("skin", sr_list[[n]]@meta.data$tissue) | grepl("colon", sr_list[[n]]@meta.data$tissue))+1]
}
sr_list$human@meta.data$cell_type_full = sr_list$human@meta.data$cell.type
sr_list$human@meta.data$cell.type = ifelse(grepl("Treg",
sr_list$human@meta.data$cell_type_full),
"Treg", "Tmem")
save(sr_list, file = "saved_data/SS2/sr_list_SS2.RData")
```
DE NLT vs LT (USE 1% CELLS HERE)
```{r}
de_lt_nlt_ss2 = list()
for(i in names(sr_list)){
sr_list[[i]] = SetIdent(sr_list[[i]],
ident.use = as.character(sr_list[[i]]@meta.data$is_nlt))
de_lt_nlt_ss2[[i]] = FindMarkers(sr_list[[i]], ident.1 = "NLT", ident.2 = "LT",
logfc.threshold = 0.25, min.pct = 0.01, # 1% CELLS
print.bar = T)
gc()
}
de_lt_nlt_ss2[1:3] = lapply(de_lt_nlt_ss2[1:3], function(x) merge(gene_names, x,
by.x = 1,
by.y = 0))
save(de_lt_nlt_ss2, file = "plots/SS2/DE/de_lt_nlt_SS2.RData")
```
DE Treg vs Tmem (USE 1% CELLS HERE)
```{r}
de_treg_tmem_ss2 = list()
for(i in names(sr_list)){
sr_list[[i]] = SetIdent(sr_list[[i]], ident.use = sr_list[[i]]@meta.data$cell.type)
de_treg_tmem_ss2[[i]] = FindMarkers(sr_list[[i]], print.bar = T,
ident.1 = "Treg", ident.2 = "Tmem",
logfc.threshold = 0.25,
min.pct = 0.01) # 1% CELLS
gc()
}
de_treg_tmem_ss2[1:3] = lapply(de_treg_tmem_ss2[1:3], function(x) merge(gene_names, x, by.x = 1, by.y = 0))
save(de_treg_tmem_ss2, file = "plots/SS2/DE/de_treg_tmem_ss2.RData")
```
Second pass DE with all genes identified before
```{r}
load("plots/SS2/DE/de_lt_nlt_SS2.RData")
load("plots/SS2/DE/de_treg_tmem_ss2.RData")
de_lt_nlt_all_ss2 = list()
de_treg_tmem_all_ss2 = list()
for(n in names(sr_list)){
genes_use = union(as.character(de_lt_nlt_ss2[[n]]$V1),
as.character(de_treg_tmem_ss2[[n]]$V1))
sr_list[[n]] = SetIdent(sr_list[[n]], ident.use = sr_list[[n]]@meta.data$cell.type)
de_treg_tmem_all_ss2[[n]] = FindMarkers(sr_list[[n]], ident.1 = "Treg",
ident.2 = "Tmem", genes.use = genes_use,
logfc.threshold = 0, min.pct = 0,
print.bar = T)
sr_list[[n]] = SetIdent(sr_list[[n]], ident.use = sr_list[[n]]@meta.data$is_nlt)
de_lt_nlt_all_ss2[[n]] = FindMarkers(sr_list[[n]], ident.1 = "NLT", ident.2 = "LT",
genes.use = genes_use,
logfc.threshold = 0, min.pct = 0,
print.bar = T)
gc()
}
de_treg_tmem_all_ss2[1:3] = lapply(de_treg_tmem_all_ss2[1:3],
function(x) merge(gene_names, x,
by.x = 1, by.y = 0))
save(de_treg_tmem_all_ss2, file = "plots/SS2/DE/de_treg_tmem_all_SS2.RData")
de_lt_nlt_all_ss2[1:3] = lapply(de_lt_nlt_all_ss2[1:3],
function(x) merge(gene_names, x, by.x = 1, by.y = 0))
save(de_lt_nlt_all_ss2, file = "plots/SS2/DE/de_lt_nlt_all_SS2.RData")
```
DE colon vs skin (USE 1% CELLS HERE)
```{r}
# Merge data
sr_list$mouse_skin@meta.data$nr.of.cells = factor(sr_list$mouse_skin@meta.data$nr.of.cells)
colon_skin_data = MergeSeurat(sr_list$mouse_colon, sr_list$mouse_skin)
colon_skin_data = SubsetData(colon_skin_data,
cells.use = rownames(colon_skin_data@meta.data)[colon_skin_data@meta.data$tissue %in% c("colon","skin")])
# DE with All, Treg and Tmem
de_colon_skin_ss2 = list()
colon_skin_data = SetIdent(colon_skin_data,
ident.use = as.character(colon_skin_data@meta.data$tissue))
de_colon_skin_ss2[["all"]] = FindMarkers(colon_skin_data, ident.1 = "colon", ident.2 = "skin",
logfc.threshold = 0.25, min.pct = 0.01, # 1% CELLS
print.bar = T)
sub_data = SubsetData(colon_skin_data,
cells.use = rownames(colon_skin_data@meta.data)[colon_skin_data@meta.data$cell.type %in% c("Treg")])
de_colon_skin_ss2[["Treg"]] = FindMarkers(sub_data, ident.1 = "colon", ident.2 = "skin",
logfc.threshold = 0.25, min.pct = 0.01, # 1% CELLS
print.bar = T)
sub_data = SubsetData(colon_skin_data,
cells.use = rownames(colon_skin_data@meta.data)[colon_skin_data@meta.data$cell.type %in% c("Tmem")])
de_colon_skin_ss2[["Tmem"]] = FindMarkers(sub_data, ident.1 = "colon", ident.2 = "skin",
logfc.threshold = 0.25, min.pct = 0.01, # 1% CELLS
print.bar = T)
de_colon_skin_ss2 = lapply(de_colon_skin_ss2, function(x) merge(gene_names, x,
by.x = 1, by.y = 0))
save(de_colon_skin_ss2, file = "plots/SS2/DE/de_colon_skin_ss2.RData")
save(colon_skin_data, file = "saved_data/SS2/colon_skin_data.RData")
```
## Melanoma
Load data
```{r}
load("saved_data/SS2/sr_list_SS2.RData")
```
Run Cyclone to get cycling cells
```{r}
mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran"))
assigned <- scran::cyclone(sr_list$mouse_mel@data, pairs = mm.pairs)
sr_list$mouse_mel@meta.data$cellcycle = assigned$phases
save(sr_list, file = "saved_data/SS2/sr_list_SS2.RData")
```
DE without cycling cells
```{r}
de_ss_mel_ss2 = list()
for(tiss in c("spleen", "LN", "skin")){
sub_dat = Seurat::SubsetData(sr_list$mouse_mel,
cells.use = rownames(sr_list$mouse_mel@meta.data)[sr_list$mouse_mel@meta.data$cellcycle=="G1" &
sr_list$mouse_mel@meta.data$tissue==tiss &
sr_list$mouse_mel@meta.data$cell.type=="Treg"])
sub_dat = SetIdent(sub_dat, ident.use = as.character(sub_dat@meta.data$condition))
de_ss_mel_ss2[[tiss]] = FindMarkers(sub_dat, ident.1 = "Tumour",
ident.2 = "Control",
logfc.threshold = 0.25,
min.pct = 0.01, # 1% CELLS
print.bar = T)
}
de_ss_mel_ss2 = lapply(de_ss_mel_ss2, function(x) merge(gene_names, x,
by.x = 1, by.y = 0))
save(de_ss_mel_ss2, file = "plots/SS2/DE/de_ss_mel_ss2.RData")
```
# Pseudotime
Load data
```{r}
load("saved_data/SS2/sr_list_SS2.RData")
```
## Treg Steady-state
Subset data
```{r}
for(n in c("mouse_colon", "mouse_skin")){
cells = rownames(sr_list[[n]]@meta.data)[!grepl("Tmem",sr_list[[n]]@meta.data$tissue_cell) & !grepl("spleen", sr_list[[n]]@meta.data$tissue_cell)]
sub_data = Seurat::SubsetData(sr_list[[n]], cells.use = cells)
sub_data <- NormalizeData(object = sub_data,
normalization.method = "LogNormalize",
scale.factor = 10000, display.progress = F)
sub_data = Seurat::FindVariableGenes(sub_data, display.progress = F,
num.bin = 100,
binning.method = "equal_frequency")
exp_mat = t(as.matrix(sub_data@data[sub_data@var.genes,]))
save_mat = merge(sub_data@meta.data[cells,c("cell.type", "tissue")],
as.matrix(exp_mat), by = 0)
rownames(save_mat) = save_mat[,1]
save_mat = save_mat[,-1]
colnames(save_mat)[1] = "cell_type"
write.csv(save_mat, file = paste0("./saved_data/SS2/Treg_", n, "_LN.csv"),
row.names = T, quote = F, col.names = T)
}
```
## Treg Melanoma
Load genes
```{r}
load("plots/10X/DE/de_lt_nlt_all_10X.RData")
mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds",
package="scran"))
genes_tissues = union(de_lt_nlt_all$colon_mLN_spleen$V1[abs(de_lt_nlt_all$colon_mLN_spleen$avg_logFC)>=0.25 & de_lt_nlt_all$colon_mLN_spleen$p_val_adj<=0.05],
de_lt_nlt_all$skin_bLN_spleen$V1[abs(de_lt_nlt_all$skin_bLN_spleen$avg_logFC)>=0.25 & de_lt_nlt_all$skin_bLN_spleen$p_val_adj<=0.05])
genes_cc = rbind(mm.pairs$G1, mm.pairs$S, mm.pairs$G2M)
genes_cc = unique(c(genes_cc[,1], genes_cc[,2]))
genes_tissues = genes_tissues[!genes_tissues %in% genes_cc]
# for t0 comparisons
gene_groups = list("Skin" = as.character(de_lt_nlt_all$skin_bLN_spleen$V1[de_lt_nlt_all$skin_bLN_spleen$p_val_adj<=0.01 & de_lt_nlt_all$skin_bLN_spleen$avg_logFC>=0.25]),
"LT" = as.character(de_lt_nlt_all$skin_bLN_spleen$V1[de_lt_nlt_all$skin_bLN_spleen$p_val_adj<=0.01 & de_lt_nlt_all$skin_bLN_spleen$avg_logFC<=(-0.25)]),
"Cell Cycle" = genes_cc)
gene_groups$Skin = gene_groups$Skin[!gene_groups$Skin %in% gene_groups$`Cell Cycle`]
gene_groups$LT = gene_groups$LT[!gene_groups$LT %in% gene_groups$`Cell Cycle`]
gene_groups = reshape2::melt(gene_groups)
write.csv(gene_groups, file = "plots/SS2/MRD_melanoma/gene_groups.csv",
col.names = T, row.names = T, quote = F)
# save melanoma metadata for velocyto
write.csv(sr_list$mouse_mel@meta.data, file = "plots/SS2/MRD_melanoma/mouse_mel_metadata.csv",
col.names = T, row.names = T, quote = F)
```
Subset data
```{r}
# MRD
treg_bln_sk = data.frame(as.matrix(t(as.matrix(sr_list$mouse_mel@data[,sr_list$mouse_mel@meta.data$cell.type=="Treg" & sr_list$mouse_mel@meta.data$tissue!="spleen"]))))
treg_bln_sk = merge(sr_list$mouse_mel@meta.data[,c("t_c_cond", "cellcycle")],
treg_bln_sk, by = 0)
rownames(treg_bln_sk) = treg_bln_sk[,1]
treg_bln_sk = treg_bln_sk[,-1]
write.csv(treg_bln_sk[,colnames(treg_bln_sk) %in% c("t_c_cond", "cellcycle",
genes_tissues)],
file = "saved_data/SS2/Treg_mel_bLN_skin_tissue.csv",
col.names = T, row.names = T, quote = F)
write.csv(treg_bln_sk[,colnames(treg_bln_sk) %in% c("t_c_cond", "cellcycle",
genes_cc),],
file = "saved_data/SS2/Treg_mel_bLN_skin_cc.csv",
col.names = T, row.names = T, quote = F)
write.csv(treg_bln_sk[,!colnames(treg_bln_sk) %in% c(genes_tissues, genes_cc),],
file = "saved_data/SS2/Treg_mel_bLN_skin_other.csv",
col.names = T, row.names = T, quote = F)
treg_sub_list = list()
# Individual conditions for BGPLVM
for(cond in c("Control", "Tumour")){
treg_sub = data.frame(as.matrix(t(sr_list$mouse_mel@data[,sr_list$mouse_mel@meta.data$cell.type=="Treg" & sr_list$mouse_mel@meta.data$tissue!="spleen" & sr_list$mouse_mel@meta.data$condition==cond])))
treg_sub = merge(sr_list$mouse_mel@meta.data[,c("t_c_cond", "cellcycle")],
treg_sub, by = 0)
rownames(treg_sub) = treg_sub[,1]
treg_sub = treg_sub[,-1]
write.csv(treg_sub,
file = paste0("saved_data/SS2/Treg_mel_bLN_skin_", cond, ".csv"),
col.names = T, row.names = T, quote = F)
treg_sub_list[[cond]] = treg_sub
}
```
Load MRD results (see MRD_melanoma notebook), format data
```{r}
mrd_lvs = read.csv("plots/SS2/MRD_melanoma/latent_variables_MRD_LNskinTreg.csv",
header = T, row.names = 1)
mrd_ard = read.csv("plots/SS2/MRD_melanoma/ARD_MRD_LNskinTreg.csv",
header = T, row.names = 1)
bgplvm_lv_ind = list("Control" = read.csv("plots/SS2/MRD_melanoma/latent_variables_Treg_mel_bLN_skin_Control.csv", header = T, row.names = 1),
"Tumour" = read.csv("plots/SS2/MRD_melanoma/latent_variables_Treg_mel_bLN_skin_Tumour.csv", header = T, row.names = 1))
bgplvm_ard_ind = list("Control" = read.csv("plots/SS2/MRD_melanoma/ARD_Treg_mel_bLN_skin_Control.csv", header = T, row.names = 1),
"Tumour" = read.csv("plots/SS2/MRD_melanoma/ARD_Treg_mel_bLN_skin_Tumour.csv", header = T, row.names = 1))
# get each condition with same expressed genes
genes = intersect(colnames(treg_sub_list$Control[,-c(1,2)])[colSums(treg_sub_list$Control[,-c(1,2)]>0)>7], colnames(treg_sub_list$Tumour[,-c(1,2)])[colSums(treg_sub_list$Tumour[,-c(1,2)]>0)>7])
treg_sw_list = lapply(treg_sub_list, function(x) t(x[,genes]))
```
Run switchDE for each condition
```{r}
switch_de_cond = list()
for(cond in c("Control", "Tumour")){
for(lv in c("LV5", "LV9", "LV4", "LV10")){
cells = colnames(treg_sw_list[[cond]])
pt = if(lv %in% c("LV9", "LV4", "LV5")) mrd_lvs[cells, lv]*-1 else mrd_lvs[cells, lv]
switch_de_cond[[paste0(cond, "_", lv)]] = switchde(treg_sw_list[[cond]][,cells], pt)
}
}
switch_de_cond = lapply(switch_de_cond, function(x) merge(gene_names, x, by = 1))
save(switch_de_cond, file = "plots/SS2/MRD_melanoma/switch_de_cond.RData")
```
Look at genes from both conditions
```{r}
load("plots/SS2/MRD_melanoma/switch_de_cond.RData")
switch_lv9 = merge(switch_de_cond$Control_LV9[,c(1,2,4:7)],
switch_de_cond$Tumour_LV9[,c(1,4:7)], by = 1)
switch_lv9$inInterval = ifelse(switch_lv9$t0.x<=max(mrd_lvs$LV9) &
switch_lv9$t0.x>=min(mrd_lvs$LV9),
ifelse(switch_lv9$t0.y<=max(mrd_lvs$LV9) &
switch_lv9$t0.y>=min(mrd_lvs$LV9),
"Both", "Control"),
ifelse(switch_lv9$t0.y<=max(mrd_lvs$LV9) &
switch_lv9$t0.y>=min(mrd_lvs$LV9),
"Tumour", "Neither"))
switch_lv9 = merge(gene_groups, switch_lv9, by = 1, all.y = T)
switch_lv9$L1[is.na(switch_lv9$L1)] = "Other"
switch_lv9$isSig = switch_lv9$qval.x<=0.05 & switch_lv9$qval.y<=0.05 &
((switch_lv9$k.x<0 & switch_lv9$k.y<0) |
(switch_lv9$k.x>0 & switch_lv9$k.y>0))
colnames(switch_lv9) = c("ens_g", "Group", "gene",
"qval_Control", "mu0_Control", "k_Control", "t0_Control",
"qval_Tumour", "mu0_Tumour", "k_Tumour", "t0_Tumour",
"inInterval", "isSig")
switch_lv9$t0_diff = switch_lv9$t0_Control-switch_lv9$t0_Tumour
save(switch_lv9, file = "plots/SS2/MRD_melanoma/switch_lv9_results.RData")
plot_df = switch_lv9[switch_lv9$inInterval=="Both" & switch_lv9$isSig,]
cor(switch_lv9$t0_Control[switch_lv9$isSig],
switch_lv9$t0_Tumour[switch_lv9$isSig], method = "sp")
cor(plot_df$t0_Control, plot_df$t0_Tumour, method = "sp")
cor(plot_df[plot_df$Group=="Skin", "t0_Control"], plot_df[plot_df$Group=="Skin", "t0_Tumour"], method = "sp")
cor(plot_df[plot_df$Group=="Other", "t0_Control"], plot_df[plot_df$Group=="Other", "t0_Tumour"], method = "sp")
cor(plot_df[plot_df$Group=="LT", "t0_Control"], plot_df[plot_df$Group=="LT", "t0_Tumour"], method = "sp")
cor(plot_df[plot_df$Group=="Cell Cycle", "t0_Control"], plot_df[plot_df$Group=="Cell Cycle", "t0_Tumour"], method = "sp")
ggplot(plot_df, aes(x = Group, y = t0_diff))+
geom_point(position = position_jitter(width = 0.1))+
stat_summary(colour = "red")+
geom_hline(yintercept = c(0.3157757, 0.5013434), linetype = "dashed")+
coord_flip()+
theme_classic()+
theme(aspect.ratio = 1)
plot(xxx$t0_Control, xxx$t0_Tumour, col = as.numeric(as.factor(xxx$Group)))
```
# Human
Load data
```{r}
load("saved_data/SS2/sr_list_SS2.RData")
gene_names_human = read.csv("saved_data/gene_names_human_GRCh38_93.csv",
header = T)
```
Split human into colon + blood and skin + blood
```{r}
human_list = list("colon" = SubsetData(sr_list$human,
cells.use = rownames(sr_list$human@meta.data)[sr_list$human@meta.data$tissue!="skin"]),
"skin" = SubsetData(sr_list$human,
cells.use = rownames(sr_list$human@meta.data)[sr_list$human@meta.data$tissue!="colon"]))
```
Regress out individual for skin data
```{r}
human_list$skin <- NormalizeData(human_list$skin,
normalization.method = "LogNormalize",
scale.factor = 10000, display.progress = F)
human_list$skin <- ScaleData(human_list$skin, model.use = "negbinom",
do.center = T, do.scale = F, use.umi = T,
vars.to.regress = "individual")
human_list$skin = RunPCA(human_list$skin,
pc.genes = rownames(human_list$skin@data),
pcs.compute = 50, do.print = FALSE)
human_list$skin = RunTSNE(human_list$skin, dims.use = 1:12, seed.use = 1)
save(human_list, file = "saved_data/SS2/human_list.RData")
```
## DE
DE Blood vs NLT (all cell types)
```{r}
human_de_all = list()
for(i in names(human_list)){
human_list[[i]] = SetIdent(human_list[[i]],
ident.use = as.character(human_list[[i]]@meta.data$tissue))
human_de_all[[i]] = FindMarkers(human_list[[i]], ident.1 = i, ident.2 = "blood",
logfc.threshold = 0.25, min.pct = 0.01,
print.bar = T)
gc()
}
human_de_all = lapply(human_de_all,
function(x) merge(gene_names_human,
x, by.x = 1, by.y = 0))
save(human_de_all, file = "plots/SS2/DE/human_de_all.RData")
```
DE Blood vs NLT (by cell types)
```{r}
human_de_ct = list()
for(i in names(human_list)){
for(ct in c("Treg", "Tmem")){
cells = if(ct=="Treg"){
human_list[[i]]@meta.data$cell.type==ct
} else{ human_list[[i]]@meta.data$cell.type!=ct}
sub_data = SubsetData(human_list[[i]], rownames(human_list[[i]]@meta.data)[cells])
sub_data = SetIdent(sub_data,
ident.use = as.character(sub_data@meta.data$tissue))
human_de_ct[[paste0(i, "_", ct)]] = FindMarkers(sub_data, ident.1 = i,
ident.2 = "blood",
logfc.threshold = 0.25,
min.pct = 0.01, print.bar = T)
gc()
}
}
human_de_ct = lapply(human_de_ct, function(x) merge(gene_names_human,
x, by.x = 1, by.y = 0))
save(human_de_ct, file = "plots/SS2/DE/human_de_ct.RData")
```
Markers each cell type by tissue/all tissues
```{r}
human_de_ct_full = list()
for(i in names(human_list)){
for(tiss in unique(human_list[[i]]@meta.data$tissue)){
cells = human_list[[i]]@meta.data$tissue==tiss
sub_data = SubsetData(human_list[[i]], rownames(human_list[[i]]@meta.data)[cells])
sub_data = SetIdent(sub_data,
ident.use = as.character(sub_data@meta.data$cell.type))
human_de_ct_full[[paste0(i, "_", tiss)]] = FindAllMarkers(sub_data,
logfc.threshold = 0.25,
return.thresh = 0.05,
min.pct = 0.01,
print.bar = T)
}
}
sr_list$human = SetIdent(sr_list$human,
ident.use = as.character(sr_list$human@meta.data$cell.type))
human_de_ct_full[["all"]] = FindAllMarkers(sr_list$human, logfc.threshold = 0.25,
return.thresh = 0.05,
min.pct = 0.01, print.bar = T)
human_de_ct_full = lapply(human_de_ct_full, function(x) merge(gene_names_human,
x, by.x = 1, by.y = 0))
save(human_de_ct_full, file = "plots/SS2/DE/human_de_ct_full.RData")
```
Tissue markers by cell type
```{r}
human_de_tiss_full = list()
for(i in names(human_list)){
for(ct in unique(human_list[[i]]@meta.data$cell.type)){
cells = human_list[[i]]@meta.data$cell.type==ct
sub_data = SubsetData(human_list[[i]], rownames(human_list[[i]]@meta.data)[cells])
sub_data = SetIdent(sub_data,
ident.use = as.character(sub_data@meta.data$tissue))
human_de_tiss_full[[paste0(i, "_", ct)]] = FindAllMarkers(sub_data,
logfc.threshold = 0.25,
return.thresh = 0.05,
min.pct = 0.01,
print.bar = T)
cells = sr_list$human@meta.data$cell.type==ct
sub_data = SubsetData(sr_list$human, rownames(sr_list$human@meta.data)[cells])
sub_data = SetIdent(sub_data, ident.use = as.character(sub_data@meta.data$tissue))
human_de_tiss_full[[paste0("all", ct)]] = FindAllMarkers(sub_data,
logfc.threshold = 0.25,
return.thresh = 0.05,
min.pct = 0.01,
print.bar = T)
}
}
sr_list$human = SetIdent(sr_list$human,
ident.use = as.character(sr_list$human@meta.data$tissue))
human_de_tiss_full[["all"]] = FindAllMarkers(sr_list$human, logfc.threshold = 0.25,
return.thresh = 0.05, min.pct = 0.01,
print.bar = T)
human_de_tiss_full = lapply(human_de_tiss_full, function(x) merge(gene_names_human,
x, by.x = 1, by.y = 0))
save(human_de_tiss_full, file = "plots/SS2/DE/human_de_tiss_full.RData")
```
Pairwise DE cell type by tissue/all tissues
```{r}
human_de_ct_pair = list()
for(i in names(human_list)){
for(tiss in unique(human_list[[i]]@meta.data$tissue)){
for(ct in unique(human_list[[i]]@meta.data$cell.type)){
cells = human_list[[i]]@meta.data$cell.type!=ct &
human_list[[i]]@meta.data$tissue==tiss
ct_de = unique(human_list[[i]]@meta.data$cell.type)[unique(human_list[[i]]@meta.data$cell.type)!=ct]
id = paste(ct_de, collapse = ".vs.")
sub_data = SubsetData(human_list[[i]], rownames(human_list[[i]]@meta.data)[cells])
sub_data = SetIdent(sub_data,
ident.use = as.character(sub_data@meta.data$cell.type))
human_de_ct_pair[[paste0(i, "_", id)]] = FindMarkers(sub_data, ident.1 = ct_de[1], ident.2 = ct_de[2],
logfc.threshold = 0.25, min.pct = 0.01, print.bar = T)
}
}
}
for(ct in unique(sr_list$human@meta.data$cell.type)){
cells = sr_list$human@meta.data$cell.type!=ct
ct_de = unique(sr_list$human@meta.data$cell.type)[unique(sr_list$human@meta.data$cell.type)!=ct]
id = paste(ct_de, collapse = ".vs.")
sub_data = SubsetData(sr_list$human, rownames(sr_list$human@meta.data)[cells])
sr_list$human = SetIdent(sr_list$human,
ident.use = as.character(sr_list$human@meta.data$cell.type))
human_de_ct_pair[[paste0("all_", id)]] = FindMarkers(sr_list$human, ident.1 = ct_de[1], ident.2 = ct_de[2],
logfc.threshold = 0.25, min.pct = 0.01, print.bar = T)
}
human_de_ct_pair = lapply(human_de_ct_pair, function(x) merge(gene_names_human,
x, by.x = 1, by.y = 0))
save(human_de_ct_pair, file = "plots/SS2/DE/human_de_ct_pair.RData")
```
## Mouse comparisons
Load evolutionary data
```{r}
load("./saved_data/bm.query_human_allevo.RData")
load("./saved_data/bm.query_mouse_allevo.RData")
load("./saved_data/paralog_list_2.RData")
```
Run mouse SS2 DE with spleen only
```{r}
mouse_de_ct = list()
for(i in names(sr_list)[1:2]){