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comparative_deconv_analysis.rmd
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---
title: "R Notebook"
author: "Sushant Patkar"
date: "2024-06-05"
output:
md_document:
variant: markdown_github
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(nanostringr)
library(survival)
library(survminer)
library(ggplot2)
library(ggpubr)
library(ComplexHeatmap)
library(cluster)
library(readxl)
library(MCPcounter)
```
# Read in COTC021/22 bulk nanostring data
```{r}
rcc_dir <- list.dirs("~/Dog/Nanostring/", recursive = FALSE)
raw_expr <- lapply(rcc_dir, function(x) read_rcc(path = x)$raw)
sfs <- list()
sfs2 <- list()
i = 1
norm_expr <- lapply(raw_expr, function(x) {
#positive control normalization
gm <- exp(colMeans(log(x[x$Code.Class == "Positive",4:15]), na.rm = T))
am <- mean(gm, na.rm = T)
sf <- sapply(gm, function(y) am/y)
sfs[[i]] <<- sf
x1 <- as.matrix(x[,4:15])
x1 <- sapply(1:ncol(x1), function(i) sf[i] * as.numeric(x1[,i]))
#House keeping gene normalization
gm <- exp(colMeans(log(x[x$Code.Class == "Housekeeping", 4:15]), na.rm = T))
am <- mean(gm, na.rm = T)
sf <- sapply(gm, function(y) am/y)
sfs2[[i]] <<- sf
x2 <- sapply(1:ncol(x1),function(i) sf[i]*as.numeric(x1[,i]))
rownames(x2) <- x$Name
colnames(x2) <- colnames(x)[4:ncol(x)]
x2 <- x2[x$Code.Class == "Endogenous",]
x2 <- x2[,!is.na(sf) & !is.infinite(sf)]
i <<- i+1
return(x2)
})
combined_expr <- Reduce(cbind, norm_expr)
rownames(combined_expr) <- rownames(norm_expr[[1]])
samp_names <- sapply(colnames(combined_expr), function(x) {
splitstr = strsplit(x,split = "_")[[1]]
return(splitstr[3])
})
samp_names <- sapply(samp_names, trimws)
colnames(combined_expr) <- samp_names
combined_expr <- combined_expr[,!grepl("0814 RNAlater tumor", samp_names)]
nanostring_annotations <- read_excel("~/Dog/Nanostring/CS032282 and CS9318963 Nanostring 07-29-2022.xlsx")
nanostring_annotations <- as.data.frame(nanostring_annotations)
rownames(nanostring_annotations) <- nanostring_annotations$`Nanostring Sample Name`
common <- intersect(rownames(nanostring_annotations), colnames(combined_expr))
CE <- combined_expr[,common]
NaAn <- nanostring_annotations[common,]
```
#Read COTC021/22 bulk rnaseq data
```{r}
library(readr)
Canine_RNA_SEQ_TPM_All_data <- read_csv("~/Downloads/Canine_RNA_SEQ_TPM_All_data.csv")
Canine_RNA_SEQ_TPM_All_data <- as.data.frame(Canine_RNA_SEQ_TPM_All_data)
rownames(Canine_RNA_SEQ_TPM_All_data) <- Canine_RNA_SEQ_TPM_All_data$gene_id
Canine_RNA_SEQ_TPM_All_data <- Canine_RNA_SEQ_TPM_All_data[,-1]
sample_type <- c("Met","Primary")[as.numeric(grepl("(Tumor)|(tumor)", colnames(Canine_RNA_SEQ_TPM_All_data)))+1]
colnames(Canine_RNA_SEQ_TPM_All_data) <- sapply(colnames(Canine_RNA_SEQ_TPM_All_data), function(x) as.character(as.numeric(strsplit(x,split = '_')[[1]][2])))
rownames(Canine_RNA_SEQ_TPM_All_data) <- sapply(rownames(Canine_RNA_SEQ_TPM_All_data), function(x) gsub("_3","",x))
DOG2_RNAseq_Sample_list <- read_excel("~/Downloads/DOG2 RNAseq Sample list.xlsx")
DOG2_RNAseq_Sample_list$`Sample ID`[7] <- "18_0620_Met_Skin"
DOG2_RNAseq_Sample_list$`Sample ID`[8] <- "20_0621_Met_Unspecified"
sites_RNASeq <- rep("Primary", ncol(Canine_RNA_SEQ_TPM_All_data))
sites_RNASeq[sample_type == "Met"] <- sapply(DOG2_RNAseq_Sample_list$`Sample ID`[1:8], function(x) toupper(strsplit(x,split = "_")[[1]][4]))
```
# helper functions to measure expression of distinct pathway signatures: (eg chromosomal instability, cGAS-STING, type1 interferon, etc)
```{r}
library(msigdbr)
msigdbr_df <- msigdbr(species = "Homo sapiens", category = "H")
msigdbr_list = split(x = msigdbr_df$gene_symbol, f = msigdbr_df$gs_name)
CIN_signature <- function(expr){
genes <- c("CCNB2","PRC1","CDC45L","KIF20A","UBE2C","CDC2","MAD2L1","H2AFZ","TRIP13","ESPL1","TTK","FEN1","MCM7","CNAP1","MELK","TPX2","FOXM1","TOP2A","MCM2","RAD51AP1","RFC4","PCNA","RNASEH2A","TGIF2","CCT5")
mat <- as.data.frame(expr)
mat <- mat[genes,]
mat <- apply(as.matrix(mat),1,scale)
scores <- rowMeans(mat, na.rm = T)
return(scores)
}
CYT_signature <- function(expr){
genes <- c("GZMA","PRF1")
mat <- as.data.frame(expr)
mat <- log2(mat[genes,] +1)
scores <- colMeans(mat, na.rm = T)
return(scores)
}
type1_interferon_signature <- function(expr) {
genes <- msigdbr_list$HALLMARK_INTERFERON_ALPHA_RESPONSE
mat <- as.data.frame(expr)
mat <- mat[genes,]
mat <- apply(as.matrix(mat),1,scale)
scores <- rowMeans(mat, na.rm = T)
return(scores)
}
cGAS_STING_signature <- function(expr){
genes = c("CGAS","STING1","TBK1","IKBKE","IRF3")
mat <- as.data.frame(expr)
mat <- mat[genes,]
mat <- apply(as.matrix(mat),1,scale)
scores <- rowMeans(mat, na.rm = T)
return(scores)
}
```
#run MCP counter + clustering analysis on bulk rnaseq data
```{r}
library(RColorBrewer)
set.seed(2425)
canin_exp <- Canine_RNA_SEQ_TPM_All_data
cellprop_rnaseq<- t(MCPcounter.estimate(expression = log2(canin_exp+1), featuresType = "HUGO_symbols", probesets = read.table("~/Dog/MCP_counter_markers.csv")))
cellprop_rnaseq <- apply(cellprop_rnaseq, 2, scale)
rownames(cellprop_rnaseq) <- colnames(canin_exp)
#k means clustering
km.res <- kmeans(cellprop_rnaseq, centers = 3, nstart = 500, iter.max = 500)
cls <- km.res$cluster
imm_clus <- rep("ID", nrow(cellprop_rnaseq))
imm_clus[cls == 1] <- "IE-ECM"
imm_clus[cls == 2] <- "IE"
ii1 <- imm_clus
imm_clus <- factor(imm_clus, levels = c("IE","IE-ECM","ID"))
#Plot results
ha = rowAnnotation(
sample = sample_type,
preservation = rep("Frozen", length(sample_type)),
col = list("sample" = c("Primary" = "yellow", "Met" = "darkgreen"),
"preservation" = c("Frozen" = "#db6d00", "FFPE" = "#924900")
)
)
n <- 45
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
cols <- sample(col_vector, n, replace = FALSE)
cols <- sample(cols, length(unique(sites_RNASeq)), replace = F)
names(cols) <- unique(sites_RNASeq)
cols["Primary"] <- "white"
ha2 = rowAnnotation(
site = sites_RNASeq,
col = list("site"=cols)
)
Heatmap(cellprop_rnaseq, row_split = imm_clus, cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38","#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha, right_annotation = ha2)
p1 <- Heatmap(cellprop_rnaseq, row_split = imm_clus, cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38","#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha)
#pdf('~/Dog/TME_manuscript1.pdf')
#Heatmap(cellprop_rnaseq, row_split = imm_clus, cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38", "#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha)
#dev.off()
names(imm_clus) <- rownames(cellprop_rnaseq)
names(ii1) <- rownames(cellprop_rnaseq)
cyt_scores <- CYT_signature(canin_exp)
names(cyt_scores) <- rownames(cellprop_rnaseq)
cell_props_dat <- as.data.frame(cellprop_rnaseq)
cell_props_dat$imm_clus <- imm_clus
Heatmap(cellprop_rnaseq, row_split = imm_clus, cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38","#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha, right_annotation = ha2)
#pdf('~/Dog/MCP_marker_genes_expression.pdf',width = 18,height = 9)
#Heatmap(cellprop_rnaseq, row_split = imm_clus, cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38","#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha, right_annotation = ha2)
#dev.off()
p1 <- ggplot(data = data.frame(subtype = imm_clus, CIN_score = CIN_signature(log2(canin_exp+1))), aes(x = subtype, y=CIN_score)) + geom_boxplot() + theme_bw() + theme(text = element_text(size = 15), axis.text.x = element_text(size = 15)) + stat_compare_means(comparisons = list(c("IE","IE-ECM"),c("IE-ECM","ID"),c("IE","ID")), method = "wilcox", label = "p.signif")
#+ stat_compare_means(method = "anova", label = "p.format")
print(p1)
#ggsave(plot = p1, filename = "~/Dog/CIN_subtypes.pdf", width = 7, height = 6)
p2 <- ggplot(data = data.frame(subtype = imm_clus, CGAS_STING_score = cGAS_STING_signature(canin_exp)), aes(x = subtype, y=CGAS_STING_score)) + geom_boxplot() + theme_bw() + theme(text = element_text(size = 15), axis.text.x = element_text(size = 15)) + stat_compare_means(comparisons = list(c("IE","IE-ECM"),c("IE-ECM","ID"),c("IE","ID")), method = "wilcox", label = "p.signif")
print(p2)
p2 <- ggplot(data = data.frame(subtype = imm_clus, IN_score = type1_interferon_signature(log2(canin_exp+1))), aes(x = subtype,y=IN_score)) + geom_boxplot() +theme_bw() + stat_compare_means(method = "anova")
print(p2)
#ggsave(plot = p2, filename = "~/Dog/IN_caines.pdf", width = 4, height = 4)
p2 <- ggplot(data = data.frame(subtype = imm_clus, CYT_score = cyt_scores), aes(x = subtype,y=CYT_score)) + geom_boxplot() +theme_bw() + stat_compare_means(method = "anova") + ylab("Cytotoxicity scores")
print(p2)
#ggsave(plot = p2, filename = "~/Dog/CYT_caines.pdf", width = 4, height = 4)
```
#Assessing reproducibility of TME clusters via MCP counter analysis using recent canine single cell RNA seq cell type markers
```{r}
library(ConsensusClusterPlus)
set.seed(2425)
Ammons_signatures <- read_excel("~/Dog/Ammons_signatures.xlsx")
genes <- c()
cell_types <- c()
cell_subtypes <- c()
for(i in 1:nrow(Ammons_signatures)){
genes <- c(genes, strsplit(Ammons_signatures$Markers[i],split = ", ")[[1]])
cell_types <- c(cell_types, rep(Ammons_signatures$`Cell type`[i], length(strsplit(Ammons_signatures$Markers[i],split = ", ")[[1]])))
cell_subtypes <- c(cell_subtypes, rep(Ammons_signatures$`Cell type detailed`[i], length(strsplit(Ammons_signatures$Markers[i],split = ", ")[[1]])))
}
markers_ammons <- data.frame(HUGO_symbols = genes, Cell_population = cell_subtypes)
colnames(markers_ammons) <- c("HUGO symbols","Cell population")
cellprop_rnaseq2<- t(MCPcounter.estimate(expression = log2(canin_exp+1), featuresType = "HUGO_symbols", genes = markers_ammons))
cellprop_rnaseq2 <- apply(cellprop_rnaseq2, 2, scale)
rownames(cellprop_rnaseq2) <- colnames(canin_exp)
res = ConsensusClusterPlus(d = t(cellprop_rnaseq2), distance = "euclidean", clusterAlg = "km", pItem = 0.8, pFeature = 1, seed = 2425,maxK = 10, reps = 100,plot = "pdf",title = "ConsensusAmmonsRNASeq")
ha = rowAnnotation(
sample = sample_type,
col = list("sample" = c("Primary" = "yellow", "Met" = "darkgreen")
)
)
imm_clus_ammons <- rep("ID", nrow(cellprop_rnaseq2))
imm_clus_ammons[as.character(res[[3]]$consensusClass) == "3"] <- "IE"
imm_clus_ammons[as.character(res[[3]]$consensusClass) == "2"] <- "IE-ECM"
names(imm_clus_ammons) <- rownames(cellprop_rnaseq2)
Heatmap(cellprop_rnaseq2, row_split = factor(imm_clus_ammons, levels = c("IE","IE-ECM","ID")), cluster_columns = T, cluster_row_slices = FALSE, row_gap = unit(3, "mm"), row_title_gp = gpar(col = c("#00BA38", "#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha)
rownames(cellprop_rnaseq2) <- NULL
pdf('~/Dog/TME_manuscript_ammons.pdf', width = "13",height = "11")
Heatmap(cellprop_rnaseq2, row_split = factor(imm_clus_ammons, levels = c("IE","IE-ECM","ID")), cluster_columns = T, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38", "#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha)
dev.off()
chisq.test(table(factor(imm_clus,levels = c("IE","IE-ECM","ID")), factor(imm_clus_ammons, levels = c("IE","IE-ECM","ID"))))
```
# Validation of TME subtypes through quantitative imaging analysis usig matched H&E and IHC slides
```{r}
library(readr)
library(readxl)
library(dplyr)
trichrome_quant_updated <- read_csv("~/Dog/trichrome_MLH_quant_updated4.csv")
osteoid_quant_updated <- read_csv("~/Dog/osteoid_quant_MLH.csv")
#Filtering out poor quality cases post manual review
osteoid_quant_updated <- osteoid_quant_updated[!(osteoid_quant_updated$`Image Tag` %in% c("2023-09-16-03-34-18.ndpi",
"2023-09-20-13-12-21.ndpi",
"2023-09-23-17-29-02.ndpi",
"2023-09-26-23-32-10.ndpi",
"2023-09-27-12-06-34.ndpi",
"2023-09-27-17-26-47.ndpi")),]
tcell_quant_updated <- read_csv("~/Dog/tcell_quant_MLH.csv")
#Filtering out poor quality cases post manual review
tcell_quant_updated <- tcell_quant_updated[!(grepl('bad section', tcell_quant_updated$`ROI annotations completed`)),]
macrophage_quant_updated = read_csv("~/Dog/macrophage_quant_MLH.csv")
#Filtering out poor quality cases post manual review
macrophage_quant_updated <- macrophage_quant_updated[!(grepl('bad section', macrophage_quant_updated$`ROI annotations completed`)) & !(macrophage_quant_updated$Image == "2023-09-22-11-22-33.ndpi"),]
mac_density = data.frame(id = macrophage_quant_updated$`Subject Code`, density = macrophage_quant_updated$`Positive %`)
mac_density = mac_density %>% group_by(id) %>% summarize(density = mean(density, na.rm=TRUE))
tcell_density = data.frame(id = tcell_quant_updated$`Subject Code`, density = tcell_quant_updated$`Positive %`)
tcell_density = tcell_density %>% group_by(id) %>% summarize(density = mean(density, na.rm=TRUE))
tri_density = data.frame(id = trichrome_quant_updated$`Subject Code`, area = trichrome_quant_updated$`trichrome_threholder_hires: Collagen %`)
tri_density = tri_density %>% group_by(id) %>% summarize(area = mean(area, na.rm=TRUE))
osteoid_density = data.frame(id = osteoid_quant_updated$`Subject Code`, area = osteoid_quant_updated$`Tumor: % Osteoid`)
osteoid_density = osteoid_density %>% group_by(id) %>% summarize(area = mean(area, na.rm=TRUE))
tcell_density$subtype = ii1[as.character(tcell_density$id)]
tcell_density <- tcell_density[!is.na(tcell_density$subtype),]
tcell_density$subtype <- factor(tcell_density$subtype, levels = c("ID","IE-ECM","IE"))
mac_density$subtype = ii1[as.character(mac_density$id)]
mac_density <- mac_density[!is.na(mac_density$subtype),]
mac_density$subtype <- factor(mac_density$subtype, levels = c("ID","IE-ECM","IE"))
tri_density$subtype = ii1[as.character(tri_density$id)]
tri_density <- tri_density[!is.na(tri_density$subtype),]
tri_density$subtype <- factor(tri_density$subtype, levels = c("ID","IE-ECM","IE"))
osteoid_density$subtype = ii1[as.character(osteoid_density$id)]
osteoid_density <- osteoid_density[!is.na(osteoid_density$subtype),]
osteoid_density$subtype <- factor(osteoid_density$subtype, levels = c("ID","IE-ECM","IE"))
tcell_density$exp <- cellprop_rnaseq[as.character(tcell_density$id),"T cells"]
mac_density$exp <- cellprop_rnaseq[as.character(mac_density$id),"Monocytic lineage"]
tri_density$exp <- cellprop_rnaseq[as.character(tri_density$id),"Fibroblasts"]
osteoid_density$exp <- cellprop_rnaseq[as.character(osteoid_density$id),"Fibroblasts"]
p1 <- ggplot(data = tcell_density, aes(x=subtype, y = density)) + geom_boxplot() + stat_compare_means(comparisons = list(c("IE","IE-ECM"),c("IE-ECM","ID"),c("IE","ID")), method = "wilcox", label = "p.format") + theme_minimal() + ylab("%Positive") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "CD3 IHC (T cells)")
p2 <- ggplot(data = mac_density, aes(x=subtype, y = density)) + geom_boxplot() + stat_compare_means(comparisons = list(c("IE","IE-ECM"),c("IE-ECM","ID"),c("IE","ID")), method = "wilcox", label = "p.format") + theme_minimal() + ylab("%Positive") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "CD204 IHC (Macrophage)")
p3 <- ggplot(data = tri_density, aes(x=subtype, y = area)) + geom_boxplot() + stat_compare_means(comparisons = list(c("IE","IE-ECM"),c("IE-ECM","ID"),c("IE","ID")), method = "wilcox", label = "p.format") + theme_minimal() + ylab("%Collagen") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "Trichrome (Collagen)")
p33 <- ggplot(data = osteoid_density, aes(x=subtype, y = area)) + geom_boxplot() + stat_compare_means(comparisons = list(c("IE","IE-ECM"),c("IE-ECM","ID"),c("IE","ID")), method = "wilcox", label = "p.format") + theme_minimal() + ylab("%Osteoid") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "Osteoid")
p6 <- ggplot(data = tri_density, aes(x = exp, y= area)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 1,
label = paste0('r = ', round(cor(tri_density$area, tri_density$exp, method = c("pearson")), 4)),
color = 'red') + ylab("%Collagen") + xlab("MCP counter score (Stroma)") + theme(text = element_text(size = 15)) + labs(title = "Trichrome (Collagen)")
p5 <- ggplot(data = mac_density, aes(x = exp, y= density)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 1,
label = paste0('r = ', round(cor(mac_density$density, mac_density$exp, method = c("pearson")), 4)),
color = 'red') + ylab("%Positive") + xlab("MCP counter score (Monocytic lineage)") + theme(text = element_text(size = 15)) + labs(title = "CD204 IHC (Macrophage)")
p4 <- ggplot(data = tcell_density, aes(x = exp, y= density)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 1,
label = paste0('r = ', round(cor(tcell_density$density, tcell_density$exp, method = c("pearson")), 4)),
color = 'red') + ylab("%Positive") + xlab("MCP counter score (T cells)") + theme(text = element_text(size = 15)) + labs(title = "CD3 IHC (T cells)")
p66 <- ggplot(data = osteoid_density, aes(x = exp, y= area)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 1,
label = paste0('r = ', round(cor(osteoid_density$area, osteoid_density$exp, method = c("pearson")), 4)),
color = 'red') + ylab("%Osteoid") + xlab("MCP counter score (Stroma)") + theme(text = element_text(size = 15)) + labs(title = "Osteoid")
fig1 <- ggarrange(p1,p2,p3,p33,p4,p5,p6,p66, nrow = 2, ncol = 4, labels = c("A","B","C","D","E","F","G","H"))
print(fig1)
########################################################################################################################
mac_density = data.frame(id = macrophage_quant_updated$`Subject Code`, density = macrophage_quant_updated$`Num Positive per mm^2`)
mac_density = mac_density %>% group_by(id) %>% summarize(density = mean(density, na.rm=TRUE))
tcell_density = data.frame(id = tcell_quant_updated$`Subject Code`, density = tcell_quant_updated$`Num Positive per mm^2`)
tcell_density = tcell_density %>% group_by(id) %>% summarize(density = mean(density, na.rm=TRUE))
tri_density = data.frame(id = trichrome_quant_updated$`Subject Code`, area = trichrome_quant_updated$`trichrome_threholder_hires: Collagen %`)
tri_density = tri_density %>% group_by(id) %>% summarize(area = mean(area, na.rm=TRUE))
osteoid_density = data.frame(id = osteoid_quant_updated$`Subject Code`, area = osteoid_quant_updated$`Tumor: % Osteoid`)
osteoid_density = osteoid_density %>% group_by(id) %>% summarize(area = mean(area, na.rm=TRUE))
tcell_density$subtype = ii1[as.character(tcell_density$id)]
tcell_density <- tcell_density[!is.na(tcell_density$subtype),]
tcell_density$subtype <- factor(tcell_density$subtype, levels = c("ID","IE-ECM","IE"))
mac_density$subtype = ii1[as.character(mac_density$id)]
mac_density <- mac_density[!is.na(mac_density$subtype),]
mac_density$subtype <- factor(mac_density$subtype, levels = c("ID","IE-ECM","IE"))
tri_density$subtype = ii1[as.character(tri_density$id)]
tri_density <- tri_density[!is.na(tri_density$subtype),]
tri_density$subtype <- factor(tri_density$subtype, levels = c("ID","IE-ECM","IE"))
osteoid_density$subtype = ii1[as.character(osteoid_density$id)]
osteoid_density <- osteoid_density[!is.na(osteoid_density$subtype),]
osteoid_density$subtype <- factor(osteoid_density$subtype, levels = c("ID","IE-ECM","IE"))
tcell_density$exp <- cellprop_rnaseq[as.character(tcell_density$id),"T cells"]
mac_density$exp <- cellprop_rnaseq[as.character(mac_density$id),"Monocytic lineage"]
tri_density$exp <- cellprop_rnaseq[as.character(tri_density$id),"Fibroblasts"]
osteoid_density$exp <- cellprop_rnaseq[as.character(osteoid_density$id),"Fibroblasts"]
p1 <- ggplot(data = tcell_density, aes(x=subtype, y = density)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("Density") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "CD3 IHC (T cells)")
p2 <- ggplot(data = mac_density, aes(x=subtype, y = density)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("Density") + xlab("Suntype") + theme(text = element_text(size = 15)) + labs(title = "CD204 IHC (Macrophage)")
p3 <- ggplot(data = tri_density, aes(x=subtype, y = area)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("%Collagen") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "Trichrome (Collagen)")
p33 <- ggplot(data = osteoid_density, aes(x=subtype, y = area)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("%Osteoid") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "Osteoid")
p6 <- ggplot(data = tri_density, aes(x = exp, y= area)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 10,
label = paste0('Spearman rho = ', round(cor(tri_density$area, tri_density$exp, method = c("spearman")), 4)),
color = 'red') + ylab("%Collagen") + xlab("MCP counter score (Stroma)") + theme(text = element_text(size = 15)) + labs(title = "Trichrome (Collagen)")
p5 <- ggplot(data = mac_density, aes(x = exp, y= density)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 1,
label = paste0('Spearman rho = ', round(cor(mac_density$density, mac_density$exp, method = c("spearman")), 4)),
color = 'red') + ylab("Density") + xlab("MCP counter score (Monocytic lineage)") + theme(text = element_text(size = 15)) + labs(title = "CD204 IHC (Macrophage)")
p4 <- ggplot(data = tcell_density, aes(x = exp, y= density)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 1,
label = paste0('Spearman rho = ', round(cor(tcell_density$density, tcell_density$exp, method = c("spearman")), 4)),
color = 'red') + ylab("Density") + xlab("MCP counter score (T cells)") + theme(text = element_text(size = 15)) + labs(title = "CD3 IHC (T cells)")
p66 <- ggplot(data = osteoid_density, aes(x = exp, y= area)) + geom_point()+ geom_smooth(method = "lm", se = FALSE) + theme_minimal() + geom_text(x = 1, y = 1,
label = paste0('Spearman rho = ', round(cor(osteoid_density$area, osteoid_density$exp, method = c("spearman")), 4)),
color = 'red') + ylab("%Osteoid") + xlab("MCP counter score (Stroma)") + theme(text = element_text(size = 15)) + labs(title = "Osteoid")
fig2 <- ggarrange(p1,p2,p3,p33,p4,p5,p6,p66, nrow = 2, ncol = 4, labels = c("A","B","C","D","E","F","G","H"))
print(fig2)
p1 <- ggplot(data = tcell_density, aes(x=subtype, y = density)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("Density") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "CD3 IHC (T cells)") + scale_y_continuous(limits = c(0,4500))
p2 <- ggplot(data = mac_density[mac_density$density < 6000,], aes(x=subtype, y = density)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("Density") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "CD204 IHC (Macrophage)") + scale_y_continuous(limits = c(0,4500))
p3 <- ggplot(data = tri_density, aes(x=subtype, y = area)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("%Collagen") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "Trichrome (Collagen)")
p33 <- ggplot(data = osteoid_density, aes(x=subtype, y = area)) + geom_boxplot() + stat_compare_means(method = "anova") + theme_minimal() + ylab("%Osteoid") + xlab("Subtype") + theme(text = element_text(size = 15)) + labs(title = "Osteoid")
fig3 <- ggarrange(p1,p2,p3,p33, nrow = 2, ncol = 2, labels = c("A","B","C","D"))
print(fig3)
#ggsave(plot = fig3, filename = "~/Dog/Final_IHC_validation_figure_panels.pdf", width = 6, height = 6)
```
#Plotting distribution of immune checkpoint gene expression across TME clusters
```{r}
library(reshape)
out <- t(log2(canin_exp[intersect(c("PDCD1","CD274","CTLA4","TIGIT","LAG3","HAVCR2"), rownames(canin_exp)),]+1))
out <- data.frame(apply(out,2,scale), subtype = imm_clus)
dd_out <- melt(out, id.vars = "subtype")
colnames(dd_out) <- c("subtype","ICI_target","expression")
dd_out$ICI_target <- as.character(dd_out$ICI_target)
dd_out$subtype <- factor(dd_out$subtype, levels = c("IE","IE-ECM","ID"))
dd_out$ICI_target[dd_out$ICI_target == "PDCD1"] = "PD1"
dd_out$ICI_target[dd_out$ICI_target == "CD274"] = "PDL1"
#plot results
p1 <- ggboxplot(dd_out, x = "subtype", y="expression", facet.by = "ICI_target") + theme_bw() + theme(text = element_text(size = 15)) + stat_compare_means(method = "anova")+ xlab("") + ylab("expression (normalized)")
print(p1)
#ggsave(plot = p1, filename = "~/Dog/canine_immune_checkpoints.pdf")
```
#Assessing reproducibility of TME clusters via xCell analysis
```{r}
library(xCell)
out <- t(xCellAnalysis(canin_exp))
res = ConsensusClusterPlus(d = t(apply(out,2,scale)), distance = "euclidean", clusterAlg = "km", pItem = 0.8, pFeature = 1, seed = 2425,maxK = 10, reps = 100, plot = "pdf",title = "ConsensusXCellRNASeq")
ha = rowAnnotation(
sample = sample_type,
col = list("sample" = c("Primary" = "yellow", "Met" = "darkgreen")
)
)
#plot results
Heatmap(apply(out,2,scale), row_split = as.character(res[[3]]$consensusClass), cluster_columns = T, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38", "#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha)
#pdf('~/Dog/TME_manuscript3.pdf', width = "13",height = "11")
#Heatmap(apply(out,2,scale), row_split = imm_clus, cluster_columns = T, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), row_title_gp = gpar(col = c("#00BA38", "#619CFF","#F8766D")), row_names_gp = gpar(cex = 0.5), left_annotation = ha)
#dev.off()
```
#run MCP counter + clustering analysis on bulk nanostring data
```{r}
set.seed(24524)
library(RColorBrewer)
cellprop_nano <- t(MCPcounter.estimate(expression = log2(cbind(CE[,NaAn$`Sample Type` %in% c("Primary Tumor")],CE[,NaAn$`Sample Type` %in% c("Met")])+1), featuresType = "HUGO_symbols", probesets = read.table("~/Dog/MCP_counter_markers.csv")))
#expression of immune checkpoint genes in nanostring
chkpt_nano <- cbind(CE[,NaAn$`Sample Type` %in% c("Primary Tumor")],CE[,NaAn$`Sample Type` %in% c("Met")])
chkpt_nano <- t(chkpt_nano[intersect(rownames(chkpt_nano), c("PDCD1","CD274","CTLA4","LAG3","HAVCR2","TIGIT")),])
nanostring_annotations <- rbind(NaAn[NaAn$`Sample Type` %in% c("Primary Tumor"),], NaAn[NaAn$`Sample Type` %in% c("Met"),])
sample_preservation = c("Frozen","FFPE")[as.numeric(grepl("FFPE",nanostring_annotations$`Name, detailed`))+1]
#perform sample preservation-specific normalization to mitigate batch effects across different sample preservation techniques
cellprop_nano_ffpe <- apply(cellprop_nano[sample_preservation == "FFPE",],2,scale)
chkpt_nano_ffpe <- apply(chkpt_nano[sample_preservation == "FFPE",],2,scale)
km.res <- kmeans(cellprop_nano_ffpe, centers = 3, nstart = 100, iter.max = 500)
imm_clus_nano_ffpe <- rep("ID", nrow(cellprop_nano_ffpe))
imm_clus_nano_ffpe[km.res$cluster == 2] <- "IE"
imm_clus_nano_ffpe[km.res$cluster == 1] <- "IE-ECM"
Heatmap(cellprop_nano_ffpe, row_split = imm_clus_nano_ffpe, cluster_column_slices = F, cluster_columns = F)
cellprop_nano_frozen <- apply(cellprop_nano[sample_preservation == "Frozen",],2,scale)
chkpt_nano_frozen <- apply(chkpt_nano[sample_preservation == "Frozen",],2,scale)
km.res <- kmeans(cellprop_nano_frozen, centers = 3, nstart = 100, iter.max = 500)
imm_clus_nano_frozen <- rep("ID", nrow(cellprop_nano_frozen))
imm_clus_nano_frozen[km.res$cluster == 3] <- "IE"
imm_clus_nano_frozen[km.res$cluster == 2] <- "IE-ECM"
Heatmap(cellprop_nano_frozen, row_split = imm_clus_nano_frozen, cluster_column_slices = F, cluster_columns = F)
#merge FFPe and frozen sample results
cellprop_nano <- rbind(cellprop_nano_ffpe, cellprop_nano_frozen)
chkpt_nano <- rbind(chkpt_nano_ffpe, chkpt_nano_frozen)
nanostring_annotations <- rbind(nanostring_annotations[sample_preservation == "FFPE",],nanostring_annotations[sample_preservation == "Frozen",])
imm_clus_nano = c(imm_clus_nano_ffpe, imm_clus_nano_frozen)
sample_preservation <- c(sample_preservation[sample_preservation == "FFPE"],sample_preservation[sample_preservation == "Frozen"])
#Primary vs metastatic tumor sample status
sstatus = nanostring_annotations$`Sample Type`
ha = rowAnnotation(
sample = sstatus,
col = list("sample" = c("Primary Tumor" = "yellow", "Met" = "green")
)
)
#metastatic site information
ss <- toupper(nanostring_annotations$Site)
ss_split <- rep("OTHER",length(ss))
ss_split[ss == "LUNG"] <- "LUNG"
ss_split[ss == "KIDNEY"] <- "KIDNEY"
ss_split[ss == "LIVER"] <- "LIVER"
ss_split[ss %in% c("BONE","RIB","L4","T5","T6","T11/T13","SPINE")] <- "SKELETAL"
ss_split[ss == "PRIMARY"] <- "PRIMARY"
n <- 70
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
cols <- sample(col_vector, n, replace = F)
cols <- sample(cols, length(unique(ss)), replace = F)
names(cols) <- unique(ss)
#plot results
ha = rowAnnotation(
sample = sstatus,
preservation = sample_preservation,
col = list("sample" = c("Primary Tumor" = "yellow", "Met" = "darkgreen"),
"preservation" = c("Frozen" = "#db6d00", "FFPE" = "#924900")
)
)
ha2 = rowAnnotation(
subtype = imm_clus_nano,
col = list("subtype" = c("IE" = "#882255", "IE-ECM" = "#CC6677", "ID" = "#88CCEE"))
)
names(imm_clus_nano) <- as.character(as.numeric(nanostring_annotations$`Subject Code`))
ii <- imm_clus_nano
imm_clus_nano <- factor(imm_clus_nano, levels = c("IE","IE-ECM", "ID"))
rownames(cellprop_nano) <- names(ii)
Heatmap(cellprop_nano, row_split = ss_split, cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), left_annotation = ha, right_annotation = ha2)
#pdf("~/Dog/metTME_landscape.pdf", height = 14, width = 8)
#Heatmap(cellprop_nano, row_split = ss_split, cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(3, #"mm"), left_annotation = ha, right_annotation = ha2)
#dev.off()
#rownames(cellprop_nano) <- samp_old
```
#Plotting distribution of immune checkpoint gene expression across TME clusters (Nanostring)
```{r}
Heatmap(chkpt_nano, row_split = ss_split, cluster_columns = T, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), left_annotation = ha, right_annotation = ha2)
out <- data.frame(chkpt_nano, subtype = imm_clus_nano)
dd_out <- melt(out, id.vars = "subtype")
colnames(dd_out) <- c("subtype","ICI_target","scaled_expression")
dd_out$ICI_target <- as.character(dd_out$ICI_target)
dd_out$subtype <- factor(dd_out$subtype, levels = c("IE","IE-ECM","ID"))
dd_out$ICI_target[dd_out$ICI_target == "PDCD1"] = "PD1"
dd_out$ICI_target[dd_out$ICI_target == "CD274"] = "PDL1"
p1 <- ggboxplot(dd_out, x = "subtype", y="scaled_expression", facet.by = "ICI_target") + theme_bw() + theme(text = element_text(size = 15)) + stat_compare_means(method = "anova") + xlab("") + ylab("expression (normalized)")
#stat_compare_means(comparisons = list(c("IE","IE-ECM"),c("IE-ECM","ID"),c("IE","ID")), method = "wilcox", label = "p.signif")
print(p1)
#ggsave(plot = p1, filename = "~/Dog/canine_immune_checkpoints_nano.pdf")
```
#Joint analysis of primary and metastatic tumor samples from dogs enolled in COTC021/22
```{r}
library(tibble)
set.seed(317796929)
#All metastatic samples (Nanostring + RNASeq)
a1 <- rbind(cellprop_nano[sstatus == "Met",], cellprop_rnaseq[sample_type == "Met",])
source_a1 <- c(rep("Nanostring", nrow(cellprop_nano[sstatus == "Met",])), rep("RNASeq",nrow(cellprop_rnaseq[sample_type == "Met",])))
prep_a1 <- c(sample_preservation[sstatus == "Met"], rep("Frozen",nrow(cellprop_rnaseq[sample_type == "Met",])))
imm_clus_a1 <- c(ii[sstatus == "Met"], ii1[sample_type == "Met"])
sites_a1 <- c(ss[sstatus == "Met"], sites_RNASeq[sample_type == "Met"])
#All primary samples (Nanostring + RNASeq)
a2 <- rbind(cellprop_rnaseq[sample_type == "Primary",], cellprop_nano[sstatus == "Primary Tumor",])
source_a2 <- c(rep("RNASeq", nrow(cellprop_rnaseq[sample_type == "Primary",])), rep("Nanostring",nrow(cellprop_nano[sstatus == "Primary Tumor",])))
prep_a2 <- c(rep("Frozen",nrow(cellprop_rnaseq[sample_type == "Primary",])), sample_preservation[sstatus == "Primary Tumor"])
imm_clus_a2 <- c(ii1[sample_type == "Primary"], ii[sstatus == "Primary Tumor"])
sites_a2 <- rep("Primary", length(imm_clus_a2))
#match tumor samples from same patient
common <- intersect(rownames(a1), rownames(a2))
case_list <- list()
for(pt in common){
xx <- a1[rownames(a1) == pt,]
if(is.null(dim(xx))){
xx <- t(xx)
}
d1 <- data.frame(xx)
d1$sample_type = "Metastatic"
d1$source = source_a1[rownames(a1) == pt]
d1$prep = prep_a1[rownames(a1) == pt]
d1$site = sites_a1[rownames(a1) == pt]
d1$case = pt
d1$immune_subtype = imm_clus_a1[rownames(a1) == pt]
xx <- a2[rownames(a2) == pt,]
if(is.null(dim(xx))){
xx <- t(xx)
}
d2 <- data.frame(xx)
d2$sample_type = "Primary"
d2$source = source_a2[rownames(a2) == pt]
d2$prep = prep_a2[rownames(a2) == pt]
d2$site = sites_a2[rownames(a2) == pt]
d2$case = pt
d2$immune_subtype = imm_clus_a2[rownames(a2) == pt]
case_list[[pt]] <- rbind(d2,d1)
}
matched_cases <- Reduce(rbind, case_list)
matched_cases$sample_type <- factor(matched_cases$sample_type, levels = c("Primary", "Metastatic"))
ss1_split <- rep("OTHER",nrow(matched_cases))
ss1_split[matched_cases$site == "LUNG"] <- "LUNG"
ss1_split[matched_cases$site == "KIDNEY"] <- "KIDNEY"
ss1_split[matched_cases$site == "LIVER"] <- "LIVER"
ss1_split[matched_cases$site %in% c("BONE","RIB","L4","T5","T6","T11/T13","SPINE")] <- "SKELETAL"
ss1_split[matched_cases$site == "Primary"] <- "PRIMARY"
#plot results
ha = columnAnnotation(
site = ss1_split,
preservation = matched_cases$prep,
platform = matched_cases$source,
subtype = matched_cases$immune_subtype,
col = list("site" = c("OTHER" = "#009E73",
"PRIMARY" = "#F0E442",
"LUNG"="#DF65B0",
"KIDNEY" = "#984EA3",
"SKELETAL" = "#FEE5D9",
"LIVER" = "#FB6A4A"),
"preservation" = c("Frozen" = "#db6d00",
"FFPE" = "#924900"),
"platform" = c("Nanostring" = "#525252",
"RNASeq" = "#F2F0F7"),
"subtype" = c("ID" = "#88CCEE",
"IE" = "#882255",
"IE-ECM" = "#CC6677")
)
)
matched_cases$site2 <- ss1_split
met_mat <- as.matrix(matched_cases[,1:10])
rownames(met_mat) <- matched_cases$case
Heatmap(t(met_mat), column_split = matched_cases$sample_type, cluster_column_slices = F, column_gap = unit(5, "mm"), cluster_rows = F, cluster_columns = T, top_annotation = ha, column_names_gp = grid::gpar(fontsize = 8))
sampIds1 <- rownames(a1)
idx <- sapply(sampIds1, function(x) x %in% rownames(a2))
a1 = a1[idx,]
imm_clus_a1 = imm_clus_a1[idx]
ss1 <- sites_a1[idx]
ha = rowAnnotation(
sample = rep("Primary Tumor", nrow(a2[sapply(rownames(a2), function(x) x %in% sampIds1[idx]),])),
col = list("Primary Tumor" = "yellow", "Met "= "darkgreen")
)
#pdf("~/Dog/Met_evolve_primary.pdf", width = 5, height = 10)
#Heatmap(a2[sapply(rownames(a2), function(x) x %in% rownames(a1)),], row_split = imm_clus_a2[sapply(rownames(a2), function(x) x %in% rownames(a1))], cluster_columns = F, cluster_row_slices = FALSE, row_gap = unit(5, "mm"), cluster_rows = F, left_annotation = ha)
#dev.off()
n <- length(unique(ss1))
#qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
#col_vector = grDevices::colors()[grep('gr(a|e)y', grDevices::colors(), invert = T)]
cols <- sample(col_vector, n, replace = F)
ss1_split <- rep("OTHER",length(ss1))
ss1_split[ss1 == "LUNG"] <- "LUNG"
ss1_split[ss1 == "KIDNEY"] <- "KIDNEY"
ss1_split[ss1 == "LIVER"] <- "LIVER"
ss1_split[ss1 %in% c("BONE","RIB","L4","T5","T6","T11/T13","SPINE")] <- "SKELETAL"
ss1_split[ss1 == "PRIMARY"] <- "PRIMARY"
names(cols) <- unique(ss1)
#samp <- sapply(rownames(a1), function(x) as.character(as.numeric(strsplit(x,"_")[[1]][2])))
#rownames(a1) <- samp
#names(imm_clus_a1) <- rownames(a1)
ha = rowAnnotation(
met_subtype = imm_clus_a1[imm_clus_a2[names(imm_clus_a1)] == "IE-ECM"],
col = list("met_subtype" = c("IE" = "#882255", "IE-ECM" = "#CC6677", "ID" = "#88CCEE")
)
)
ha2 = rowAnnotation(
sample = rep("Met", length(imm_clus_a1[imm_clus_a2[names(imm_clus_a1)] == "IE-ECM"])),
col = list("Primary Tumor"="yellow","Met"="darkgreen")
)
#pdf("~/Dog/Met_evolve_IE-ECM.pdf", width = 5, height = 10)
#Heatmap(a1[imm_clus_a2[names(imm_clus_a1)] == "IE-ECM",], row_split = ss1_split[imm_clus_a2[names(imm_clus_a1)] == "IE-ECM"], cluster_columns = T, cluster_row_slices = FALSE, row_gap = unit(3, "mm"), right_annotation = ha,left_annotation = ha2)
#dev.off()
ha = rowAnnotation(
met_subtype = imm_clus_a1[imm_clus_a2[names(imm_clus_a1)] == "ID"],
col = list("met_subtype" = c("IE" = "#882255", "IE-ECM" = "#CC6677", "ID" = "#88CCEE")
)
)
ha2 = rowAnnotation(
sample = rep("Met", length(imm_clus_a1[imm_clus_a2[names(imm_clus_a1)] == "ID"])),
col = list("Primary Tumor"="yellow","Met"="darkgreen")
)
#pdf("~/Dog/Met_evolve_ID.pdf", width = 5, height = 10)
#Heatmap(a1[imm_clus_a2[names(imm_clus_a1)] == "ID",], row_split = ss1_split[imm_clus_a2[names(imm_clus_a1)] == "ID"], cluster_columns =T, cluster_row_slices = FALSE, row_gap = unit(3, "mm"), right_annotation = ha, left_annotation = ha2)
#dev.off()
#contingency table of matched tumor samples
mat_subtype <- matrix(0,nrow = 3, ncol = 3)
rownames(mat_subtype) <- c("IE","IE-ECM","ID")
colnames(mat_subtype) <- c("IE","IE-ECM","ID")
edge_list <- list()
idx <- 1
for(id in unique(matched_cases$case)){
case_id <- which(matched_cases$case == id)
is_prim <- matched_cases$sample_type[case_id] == "Primary"
is_met <- matched_cases$sample_type[case_id] == "Metastatic"
for(i in case_id[is_prim]){
for(j in case_id[is_met]){
for(subtype_i in c("IE","IE-ECM","ID")){
for(subtype_j in c("IE","IE-ECM","ID")){
if(matched_cases$immune_subtype[i] == subtype_i & matched_cases$immune_subtype[j] == subtype_j){
mat_subtype[subtype_i,subtype_j] = mat_subtype[subtype_i, subtype_j] + 1
edge_list[[idx]] <- c(i,j)
idx <- idx + 1
}
}
}
}
}
}
print(mat_subtype)
chisq.test(mat_subtype, simulate.p.value = TRUE)
#generate sankey diagram
library(plotly)
#reticulate::import("sys")
matched_cases$site[matched_cases$site == "SPINAL CORD"] = "EXTRADURAL"
matched_cases$site[matched_cases$site == "LIP"] = "ORAL"
prim_sample <- sapply(edge_list, function(x) paste("primary:",x[1], sep = ""))
met_sample <- sapply(edge_list, function(x) paste(matched_cases$site[x[2]],x[2],sep = ":"))
node_lab <- c(unique(sapply(edge_list, function(x) matched_cases$case[x[1]])), unique(prim_sample), unique(met_sample))
subtype_col <- c("IE" = "#882255", "IE-ECM" = "#CC6677", "ID" = "#88CCEE")
node_col <- c(rep("grey", length(unique(sapply(edge_list, function(x) matched_cases$case[x[1]])))),
subtype_col[sapply(unique(prim_sample), function(x) matched_cases$immune_subtype[as.numeric(strsplit(x,split = ":")[[1]][2])])],
subtype_col[sapply(unique(met_sample), function(x) matched_cases$immune_subtype[as.numeric(strsplit(x,split = ":")[[1]][2])])])
names(node_col) <- NULL
elist1 <- t(sapply(edge_list, function(x) {
a <- matched_cases$case[x[1]]
b <- paste("primary:",x[1], sep = "")
return(c(which(node_lab == a), which(node_lab == b)))
}))
elist2 <- t(sapply(edge_list, function(x) {
a <- paste("primary:",x[1], sep = "")
b <- paste(matched_cases$site[x[2]],x[2],sep = ":")
return(c(which(node_lab == a), which(node_lab == b)))
}))
elist <- rbind(elist1,elist2)
fig <- plot_ly(
type = "sankey",
domain = list(
x = c(0,1),
y = c(0,1)
),
orientation = "h",
valueformat = ".0f",
valuesuffix = "TWh",
node = list(
label = sapply(node_lab, function(x) strsplit(x,split = ":")[[1]][1]),
color = node_col,
pad = 15,
thickness = 15,
line = list(
color = "black",
width = 0.5
)
),
link = list(
source = elist[,1]-1,
target = elist[,2]-1,
value = rep(1,nrow(elist))
)
)
fig <- fig %>% layout(
title = " ",
font = list(
size = 9
),
xaxis = list(showgrid = F, zeroline = F),
yaxis = list(showgrid = F, zeroline = F)
)
print(fig)
#save_image(fig, file = "/Users/sushantpa/Dog/met_evolve_kaleido.pdf",width = 410, height = 720)
```
#Read clinical outcome data
```{r}
library(survival)
library(survminer)
#Scripts to read in the canine OS clinical metadata
#clinical metadata for all cases that received Rapamycin (mTOR inhibitor) in addition to standard of care therapy
Slides2Outcomes_Rapa_all <- read_csv("~/Dog/Slides2Outcomes_Rapa_all_new.csv")
#clinical metadata for all cases that received standard of care therapy
Slides2Outcomes_SOC_all <- read_csv("~/Dog/Slides2Outcomes_SOC_all_new.csv")
dat1 <- subset(Slides2Outcomes_Rapa_all,select = c("slide","Patient ID","Tumor Location","Site","age","weight","breed","gender","PH","ALP","Group","DFI","DFI_censor","Survival (days from sx)","Surv_censor"))
dat1$treatment = rep("Rapamycin", nrow(dat1))
dat2 <- subset(Slides2Outcomes_SOC_all,select = c("slide","Patient ID","Tumor Location","Site","age","weight","breed","gender","PH","ALP","Group","DFI","DFI_censor","Survival (days from sx)","Surv_censor"))
dat2$treatment = rep("SOC", nrow(dat2))
clindat_all <- rbind(dat1,dat2)
clindat_all <- as.data.frame(clindat_all)
clindat_all <- clindat_all[!duplicated(clindat_all$slide),]
rownames(clindat_all) <- substr(clindat_all$slide,1,19)
clindat_all <- clindat_all[,-1]
colnames(clindat_all)[c(11,12,13,14)] <- c("DFS_time","DFS_status","OS_time","OS_status")
clindat_all$ALP[clindat_all$ALP == "elevated"] <- "Elevated"
clindat_all$gender[clindat_all$`Patient ID` == "307"] <- "Spayed Female"
clindat_all$gender[grepl("Castrated",clindat_all$gender)] <- "Castrated Male"
clindat_all$gender[grepl("Spayed",clindat_all$gender)] <- "Spayed Female"
clindat_all$gender[grepl("Male Phenotype",clindat_all$gender)] <- "Intact Male"
clindat_all$gender[grepl("Female Phenotype",clindat_all$gender)] <- "Intact Female"
clindat_all <- clindat_all[!duplicated(clindat_all$`Patient ID`),]
cc1 <- clindat_all
rownames(cc1) <- as.character(clindat_all$`Patient ID`)
common <- intersect(names(imm_clus)[sample_type == "Primary"], rownames(cc1))
cc1 <- cc1[common,]
cc1$primary_immune_subtype <- as.character(imm_clus[sample_type == "Primary"][common])
#cc1$integrated = cc1$group
#cc1$integrated[cc1$group == "{cluster 1, 2}"] = cc1$immune_subtype[cc1$group == "{cluster 1, 2}"]
ccc1 <-cc1
#ccc1 <- ccc1[names(met_subtype_lung),]
#ccc1$met_subtype_lung <- met_subtype_lung
surv <- survfit(Surv(OS_time, OS_status) ~ primary_immune_subtype, data = ccc1)
diff <- survdiff(Surv(OS_time, OS_status) ~ primary_immune_subtype, data = ccc1)
p1 <- ggsurvplot(surv, data = ccc1,
legend.title = "",
legend = "right",
conf.int = F,
pval = TRUE,
risk.table = TRUE,
tables.height = 0.2,
tables.theme = theme_cleantable(),
risk.table.y.text = FALSE,
pval.coord = c(0, 0.03),
# Color palettes. Use custom color: c("#E7B800", "#2E9FDF"),
# or brewer color (e.g.: "Dark2"), or ggsci color (e.g.: "jco")
palette = c("#88CCEE","#882255","#CC6677"),
ggtheme = theme(text = element_text(size = 15)) +theme_bw() # Change ggplot2 theme
) + xlab("Time (days from sx)")
print(p1)
#ggsave(filename = "~/Dog/canine_OS_survfigure.pdf", plot=p1$plot, width = 9, height = 6)
surv <- survfit(Surv(DFS_time, DFS_status) ~ primary_immune_subtype, data = ccc1)
diff <- survdiff(Surv(DFS_time, DFS_status) ~ primary_immune_subtype, data = ccc1)
p1 <- ggsurvplot(surv, data = ccc1,
legend.title = "",
legend = "right",
conf.int = F,
pval = TRUE,
risk.table = TRUE,
tables.height = 0.2,
tables.theme = theme_cleantable(),
risk.table.y.text = FALSE,
pval.coord = c(0, 0.03),
# Color palettes. Use custom color: c("#E7B800", "#2E9FDF"),
# or brewer color (e.g.: "Dark2"), or ggsci color (e.g.: "jco")
palette = c("#88CCEE","#882255","#CC6677"),
ggtheme = theme(text = element_text(size = 15)) + theme_bw() # Change ggplot2 theme
) + xlab("Time (days from sx)")
print(p1)
#ggsave(filename = "~/Dog/canine_DFS_survfigure.pdf", plot=p1$plot, width = 9, height = 6)
```
#Cox proportional hazards analysis
```{r}
cph_dat <- data.frame(time = cc1$DFS_time,
subtype = cc1$primary_immune_subtype,
cytotoxicity_score = as.numeric(cyt_scores[common]),
status = cc1$DFS_status,
location = c("NPH","PH")[as.numeric(ccc1$PH)+1],
ALP = cc1$ALP,
age = scale(as.numeric(cc1$age)),
gender = cc1$gender,
weight = scale(cc1$weight),
treatment = c("SOC","Sirolimus+SOC")[as.numeric(cc1$treatment == "Rapamycin")+1])
cph_dat$gender[grepl("female", ignore.case = T, cph_dat$gender)] = "F"
cph_dat$gender[grepl("male", ignore.case = T, cph_dat$gender)] = "M"
cph_dat$ALP[cph_dat$ALP != "Elevated"] = "Normal"
#cph_dat$isFemale = as.numeric(cph_dat$isFemale == "F")
cph_dat$ALP <- relevel(as.factor(cph_dat$ALP), ref = "Normal")
cph_dat$treatment <- relevel(as.factor(cph_dat$treatment), ref = "SOC")
cph_dat$location <- relevel(as.factor(cph_dat$location), ref = "NPH")
cph_dat$gender <- relevel(as.factor(cph_dat$gender), ref = "M")
fit <- coxph(Surv(time, status) ~ location + ALP + age + gender + weight + treatment + subtype, data = cph_dat)
p1 <- ggforest(fit, data = cph_dat,fontsize = 0.7, noDigits = 2) + theme(text = element_text(size = 15))
#ggsave(plot = p1, filename = "~/Dog/ggforest_DFS.pdf", width = 6, height = 8)
cph_dat <- data.frame(time = cc1$OS_time,
subtype = cc1$primary_immune_subtype,
cytotoxicity_score = as.numeric(cyt_scores[common]),
status = cc1$OS_status,
location = c("NPH","PH")[as.numeric(ccc1$PH)+1],
ALP = cc1$ALP,
age = scale(as.numeric(cc1$age)),
gender = cc1$gender,
weight = scale(cc1$weight),
treatment = c("SOC","Sirolimus+SOC")[as.numeric(cc1$treatment == "Rapamycin")+1])
cph_dat$gender[grepl("female", ignore.case = T, cph_dat$gender)] = "F"
cph_dat$gender[grepl("male", ignore.case = T, cph_dat$gender)] = "M"
cph_dat$ALP[cph_dat$ALP != "Elevated"] = "Normal"
#cph_dat$isFemale = as.numeric(cph_dat$isFemale == "F")
cph_dat$ALP <- relevel(as.factor(cph_dat$ALP), ref = "Normal")
cph_dat$treatment <- relevel(as.factor(cph_dat$treatment), ref = "SOC")
cph_dat$location <- relevel(as.factor(cph_dat$location), ref = "NPH")
cph_dat$gender <- relevel(as.factor(cph_dat$gender), ref = "M")
fit <- coxph(Surv(time, status) ~ location + ALP + age + gender + weight + treatment + subtype, data = cph_dat)
p2 <-ggforest(fit, data = cph_dat,fontsize = 0.7, noDigits = 2) + theme(text = element_text(size = 15))
#ggsave(plot = p2, filename = "~/Dog/ggforest_OS.pdf", width = 6, height = 8)
fig <- ggarrange(p2,p1, ncol = 2, nrow = 1, labels = c("",""))
print(fig)
#ggsave(plot = fig, filename = "~/Dog/ggforest_cyt_figures.pdf",width = 12, height = 8)
```
#Plot TME characteristics of metastatic samples in long term vs short term survivors
```{r}
library(reshape)
cc1 <- clindat_all
rownames(cc1) <- as.character(clindat_all$`Patient ID`)
met_dd1 <- data.frame(surv_group = c("shorter survival","longer survival")[as.numeric(cc1[matched_cases$case[matched_cases$sample_type == "Metastatic"], "OS_time"] > median(cc1$OS_time[cc1$OS_status==1]))+1], os_status = as.numeric(cc1[matched_cases$case[matched_cases$sample_type == "Metastatic"], "OS_status"]), os_time = as.numeric(cc1[matched_cases$case[matched_cases$sample_type == "Metastatic"], "OS_time"]), met_subtype = matched_cases$immune_subtype[matched_cases$sample_type == "Metastatic"], sites = matched_cases$site2[matched_cases$sample_type == "Metastatic"], matched_cases[matched_cases$sample_type == "Metastatic",1:10])
met_dd1 <- met_dd1[!is.na(met_dd1$surv_group),]
ha <- columnAnnotation(
group = met_dd1$surv_group,
preservation = matched_cases$prep[matched_cases$sample_type == "Metastatic"],
platform = matched_cases$source[matched_cases$sample_type == "Metastatic"],
subtype = matched_cases$immune_subtype[matched_cases$sample_type == "Metastatic"],
col = list(
"preservation" = c("Frozen" = "#db6d00",
"FFPE" = "#924900"),
"platform" = c("Nanostring" = "#525252",