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S05_TumorAnalysis.R
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S05_TumorAnalysis.R
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# Evaluate tumor heterogeneity #
## author: Antonietta Salerno
## date: 07/02/2023
library("Seurat")
library("MAST")
library("writexl")
library(openxlsx)
library("EnhancedVolcano")
library("fgsea")
library(dplyr)
library(devtools)
library(msigdbr)
library(readr)
library(stringr)
library("org.Mm.eg.db", character.only = TRUE)
library(clusterProfiler)
library(ggplot2)
library(ggcharts)
setwd("~/Library/CloudStorage/OneDrive-UNSW/TEPA_project")
source("TEPA_code/supportFunctions.R")
tumor <- LoadSeuratRds("TEPA_results/S00_tumor.Rds") #13560 cells and 21388 genes
#### 1 - QC and filtering of tumor cells ####
png("TEPA_plots/S05_umapTumorNotInt.png", w = 2000, h = 2000, res = 300)
#pdf(qq("TEPA_final_figures/S05_umapTumorNotInt.pdf"), w = 6, h = 5)
DimPlot(object = tumor, pt.size = 0.000000005, group.by = "condition",
reduction = 'umap', ncol = 1,
label = FALSE, cols = cond_col) +
ggtitle("Tumor cells in TEPA vs Control") +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
tumor <- NormalizeData(tumor)
# Add percent mito data in seurat seuset_tumorect
tumor[["percent.mt"]] <- PercentageFeatureSet(tumor, pattern = "^mt.")
# Add percent ribo genes
tumor[["percent.ribo"]] <- PercentageFeatureSet(tumor, pattern = "^Rp.")
# Visualize the distribution of mitochondrial gene expression detected per cell
png("TEPA_plots/S05_tumorPreFilterQC_mito.png", h = 3000, w = 4200, res = 300)
ggplot(aes(color=orig.ident, x=percent.mt, fill=orig.ident)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
geom_vline(xintercept = 25)
dev.off()
png("TEPA_plots/S05_tumorPreFilterQC_ribo.png", h = 3000, w = 4200, res = 300)
ggplot(aes(color=orig.ident, x=percent.ribo, fill=orig.ident)) +
geom_density(alpha = 0.2) +
scale_x_log10() +
theme_classic() +
geom_vline(xintercept = 15)
dev.off()
tumor <- subset(tumor, subset = nFeature_RNA > 200 &
percent.mt < 25 & percent.ribo < 15) # 13535 cells
png("TEPA_plots/S05_tumorPostFilterQC_violin.png", h = 3000, w = 4200, res = 300)
VlnPlot(tumor, features = c("nFeature_RNA", "nCount_RNA", "Mycn"), ncol = 3, pt.size = 0.000005)
# a little difference in the distribution -> batch effect
dev.off()
png("TEPA_plots/S05_tumorPostFilterQC_scatter.png", h = 3000, w = 4200, res = 300)
plot1 <- FeatureScatter(tumor, feature1 = "nCount_RNA", feature2 = "percent.mt", pt.size = 0.0005)
plot2 <- FeatureScatter(tumor, feature1 = "nCount_RNA", feature2 = "nFeature_RNA", pt.size = 0.0005)
plot1 + plot2 # We can spot two separate data clouds because of the bimodal distribution of the data: control vs treated cells
dev.off()
### Batch effect correction ###
samples.list <- SplitObject(tumor, split.by = "condition")
# Normalize and identify variable features for each dataset independently (Treatment vs Control)
samples.list <- lapply(X = samples.list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
features <- SelectIntegrationFeatures(seuset_tumorect.list = samples.list)
tumor.anchors <- FindIntegrationAnchors(seuset_tumorect.list = samples.list,anchor.features = features)
tumor.combined <- IntegrateData(anchorset = tumor.anchors)
#### 2 - Dimensionality reduction ####
# Scaling
seuset_tumor <- ScaleData(tumor.combined, verbose = FALSE)
seuset_tumor <- RunPCA(seuset_tumor, npcs = 100, verbose = FALSE, assay = "integrated")
# Determine percent of variation associated with each PC
pct <- seuset_tumor@reductions$pca@stdev / sum(seuset_tumor@reductions$pca@stdev) * 100
# Calculate cumulative percents for each PC
cum <- cumsum(pct)
head(cum, n=80) # Select 80 PCs to retain 86.09 % of variability
seuset_tumor <- FindNeighbors(seuset_tumorect = seuset_tumor, graph.name = "clust", dims = 1:80, reduction = 'pca')
seuset_tumor <- FindClusters(seuset_tumorect = seuset_tumor, graph.name = "clust", resolution = 0.3, algorithm = 1) # original plot is 0.7
seuset_tumor <- RunUMAP(seuset_tumor, dims = 1:80, reduction = "pca", verbose = FALSE)
png("TEPA_plots/S05_tumorUmapClust.png", w = 4000, h = 4000, res = 350)
DimPlot(seuset_tumor, pt.size = 0.5, reduction = 'umap', ncol = 1,
group.by = c("seurat_clusters", "condition"), label = FALSE) +
ggtitle(paste(as.character(nrow([email protected])), " cells")) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
#png("TEPA_plots/S05_tumorUmapClusters.png", w = 2000, h = 2000, res = 200)
pdf(qq("TEPA_final_figures/S05_tumorUmapClusters.pdf"), w = 9, h = 7)
DimPlot(seuset_tumor, pt.size = 0.5, reduction = 'umap', ncol = 1, cols = tum_col,
group.by = c("seurat_clusters"), label = TRUE) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
png("TEPA_plots/S05_tumorUmapClusters.png", w = 2000, h = 2000, res = 200)
#pdf(qq("TEPA_final_figures/S05_tumorUmapCond.pdf"), w = 10, h = 7)
DimPlot(seuset_tumor, pt.size = 0.5, reduction = 'umap', ncol = 1, cols = cond_col,
group.by = c("condition"), label = FALSE) +
theme(plot.title = element_text(hjust = 0.5))
dev.off()
SaveSeuratRds(seuset_tumor, "TEPA_results/S05_seusetTumorClu.Rds")
#### 3 - Clustering annotation ####
### 3.1 Inter-cluster DEA: get marker genes ###
DefaultAssay(seuset_tumor) <- "RNA"
save = "S05_tumorMarkers"
Idents(seuset_tumor) <- "seurat_clusters"
## A - Find markers for every cluster compared to all remaining cells
tumor.markers <- FindAllMarkers(seuset_tumor,
only.pos = FALSE,
min.pct = 0.5,
min.diff.pct = 0.2,
logfc.threshold = 0.3,
test.use="MAST",
latent.vars="orig.ident")
write.csv(tumor.markers, file=paste0("TEPA_results/", save,".csv"))
# Save results in different excel sheets
clusters = levels(Idents(seuset_tumor))
wb <- createWorkbook()
for(c in 1:length(clusters)){
cluster = tumor.markers[tumor.markers$cluster == clusters[c],]
addWorksheet(wb, as.character(clusters[c]))
writeData(wb, as.character(clusters[c]), cluster[,2:ncol(cluster)], colNames = TRUE)
}
saveWorkbook(wb, file=paste0("TEPA_results/",save,".xlsx"), overwrite = TRUE)
# B - Add module score to the Seurat seuset_tumorect for each cluster ###
tumor.markers <- read.csv("TEPA_results/S05_tumorMarkers.csv")
seuset_tumor <- createSets(markers = tumor.markers,
obj = seuset_tumor, id = "seurat_clusters")
#png("TEPA_plots/S05_tumorClustersAnn.png", h = 3000, w = 4500, res = 300)
pdf(qq("TEPA_final_figures/S05_tumorClustersAnn.pdf"), h = 7, w = 9)
clusters = unique(Idents(seuset_tumor))
patchwork::wrap_plots(FeaturePlot(seuset_tumor, ncol = 2, combine = TRUE, pt.size = 1,
features = as.character(clusters), label = TRUE, repel = TRUE)) & theme_minimal() &
scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdBu")))
dev.off()
## C - Identify most important markers per cluster ###
markers <- getTopMarkers(tumor.markers, 5)
#png("TEPA_plots/S05_tumorDotPlot.png", h = 2000, w = 2500, res = 300)
pdf(qq("TEPA_final_figures/S05_tumorDotPlot.pdf"), h = 5, w = 6)
DotPlot(seuset_tumor, features = unique(markers), # split.by = "condition",
scale=TRUE, col.min = -4, col.max = 4,
dot.min = 0, dot.scale = 5, cols = c("blue","red")) + RotatedAxis() + coord_flip() +
theme(axis.text.x = element_text(size=7), axis.text.y = element_text(size=7))
dev.off()
## D - Plot Volcano of each cluster vs all the others:
save = "S05_tumorMarkers"
plotVolcano(clusters, res = tumor.markers, type = "markers", input = "tumor", log2FC = 0.5, save = save)
#### 3.2 - Intra-cluster DEA with annotated dataset - Treatment vs Control ####
Idents(seuset_tumor) <- "seurat_clusters"
seuset_tumor$clusters.tepa <- paste(Idents(seuset_tumor), seuset_tumor$condition, sep = "_")
Idents(seuset_tumor) <- "clusters.tepa"
DefaultAssay(seuset_tumor) <- "RNA"
save = "S05_tumorCond_"
sheets <- list()
for (cluster in unique(seuset_tumor$seurat_clusters)){
try({
ident1 <- paste0(cluster,"_Treatment")
ident2 <- paste0(cluster,"_Control")
message(paste0("DEA control vs treatment for cluster: ", cluster))
condition.diffgenes <- FindMarkers(seuset_tumor,
ident.1 = ident1, ident.2 = ident2,
#logfc.threshold = 0.25,
only.pos = FALSE, verbose = FALSE,
#latent.vars="orig.ident",
#min.cells.feature = 1, min.cells.group = 1,
test.use="MAST")
condition.diffgenes$p_val_adj = p.adjust(condition.diffgenes$p_val, method='BH')
sheets[[cluster]] <- as.data.frame(condition.diffgenes)
# Needed for plotting
write.csv(condition.diffgenes, file=paste0("TEPA_results/", save,"DEA_",sub(" ", "_", cluster),".csv"))
})
}
# Needed for manual curation
openxlsx::write.xlsx(sheets, paste0("TEPA_results/", save,"DEA.xlsx"), rowNames=TRUE)
# Plot Volcano DEA by condition
Idents(seuset_tumor) <- "seurat_clusters"
clusters = unique(Idents(seuset_tumor))
save = "S05_tumor_"
plotVolcano(clusters, log2FC = 0.5, save = save, input = "tumor")
#### 3.3 DEA Treatment vs Control bulk dataset ####
counts <- GetAssayData(seuset_tumor, assay = "RNA")
counts <- counts[-(which(rownames(counts) == "Xist")),]
seuset_tumor <- subset(seuset_tumor, features = rownames(counts))
Idents(seuset_tumor) <- "condition"
res <- FindMarkers(seuset_tumor,
ident.1 = "Treatment", ident.2 = "Control",
only.pos = FALSE, verbose = FALSE,
latent.vars="orig.ident",
test.use="DESeq2")
#res$p_val_adj = p.adjust(res$p_val, method='BH')
write.csv(res, file=paste0("TEPA_results/S05_tumorBulkDEA_DESeq2.csv"))
log2FC = 0.5
save = "S05_tumorBulk_"
res <- res %>%
filter(p_val_adj < 0.05);
res[order(-res$avg_log2FC),]
markers_DEA <- rownames(rbind(head(res,20), tail(res,20)))
#png("TEPA_plots/S05_tumorBulk_DEA_FeaturePlot.png", h = 2000, w = 2500, res = 300)
pdf(qq("TEPA_final_figures/S05_tumorBulk_DEA_FeaturePlot.pdf"), h = 2, w = 10)
DotPlot(seuset_tumor, features = unique(markers_DEA), # split.by = "condition",
scale=TRUE, col.min = -4, col.max = 4,
dot.min = 0, dot.scale = 5, cols = c("blue","red")) + RotatedAxis() +
theme(axis.text.x = element_text(size=7), axis.text.y = element_text(size=7))
dev.off()
# Stmn3 = Exhibits microtubule-destabilizing activity, which is antagonized by STAT3
# Zfp580 = cellular response to hydrogen peroxide; positive regulation of cell migration
p <- EnhancedVolcano(res, subtitle = "",
lab = rownames(res),
x = 'avg_log2FC',
y = 'p_val_adj',
xlim = c(-3.5, 2),
title = "DEA Tumor TEPA vs Control",
pCutoff = 0.05,
FCcutoff = log2FC,
labFace = "bold",
labSize = 3,
col = c('lightgrey', 'pink', "#ADD8E6", 'salmon'),
colAlpha = 4/5,
legendLabSize = 14,
legendIconSize = 4.0,
drawConnectors = TRUE,
cutoffLineType = 'blank',
cutoffLineCol = 'black',
cutoffLineWidth = 0.8,
hline = c(10e-10, 10e-50, 10e-100, 10e-300),
hlineCol = c('grey0', 'grey25','grey50','grey75'),
hlineType = 'longdash',
hlineWidth = 0.8,
gridlines.major = FALSE,
gridlines.minor = FALSE,
widthConnectors = 0.3,
colConnectors = 'gray51',
maxoverlapsConnectors = 170,
caption = paste0('Upregulated = ', nrow(res[res$avg_log2FC>log2FC&res$p_val_adj<=0.05,]), ' genes',
"\n",'Downregulated = ', nrow(res[res$avg_log2FC< -log2FC&res$p_val_adj<=0.05,]), ' genes')) +
coord_flip(expand = TRUE, clip = "on") +
theme(plot.title = element_text(hjust = 0.5)) + coord_cartesian(ylim=c(0,400))
ggsave(p, file=paste0("TEPA_final_figures/", save, "DEA.pdf"), width = 28, height = 17, units = "cm")
#### 4 - Gene Set Enrichment Analysis ####
sets1 <- read.gmt("TEPA_data/mh.all.v2022.1.Mm.symbols.gmt") # Mouse hallmark
sets2 <- read.gmt("TEPA_data/GOBP_CELL_MOTILITY.v2023.1.Mm.gmt")
sets3 <- read.gmt("TEPA_data/REACTOME_NEUTROPHIL_DEGRANULATION.v2022.1.Mm.gmt") # The only Reactome pathway we're interested in
sets4 <- read.gmt("TEPA_data/REACTOME_ENOS_ACTIVATION.v2023.1.Mm.gmt") # REACTOME_ENOS_ACTIVATION
sets5 <- read.gmt("TEPA_data/REACTOME_PENTOSE_PHOSPHATE_PATHWAY.v2023.1.Mm.gmt")
sets6 <- read.gmt("TEPA_data/WP_OXIDATIVE_STRESS_AND_REDOX_PATHWAY.v2023.1.Mm.gmt")
sets7 <- read.gmt("TEPA_data/WP_OXIDATIVE_STRESS_RESPONSE.v2023.1.Mm.gmt")
sets8 <- read.gmt("TEPA_data/WP_OXIDATIVE_DAMAGE_RESPONSE.v2023.1.Mm.gmt")
sets9 <- read.gmt("TEPA_data/REACTOME_CITRIC_ACID_CYCLE_TCA_CYCLE.v2023.1.Mm.gmt")
sets10 <- read.gmt("TEPA_data/REACTOME_DNA_DOUBLE_STRAND_BREAK_RESPONSE.v2023.1.Mm.gmt")
sets1$term <- as.character(sets1$term)
sets2$term <- as.character(sets2$term)
sets3$term <- as.character(sets3$term)
sets4$term <- as.character(sets4$term)
sets5$term <- as.character(sets5$term)
sets6$term <- as.character(sets6$term)
sets7$term <- as.character(sets7$term)
sets8$term <- as.character(sets8$term)
sets9$term <- as.character(sets9$term)
sets10$term <- as.character(sets10$term)
sets1 <- sets1 %>% split(x = .$gene, f = .$term)
sets2 <- sets2 %>% split(x = .$gene, f = .$term)
sets3 <- sets3 %>% split(x = .$gene, f = .$term)
sets4 <- sets4 %>% split(x = .$gene, f = .$term)
sets5 <- sets5 %>% split(x = .$gene, f = .$term)
sets6 <- sets6 %>% split(x = .$gene, f = .$term)
sets7 <- sets7 %>% split(x = .$gene, f = .$term)
sets8 <- sets8 %>% split(x = .$gene, f = .$term)
sets9 <- sets9 %>% split(x = .$gene, f = .$term)
sets10 <- sets10 %>% split(x = .$gene, f = .$term)
#sets3 <- sets3 %>% split(x = .$gene_symbol, f = .$gs_name)
fgsea_sets <- list()
fgsea_sets <- append(sets1, sets2)
fgsea_sets <- append(fgsea_sets, sets3)
fgsea_sets <- append(fgsea_sets, sets4)
fgsea_sets <- append(fgsea_sets, sets5)
fgsea_sets <- append(fgsea_sets, sets6)
fgsea_sets <- append(fgsea_sets, sets7)
fgsea_sets <- append(fgsea_sets, sets8)
fgsea_sets <- append(fgsea_sets, sets9)
fgsea_sets <- append(fgsea_sets, sets10)
fgsea_sets <- append(fgsea_sets, custom)
fgsea_sets <- append(fgsea_sets, copper_sign)
Idents(seuset_tumor) <- "condition"
clusters = unique(Idents(seuset_tumor))
save = "S05_tumorEnrichment_Cond"
gseaRES(clusters, fgsea_sets = fgsea_sets, save = save, input = "tumor")
#### 5 - Look at expression of specific genes ####
# Search all isoforms of gene of interest
grep(pattern = "Ifi2",
x = rownames(x = seuset_tumor@assays$RNA@data),
value = TRUE, ignore.case = TRUE)
#png("TEPA_plots/S05_tumorMt1.png", h = 2000, w = 2000, res = 200)
pdf("TEPA_final_figures/S05_tumorMt1.pdf", h = 4, w = 4)
Idents(seuset_tumor) <- "condition"
VlnPlot(seuset_tumor, features = c("Mt1"),
cols = cond_col, ncol = 1, pt.size = 0) +
ylim(-1,5) +
geom_signif(xmin = 1, xmax = 2, y_position = 4, annotations="***")+
geom_boxplot(width=.1, outlier.size = 0.5)
dev.off()
#png("TEPA_plots/S05_tumorMt2.png", h = 2000, w = 2000, res = 200)
pdf("TEPA_final_figures/S05_tumorMt2.pdf", h = 4, w = 4)
Idents(seuset_tumor) <- "condition"
VlnPlot(seuset_tumor, features = c("Mt2"),
cols = cond_col, ncol = 1, pt.size = 0) +
ylim(-1,5) +
geom_signif(xmin = 1, xmax = 2, y_position = 4, annotations="***")+
geom_boxplot(width=.1, outlier.size = 0.5)
dev.off()
#png("TEPA_plots/S05_tumorMycn.png", h = 2000, w = 2000, res = 200)
pdf(qq("TEPA_final_figures/S05_tumorMycn.pdf"), h = 4, w = 4)
Idents(seuset_tumor) <- "condition"
VlnPlot(seuset_tumor, features = "Mycn", cols = cond_col,
ncol = 1, pt.size = 0) +
ylim(0,6) +
#geom_signif(xmin = 1, xmax = 2, y_position = 5.25, annotations="***") +
geom_boxplot(width=.1, outlier.size = 0.5)
dev.off()
png("TEPA_plots/S05_tumorH1fx.png", h = 2000, w = 2000, res = 200)
Idents(seuset_tumor) <- "condition"
VlnPlot(seuset_tumor, features = "H1fx", cols = cond_col,
ncol = 1, pt.size = 0.000005) +
ylim(0,6) +
geom_signif(xmin = 1, xmax = 2, y_position = 5.25, annotations="***")
dev.off()
#### 6 - Investigate whether the tumor is adrenergic or mesenchymal ####
seuset_tumor <- LoadSeuratRds("TEPA_results/S05_seusetTumorClu.Rds")
MES <- c("Elk4", "Creg1", "Dcaf6", "Id1", "Smad3", "Six4", "Six1", "Maml2", "Notch2",
"Cbfb", "Ifi203","Ifi204", "Ifi205", "Ifi206", "Ifi207", "Ifi208", "Ifi209",
"Ifi211","Ifi214", "Zfp217", "Egr3", "Zfp36l1", "Wwtr1", "Prrx1", "Sox9",
"Meox1", "Meox2", "Aebp1", "Col1a1", "Col1a2", "Fn1", "Vim", "Snai2", "Prom1",
"Prrx1", "Yap1", "Hey1", "Egr3", "Fosl1", "Fosl2", "Tbx18", "Runx1", "Runx2",
"Mef2d", "Irf1", "Irf2", "Irf3", "Fli1", "Glis3", "Maff", "Bhlhe41", "Nr3c1")
ADRN <- c("Zfp536", "Phox2a", "Hand1", "Ascl1", "Klf13", "Sox11", "Gata2", "Gata3", "Klf7",
"Eya1", "Tfap2b", "Isl1", "Hey1", "Six3", "Dach1", "Phox2b", "Pbx3", "Satb1",
"Chga", "Chgb", "Dbh", "Dlk1", "Tfap2b", "Six3")
# https://www.cell.com/cell-reports/pdf/S2211-1247(22)01296-7.pdf
# https://www.nature.com/articles/ng.3899
# https://www.nature.com/articles/ng.3899
# https://www.nature.com/articles/s41467-019-09470-w#:~:text=Mesenchymal%2Dtype%20(MES)%20neuroblastoma,a%20high%20transcriptional%20plasticity1.
# Run gsea
custom_tum <- list(MES,ADRN)
names(custom_tum) <- c("MESENCHYMAL TUMOR", "ADRENERGIC TUMOR")
Idents(seuset_tumor) <- "condition"
clusters = unique(Idents(seuset_tumor))
save = "S05_tumorAdreMese_"
gseaRES(clusters, fgsea_sets = custom_tum, save = save,
minSize = 5, input = "tumor")
# Add module score to the Seurat seuset_tumor for each signature ###
seuset_tumor <- AddModuleScore(seuset_tumor, assay = "RNA", features = list(ADRN), name=make.names("Adrenergic"))
names([email protected])[grep(make.names("Adrenergic"), names([email protected]))] <- "Adrenergic"
seuset_tumor <- AddModuleScore(seuset_tumor, assay = "RNA", features = list(MES), name=make.names("Mesenchymal"))
names([email protected])[grep(make.names("Mesenchymal"), names([email protected]))] <- "Mesenchymal"
library(RColorBrewer)
png("TEPA_plots/S05_tumorAdreMese_FeaturePlot.png", h = 3000, w = 4500, res = 200)
patchwork::wrap_plots(FeaturePlot(seuset_tumor, ncol = 2, combine = TRUE, pt.size = 5,
features = c("Adrenergic", "Mesenchymal"), label = F,
repel = TRUE)) & theme_minimal() &
scale_colour_gradientn(colours = rev(brewer.pal(n = 11, name = "RdBu")))
dev.off()
Idents(seuset_tumor) <- "condition"
png("TEPA_plots/S05_tumorAdreMese_Dotplot.png", h = 2000, w = 2500, res = 300)
DotPlot(object = seuset_tumor, features = unique(c(MES,ADRN)),
scale=TRUE, col.min = -4, col.max = 4,
dot.min = 0, dot.scale = 5, cols = c("blue","red")) + RotatedAxis() + coord_flip() +
theme(axis.text.x = element_text(size=7), axis.text.y = element_text(size=7))
dev.off()
Idents(seuset_tumor) <- "condition"
png("TEPA_plots/S05_tumorAdreMese_ScatterPlot.png", h = 3000, w = 4200, res = 300)
FeatureScatter(seuset_tumor, feature1 = "Adrenergic", feature2 = "Mesenchymal", pt.size = 0.0005)
dev.off()