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NSCLC_Pipeline3.R
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library(spatstat); library(Rfast2); library(data.table); library(Rfast)
library(dplyr); library(tidyr); library(reshape2); library(tibble); library(tidyverse)
library(ggplot2); library(umap); library(ggvoronoi); library(Rtsne); library(ComplexHeatmap)
library(ggforce); library(UpSetR); library(rstatix); library(dendextend)
library(tripack); library(igraph); library(RANN)
library(readxl); library(stringr); library(rjson); library(pracma)
library(survival); library(survminer)
library(ggh4x)
# working directory
current_path <- rstudioapi::getActiveDocumentContext()$path
setwd(dirname(current_path))
setwd('..')
source('./Codes/Function.R')
#---------------------- DATA INPUT ----------------#
# read single-cell data
NSCLCdata <- readRDS('./NSCLCdataset.rds')
# read patient data
patient_data <- read.csv('./NSCLC Clinical Data.csv')
# core demographic data
Core_demo <- read.csv('./Clinicopathology parameters.csv')
# polygon data
#--------------------------------------------------#
#
# PREREQUISITE DATA: INHERITED FROM PIPELINE 1
#-------------- Cell type fraction per image with hierarchical clustering ------------#
# Create table with celltype fractions
cur_df <- NSCLCdata %>%
# remove control cores
dplyr::filter(core %in% Core_demo$Core) %>%
group_by(core, Phenotype) %>%
dplyr::summarise(n=n()) %>%
group_by(core) %>%
mutate(fraction = n / sum(n)) %>%
reshape2::dcast(core ~ Phenotype, value.var = "fraction", fill=0)
matrixrownames <- cur_df$core
# now we create a matrix from the data and cluster the data based on the cell fractions
hm_dat = as.matrix(cur_df[,-1])
rownames(hm_dat) <- as.character(matrixrownames)
# calculate distance and then cluster images based on cluster fraction
dd <- dist((hm_dat), method = "euclidean")
hc <- hclust(dd, method = "ward.D2")
# see cluster tree
cl_tree <- cutree(hc, k = 3) %>%
data.frame() %>%
rownames_to_column() %>%
`colnames<-` (c('Core', 'Cluster')) %>%
merge(Core_demo, by = 'Core') %>%
dplyr::filter(Region == 'Central')
table_tree <- table(cl_tree[, c(2, 5)]) %>%
data.frame()
clus_dist <- reshape(table_tree, idvar = "PatientID", timevar = "Cluster", direction = "wide")
# method: find the maximum
group <- apply(clus_dist[, -1], 1, which.max)
clus_dist$group <- as.numeric(group)
patient_data_clus <- merge(clus_dist, patient_data, by = 'PatientID')
patient_data_clus <- patient_data_clus %>%
dplyr::filter(group != 2)
mean(patient_data_clus[patient_data_clus$group == 1,]$Time)
mean(patient_data_clus[patient_data_clus$group == 3,]$Time)
# CONSTRUCT SPATIAL TOPOLOGICAL GRAPH
#-------------- Cell type fraction per image with hierarchical clustering ------------#
# Create table for data storage
# index cell type
index_types <- unique(NSCLCdata$Phenotype)
# assortativity data frame
assort_df <- data.frame(matrix(nrow = 0, ncol = length(index_types) + 2))
colnames(assort_df) <- c(index_types, 'Group', 'Core')
assort_df2 <- data.frame(matrix(nrow = 0, ncol = 8))
colnames(assort_df2) <- c(unique(NSCLCdata$Phenotype), 'Group', 'Core')
# centrality coefficients data frame
degrees_all <- data.frame(matrix(nrow = 0, ncol = 0))
betweenness_all <- data.frame(matrix(nrow = 0, ncol = 0))
closeness_all <- data.frame(matrix(nrow = 0, ncol = 0))
transitivities_all <- data.frame(matrix(nrow = 0, ncol = 0))
colnames(NSCLCdata)[1] <- 'Core'
for(file in list.files('./polygonList')){
#file <- list.files('./polygonList')[4]
spName <- strsplit(file, '\\]|,') # regex to extract cores
core <- paste0(LETTERS[as.numeric(spName[[1]][2])], spName[[1]][3]) # pieces to cores
# cell position
CellPos <- NSCLCdata %>%
dplyr::filter(Core == core)
# cell boundary
CellBound = fread(paste0('./polygonList/', file)) %>%
data.frame() %>%
merge(CellPos, by = 'CellID')
tryCatch(
expr = {
# patient data
location <- Core_demo[Core_demo$Core == core, 'Region']
if(location == 'Central'){
PatientID <- Core_demo[Core_demo$Core == core, 'PatientID']
# group number
Group <- patient_data_clus[patient_data_clus$PatientID == PatientID, 'group']
#--------- Cell Map -----------#
# save image
#--------- Cell Map -----------#
p <- ggplot(CellBound) +
theme_void() +
geom_polygon(aes(PosX, PosY, group = CellID, fill = Phenotype)) +
scale_fill_manual(values = c('CD8' = '#f8f8f8', 'FoxP3' = '#33a02c', 'CD163' = '#f8f8f8', 'Other' = '#f8f8f8',
'Tumor' = '#e31b1c', 'FoxP3CD8' = '#f8f8f8')) +
theme(legend.position="none") +
#ylim(700, 1428) + # 670 - 1398 (Patient 16)
#xlim(1200, 1928) + # 830 - 1558 (Patient 16)
coord_fixed(ratio = 1)
p
#ggsave(p, file=paste0("./Figures/Spatial Clustering/Patient_", PatientID, '_', core, '.jpeg'), width = 8, height = 8, units = "in", dpi = 300)
#--------- Spatial Graph -------#
#-------------- USE FUNCTION HERE ---------------#
lists <- Network(CellPos[, c('CellXPos', 'CellYPos', 'Phenotype', 'ExprPhenotype', 'CellID')], 60)
Dual_EdgeList <- lists[[1]]
Dual_NodeList <- lists[[2]]
Dual_EdgeList$from <- Dual_NodeList[Dual_EdgeList$from, 'nodes']
Dual_EdgeList$to <- Dual_NodeList[Dual_EdgeList$to, 'nodes']
# split Dual_Edgelist to two part, part 1 contains connected nodes of same kinds
# ------ part 1 -----#
Dual_EdgeList_types <- data.frame(from = CellPos[match(Dual_EdgeList$from, CellPos$CellID), 'Phenotype'],
to = CellPos[match(Dual_EdgeList$to, CellPos$CellID), 'Phenotype'])
Dual_EdgeList_pt1 <- Dual_EdgeList[which(Dual_EdgeList_types$from == Dual_EdgeList_types$to), ]
Dual_EdgeList_pt1_types <- Dual_EdgeList_types[which(Dual_EdgeList_types$from == Dual_EdgeList_types$to), ]
# ------ part 2 -----#
Dual_EdgeList_pt2 <- Dual_EdgeList[which(Dual_EdgeList_types$from != Dual_EdgeList_types$to), ]
Dual_EdgeList_pt2_types <- Dual_EdgeList_types[which(Dual_EdgeList_types$from == Dual_EdgeList_types$to), ]
p <- ggplot() +
#theme_bw() +
theme_void() +
#geom_polygon(data = mask, aes(Y, X, group = cellLabelInImage, fill = immuneGroup)) +
geom_segment(aes(x = Dual_NodeList[match(Dual_EdgeList_pt1$from, Dual_NodeList$nodes), 2],
xend = Dual_NodeList[match(Dual_EdgeList_pt1$to, Dual_NodeList$nodes), 2],
y = Dual_NodeList[match(Dual_EdgeList_pt1$from, Dual_NodeList$nodes), 3],
yend = Dual_NodeList[match(Dual_EdgeList_pt1$to, Dual_NodeList$nodes), 3],
color = Dual_EdgeList_pt1_types$from), size = 0.4) +
geom_segment(aes(x = Dual_NodeList[match(Dual_EdgeList_pt2$from, Dual_NodeList$nodes), 2],
xend = Dual_NodeList[match(Dual_EdgeList_pt2$to, Dual_NodeList$nodes), 2],
y = Dual_NodeList[match(Dual_EdgeList_pt2$from, Dual_NodeList$nodes), 3],
yend = Dual_NodeList[match(Dual_EdgeList_pt2$to, Dual_NodeList$nodes), 3],
color = '#b4b3b3'), size = 0.4) +
geom_point(data = CellPos, aes(CellXPos, CellYPos, color = Phenotype), size = 1) +
scale_color_manual(values = c('CD8' = '#a6cee3', 'FoxP3' = '#33a02c', 'CD163' = '#cab2d6', 'Other' = '#1f78b4',
'Tumor' = '#e31b1c', 'FoxP3CD8' = '#fdbf6f')) + #f8f8f8
theme(legend.position = 'none') +
#ylim(670, 1398) +
#xlim(830, 1558) +
#ylim(700, 1428) + # 670 - 1398 (Patient 16)
#xlim(1200, 1928) + # 830 - 1558 (Patient 16)
coord_fixed(ratio = 1)
#p
#ggsave(p, file=paste0("./Figures/Spatial_Clustering/Patient_", PatientID, '_', core, '.jpeg'), width = 8, height = 8, units = "in", dpi = 300)
# ------------------ #
# iGraph #
# ------------------ #
#---------------- Compute centrality coefficients -------------#
ig <- graph_from_data_frame(vertices = Dual_NodeList, d = Dual_EdgeList, directed = FALSE)
ig <- set_vertex_attr(ig, 'cell_type', value = CellPos$Phenotype)
for(ctype in c('CD8', 'CD163', 'FoxP3', 'Tumor')){
#ctype <- 'CD163'
degrees <- igraph::degree(ig, v = V(ig), normalized = TRUE)%>%
as.data.frame() %>%
cbind(CellPos$Phenotype) %>%
`colnames<-` (c('degrees', 'Phenotype')) %>%
dplyr::filter(Phenotype == ctype)
closenesses <- closeness(ig, vids = V(ig), weights = NULL, normalized = TRUE)%>%
as.data.frame() %>%
cbind(CellPos$Phenotype) %>%
`colnames<-` (c('closenesses', 'Phenotype')) %>%
dplyr::filter(Phenotype == ctype)
betweenesses <- betweenness(ig, v = V(ig), normalized = TRUE)%>%
as.data.frame() %>%
cbind(CellPos$Phenotype) %>%
`colnames<-` (c('betweennesses', 'Phenotype')) %>%
dplyr::filter(Phenotype == ctype)
#transitivities <- transitivity(ig, type = 'localundirected', vids = V(ig)[V(ig)$`cell_type` == ctype])
transitivities <- transitivity(ig, type = 'localundirected', vids = V(ig)) %>%
as.data.frame() %>%
cbind(CellPos$Phenotype) %>%
`colnames<-` (c('transitivities', 'Phenotype')) %>%
dplyr::filter(Phenotype == ctype)
degrees_all <- rbind.data.frame(degrees_all, cbind.data.frame(degrees, core, Group, PatientID))
closeness_all <- rbind.data.frame(closeness_all, cbind.data.frame(closenesses, core, Group, PatientID))
betweenness_all <- rbind.data.frame(betweenness_all, cbind.data.frame(betweenesses, core, Group, PatientID))
transitivities_all <- rbind.data.frame(transitivities_all, cbind.data.frame(transitivities, core, Group, PatientID))
}
# construct iGraph object
ig <- graph_from_data_frame(vertices = Dual_NodeList, d = Dual_EdgeList, directed = FALSE)
# assign vertex attributes
# For this part, we set the objective cell type as type 1 and all other cell types as type 2
assort_vec <- data.frame(matrix(nrow = 0, ncol = length(index_types)))
colnames(assort_vec) <- index_types
for(type1 in unique(CellPos$Phenotype)){
type1_id <- which(CellPos$Phenotype == type1)
vertex_attr <- rep(2, nrow(CellPos))
vertex_attr[type1_id] <- 1
ig <- set_vertex_attr(ig, 'cell_type', value = vertex_attr)
print(assortativity_nominal(ig, types = V(ig)$cell_type))
assort_vec[1, type1] <- assortativity_nominal(ig, types = V(ig)$cell_type)
}
assort_vec$Group <- Group
assort_vec$Core <- core
assort_df <- rbind.data.frame(assort_df, assort_vec)
#lt_assort_df <- assort_df[assort_df$Group == 'long-term',]
#st_assort_df <- assort_df[assort_df$Group == 'short-term',]
# the index of the second minimum distance
#index2 = apply(dist_mutual, 1, function(x) names(sort(x)[2]))
# the cell type pair of index cell and the top closest cell
#cell_pair <- data.frame(index_type = CellPos$Phenotype, CellPos[index2, 'Phenotype'])
#cell_pair_Mat <- as.data.frame.matrix(table(cell_pair))
}
},
error = function(e){
# (Optional)
# Do this if an error is caught...
}
)
}
#--------------------------------------------------------------------------#
#-------------- Visualizations for network measurements -------------------#
#--------------------------------------------------------------------------#
library(hrbrthemes)
degrees_all_vis <- closeness_all %>%
dplyr::filter(ctype != 'FoxP3CD8') %>%
dplyr::filter(ctype != 'Other')
pd = position_dodge(width = 0.75)
signif_pairs <- degrees_all_vis %>%
select(-c('core', 'PatientID')) %>%
melt(id.vars = c('Phenotype', 'Group')) #%>%
#select(-'core')
stat.test <- signif_pairs %>%
group_by(Phenotype) %>%
wilcox_test(value ~ Group) %>%
add_significance() %>%
add_xy_position(x = "Phenotype")
stat.test$y.position <- log(stat.test$y.position) - 2.5 # - closeness
p <- ggplot(degrees_all_vis, aes(x= Phenotype, y= log(closenesses + 0.000001), colour=as.factor(Group))) +
stat_boxplot(geom = "errorbar", width = 0.25, position=pd) +
scale_color_manual(values = c('1' = '#0a377b', '3' = "#d63434")) +
geom_boxplot(outlier.color = NA) +
#ylim(-9, -4) + # degree centrality
#ylim(2, 20) + # betweenness centrality
#ylim(-2, 1) + # transitivity centrality
ylim(-5, -2) + # transitivity centrality
theme_classic() +
stat_pvalue_manual(stat.test, size = 5) + rremove("x.title") + rremove('y.title') +
theme(strip.text = element_text(size = 15)) +
#ylim(800, 1300) +
font("xy.text", size = 20) +
font("xy.text", size = 20) +
rremove('legend')
p
ggsave(p, file=paste0("./Figures/closenesses_centrality.jpeg"), width = 4, height = 5, units = "in", dpi = 300)
#------------------- Heatmap similar to Won et al ----------------------------#
nn_DF_LT <- data.frame(matrix(nrow = 4, ncol = 4))
nn_DF_LT[is.na(nn_DF_LT)] <- 0
colnames(nn_DF_LT) <- c('CD163', 'CD8', 'FoxP3', 'Tumor')
rownames(nn_DF_LT) <- c('CD163', 'CD8', 'FoxP3', 'Tumor')
nn_DF_ST <- nn_DF_LT
for(file in list.files('./polygonList')){
#file <- list.files('./polygonList')[4]
spName <- strsplit(file, '\\]|,') # regex to extract cores
core <- paste0(LETTERS[as.numeric(spName[[1]][2])], spName[[1]][3]) # pieces to cores
# cell position
CellPos <- NSCLCdata %>%
dplyr::filter(Core == core) %>%
dplyr::filter(Phenotype != 'Other') %>%
dplyr::filter(Phenotype != 'FoxP3CD8')
tryCatch(
expr = {
# patient data
location <- Core_demo[Core_demo$Core == core, 'Region']
if(location == 'Central'){
print(core)
PatientID <- Core_demo[Core_demo$Core == core, 'PatientID']
# group number
Group <- patient_data_clus[patient_data_clus$PatientID == PatientID, 'group']
res <- nn2(CellPos[, c('CellXPos', 'CellYPos')], query = CellPos[, c('CellXPos', 'CellYPos')],
k = 2, treetype = 'kd', searchtype = 'priority')[['nn.idx']] %>%
data.frame() %>%
`colnames<-` (c('index', 'nn1'))
# find the nearest neighbors of type i
res$index <- CellPos[res$index, 'Phenotype']
res$nn1 <- CellPos[res$nn1, 'Phenotype']
#res$nn2 <- CellPos[res$nn2, 'Phenotype']
#res$nn3 <- CellPos[res$nn3, 'Phenotype']
res_mlt <- melt(res, id.vars=c("index"))
nn_DF <- table(res_mlt[, c('index', 'value')]) %>%
as.data.frame.matrix()
for(rid in 1:4){
rn <- nn_DF[rid,] %>%
rownames()
# total number
tn <- CellPos %>%
dplyr::filter(Phenotype == rn) %>%
nrow()
# normalization
nn_DF[rid,] <- nn_DF[rid,] / tn
}
if(Group == 1){
nn_DF_LT <- nn_DF_LT + nn_DF
}
if(Group == 3){
nn_DF_ST <- nn_DF_ST + nn_DF
}
}
},
error = function(e){
# (Optional)
# Do this if an error is caught...
}
)
}
heatmap(as.matrix(nn_DF_ST - nn_DF_LT))
heatmap(as.matrix(nn_DF_LT))
#------------------- Mimimum spanning tree ----------------------------#
miniDist_DF_LT <- data.frame(matrix(nrow = 4, ncol = 4))
miniDist_DF_LT[is.na(miniDist_DF_LT)] <- 0
colnames(miniDist_DF_LT) <- c('CD163', 'CD8', 'FoxP3', 'Tumor')
rownames(miniDist_DF_LT) <- c('CD163', 'CD8', 'FoxP3', 'Tumor')
miniDist_DF_all <- data.frame(matrix(nrow = 0, ncol = 0))
res_all <- data.frame(matrix(nrow = 0, ncol = 0))
miniDist_DF_ST <- miniDist_DF_LT
for(file in list.files('./polygonList')){
#file <- list.files('./polygonList')[1]
spName <- strsplit(file, '\\]|,') # regex to extract cores
core <- paste0(LETTERS[as.numeric(spName[[1]][2])], spName[[1]][3]) # pieces to cores
# cell position
CellPos <- NSCLCdata %>%
dplyr::filter(Core == core) %>%
dplyr::filter(Phenotype != 'Other') %>%
dplyr::filter(Phenotype != 'FoxP3CD8')%>%
dplyr::filter(ExprPhenotype != '68') %>%
dplyr::filter(!(Phenotype == 'Tumor' & ExprPhenotype == '64')) %>%
dplyr::filter(!(Phenotype == 'CD163' & ExprPhenotype == '64'))
#
CellPos$TruePheno <- as.factor(paste0(CellPos$Phenotype, '_', CellPos$ExprPhenotype)) %>%
as.numeric
unique(CellPos$TruePheno)
tryCatch(
expr = {
# patient data
location <- Core_demo[Core_demo$Core == core, 'Region']
if(location == 'Central'){
print(core)
PatientID <- Core_demo[Core_demo$Core == core, 'PatientID']
# group number
Group <- patient_data_clus[patient_data_clus$PatientID == PatientID, 'group']
res <- nn2(CellPos[, c('CellXPos', 'CellYPos')], query = CellPos[, c('CellXPos', 'CellYPos')],
k = 2, treetype = 'kd', searchtype = 'priority')[['nn.idx']] %>%
data.frame() %>%
`colnames<-` (c('index', 'nn1'))
res$dist <- nn2(CellPos[, c('CellXPos', 'CellYPos')], query = CellPos[, c('CellXPos', 'CellYPos')],
k = 2, treetype = 'kd', searchtype = 'priority')[['nn.dists']][,2]
# find the nearest neighbors of type i
res$index <- CellPos[res$index, 'TruePheno']
res$nn1 <- CellPos[res$nn1, 'TruePheno']
res$combination <- paste0(res$index, '_', res$nn1)
res_aggregated <- aggregate(res, list(res$combination), FUN = 'mean')
res_aggregated$index <- strsplit(res_aggregated[, 'Group.1'], '_') %>%
sapply("[[",1)
res_aggregated$nn1 <- strsplit(res_aggregated[, 'Group.1'], '_') %>%
sapply("[[",2)
colnames(res_aggregated)[1] <- 'combination'
res_all <- rbind.data.frame(res_all, cbind.data.frame(res_aggregated[, 1:4], Group, core))
}
},
error = function(e){
# (Optional)
# Do this if an error is caught...
}
)
}
res_aggregated <- aggregate(res_all, list(res_all$combination, res_all$Group), FUN = 'mean')
res_aggregated[res_aggregated$Group == 1, 'index']<- strsplit(res_aggregated[res_aggregated$Group == 1, 'Group.1'], '_') %>%
sapply("[[",1)
res_aggregated[res_aggregated$Group == 1, 'nn1']<- strsplit(res_aggregated[res_aggregated$Group == 1, 'Group.1'], '_') %>%
sapply("[[",2)
res_aggregated[res_aggregated$Group == 3, 'index']<- strsplit(res_aggregated[res_aggregated$Group == 3, 'Group.1'], '_') %>%
sapply("[[",1)
res_aggregated[res_aggregated$Group == 3, 'nn1']<- strsplit(res_aggregated[res_aggregated$Group == 3, 'Group.1'], '_') %>%
sapply("[[",2)
res_aggregated_LT <- res_aggregated[res_aggregated$Group == 1,]
res_aggregated_LT <- xtabs(dist ~index + nn1, res_aggregated_LT)
res_aggregated_ST <- res_aggregated[res_aggregated$Group == 3,]
res_aggregated_ST <- xtabs(dist ~index + nn1, res_aggregated_ST)
#Mutual.dist <- data.frame(matrix(nrow = 0, ncol = 0))
cols <- c('#cab2d6', '#e31b1c',
'#cab2d6', '#a6cee3',
'#a6cee3', '#a6cee3', '#33a02c',
'#33a02c', '#33a02c', '#e31b1c')
pdf("Figures/network_mindist.pdf",width=7,height=5)
set.seed(4)
g<-graph.adjacency(-res_aggregated_ST,weighted=T)
g_mst<-mst(g)
network5<-as_adjacency_matrix(g_mst)
network5<-graph_from_adjacency_matrix(network5, mode="undirected", diag=F)
plot.igraph(network5,
vertex.color=cols,
vertex.size=12,
edge.arrow.size = 0.5,
edge.arrow.width = 1,
vertex.label.dist=0,
vertex.label.family="sans", #sans = Arial, serif= Times
vertex.label.font=2,
vertex.label.cex=0.75,
vertex.label.color="black",
vertex.frame.color="transparent")
title("short-term survivor",cex.main= 1,col.main="black")
g<-graph.adjacency(-res_aggregated_LT,weighted=T)
g_mst<-mst(g)
network5<-as_adjacency_matrix(g_mst)
network5<-graph_from_adjacency_matrix(network5, mode="undirected", diag=F)
plot.igraph(network5,
vertex.color=cols,
vertex.size=12,
edge.arrow.size = 0.5,
edge.arrow.width = 1,
vertex.label.dist=0,
vertex.label.family="sans", #sans = Arial, serif= Times
vertex.label.font=2,
vertex.label.cex=0.75,
vertex.label.color="black",
vertex.frame.color="transparent")
title("long-term survivor",cex.main=1,col.main="black")
dev.off()
#cols <- c('CD8' = '#a6cee3', 'FoxP3' = '#33a02c', 'CD163' = '#cab2d6','Tumor' = '#e31b1c')
#---------------- pairwise Gcross function ------------------#
group1_all <- data.frame(matrix(nrow = 0, ncol = 0))
group2_all <- data.frame(matrix(nrow = 0, ncol = 0))
group3_all <- data.frame(matrix(nrow = 0, ncol = 0))
Mutual.dist <- data.frame(matrix(nrow = 0, ncol = 0))
for(file in list.files('./boundary')) {
#file <- list.files('./polygonList')[80] # This is M9
spName <- strsplit(file, '\\]|,') # regex to extract cores
Sample <- paste0(LETTERS[as.numeric(spName[[1]][2])], spName[[1]][3]) # pieces to cores
#ID <- 2089
Location <- Core_demo[Core_demo$Core == Sample, 'Region']
PatientID <- Core_demo[Core_demo$Core == Sample, 'PatientID']
Group <- patient_data_clus[patient_data_clus$PatientID == PatientID, 'group']
coreDat <- NSCLCdata %>%
filter(Core == Sample)
# ROI data
#ROI <- 'ROI03'
# CD163 cells coords
posCD163 <- coreDat %>%
filter(Phenotype == 'CD163')
# FoxP3 cells coords
posFoxP3 <- coreDat %>%
filter(Phenotype == 'FoxP3')
# CD8 cells coords
posCD8 <- coreDat %>%
filter(Phenotype == 'CD8')
# Tumor cell coords
posTumor <- coreDat %>%
filter(Phenotype == 'Tumor')
# define region to compute Kcross function
# Kcross functions
# read boundaries
tisse_files <- list.files(paste0('./QuPath/Tissue_Boundary/', Sample))
tryCatch(
expr = {
if(Location == 'Central'){
Tissue_all <- data.frame(matrix(nrow = 0, ncol = 0))
for(piece in tisse_files){
Tissue <- fromJSON(file = paste0('./QuPath/Tissue_Boundary/', Sample, '/', piece))%>%
with(geometry) %>%
with(coordinates) %>%
`[[`(1) %>%
as.data.frame() %>%
t() %>%
as.data.frame()
flag <- clockwise(data.frame(x = Tissue$V1, y = Tissue$V2))
# if clockwise, counter-clockwise it
if(flag){
Tissue$V1 <- rev(Tissue$V1)
Tissue$V2 <- rev(Tissue$V2)
}
Tissue_all <- rbind(Tissue_all, cbind(Tissue, piece))
}
colnames(Tissue_all) <- c('x', 'y', 'group')
#ggplot() +
# geom_polygon(aes(Tissue_all[,1], Tissue_all[,2], group = Tissue_all[,3]), fill = NA, color = 'black') +
# geom_point(data = coreDat, aes(CellXPos, CellYPos))
# CD8 + FoxP3
list1 <- bivarAnalysis.Kcross('posCD8', 'posFoxP3', Region = list(Tissue_all), neigbor_thresh = 60, step_thresh = 1)
list1_subgroups1 <- split(list1[[1]], list1[[1]]$interval)
list1_subgroups1_g1 <- list1_subgroups1$`1` %>%
mutate(category = 'CD8-FoxP3') %>%
mutate(Group = Group)
list1_subgroups1_g2 <- list1_subgroups1$`2` %>%
mutate(category = 'CD8-FoxP3') %>%
mutate(Group = Group)
list1_subgroups1_g3 <- list1_subgroups1$`3` %>%
mutate(category = 'CD8-FoxP3') %>%
mutate(Group = Group)
list1_subgroups2 <- split(list1[[2]], list1[[2]]$interval)
list1_subgroups2_g1 <- list1_subgroups2$`1` %>%
mutate(category = 'FoxP3-CD8') %>%
mutate(Group = Group)
list1_subgroups2_g2 <- list1_subgroups2$`2` %>%
mutate(category = 'FoxP3-CD8') %>%
mutate(Group = Group)
list1_subgroups2_g3 <- list1_subgroups2$`3` %>%
mutate(category = 'FoxP3-CD8') %>%
mutate(Group = Group)
list1_AUCs <- bivarAnalysis.Kcross_AUC('posCD8', 'posFoxP3', Region = list(Tissue_all), neigbor_thresh = 60)
# CD8 + CD163
list2 <- bivarAnalysis.Kcross('posCD8', 'posCD163', Region = list(Tissue_all), neigbor_thresh = 60, step_thresh = 1)
list2_subgroups1 <- split(list2[[1]], list2[[1]]$interval)
list2_subgroups1_g1 <- list2_subgroups1$`1` %>%
mutate(category = 'CD8-CD163') %>%
mutate(Group = Group)
list2_subgroups1_g2 <- list2_subgroups1$`2` %>%
mutate(category = 'CD8-CD163') %>%
mutate(Group = Group)
list2_subgroups1_g3 <- list2_subgroups1$`3` %>%
mutate(category = 'CD8-CD163') %>%
mutate(Group = Group)
list2_subgroups2 <- split(list2[[2]], list2[[2]]$interval)
list2_subgroups2_g1 <- list2_subgroups2$`1` %>%
mutate(category = 'CD163-CD8') %>%
mutate(Group = Group)
list2_subgroups2_g2 <- list2_subgroups2$`2` %>%
mutate(category = 'CD163-CD8') %>%
mutate(Group = Group)
list2_subgroups2_g3 <- list2_subgroups2$`3` %>%
mutate(category = 'CD163-CD8') %>%
mutate(Group = Group)
list2_AUCs <- bivarAnalysis.Kcross_AUC('posCD8', 'posCD163', Region = list(Tissue_all), neigbor_thresh = 60)
# CD8 + Tumor
list3 <- bivarAnalysis.Kcross('posCD8', 'posTumor', Region = list(Tissue_all), neigbor_thresh = 60, step_thresh = 1)
list3_subgroups1 <- split(list3[[1]], list3[[1]]$interval)
list3_subgroups1_g1 <- list3_subgroups1$`1` %>%
mutate(category = 'CD8-Tumor') %>%
mutate(Group = Group)
list3_subgroups1_g2 <- list3_subgroups1$`2` %>%
mutate(category = 'CD8-Tumor') %>%
mutate(Group = Group)
list3_subgroups1_g3 <- list3_subgroups1$`3` %>%
mutate(category = 'CD8-Tumor') %>%
mutate(Group = Group)
list3_subgroups2 <- split(list3[[2]], list3[[2]]$interval)
list3_subgroups2_g1 <- list3_subgroups2$`1` %>%
mutate(category = 'Tumor-CD8') %>%
mutate(Group = Group)
list3_subgroups2_g2 <- list3_subgroups2$`2` %>%
mutate(category = 'Tumor-CD8') %>%
mutate(Group = Group)
list3_subgroups2_g3 <- list3_subgroups2$`3` %>%
mutate(category = 'Tumor-CD8') %>%
mutate(Group = Group)
list3_AUCs <- bivarAnalysis.Kcross_AUC('posCD8', 'posTumor', Region = list(Tissue_all), neigbor_thresh = 60)
# FoxP3 + CD163
list4 <- bivarAnalysis.Kcross('posFoxP3', 'posCD163', Region = list(Tissue_all), neigbor_thresh = 60, step_thresh = 1)
list4_subgroups1 <- split(list4[[1]], list4[[1]]$interval)
list4_subgroups1_g1 <- list4_subgroups1$`1` %>%
mutate(category = 'FoxP3-CD163') %>%
mutate(Group = Group)
list4_subgroups1_g2 <- list4_subgroups1$`2` %>%
mutate(category = 'FoxP3-CD163') %>%
mutate(Group = Group)
list4_subgroups1_g3 <- list4_subgroups1$`3` %>%
mutate(category = 'FoxP3-CD163') %>%
mutate(Group = Group)
list4_subgroups2 <- split(list4[[2]], list4[[2]]$interval)
list4_subgroups2_g1 <- list4_subgroups2$`1` %>%
mutate(category = 'CD163-FoxP3') %>%
mutate(Group = Group)
list4_subgroups2_g2 <- list4_subgroups2$`2` %>%
mutate(category = 'CD163-FoxP3') %>%
mutate(Group = Group)
list4_subgroups2_g3 <- list4_subgroups2$`3` %>%
mutate(category = 'CD163-FoxP3') %>%
mutate(Group = Group)
list4_AUCs <- bivarAnalysis.Kcross_AUC('posFoxP3', 'posCD163', Region = list(Tissue_all), neigbor_thresh = 60)
# FoxP3 + Tumor
list5 <- bivarAnalysis.Kcross('posFoxP3', 'posTumor', Region = list(Tissue_all), neigbor_thresh = 60, step_thresh = 1)
list5_subgroups1 <- split(list5[[1]], list5[[1]]$interval)
list5_subgroups1_g1 <- list5_subgroups1$`1` %>%
mutate(category = 'FoxP3-Tumor') %>%
mutate(Group = Group)
list5_subgroups1_g2 <- list5_subgroups1$`2` %>%
mutate(category = 'FoxP3-Tumor') %>%
mutate(Group = Group)
list5_subgroups1_g3 <- list5_subgroups1$`3` %>%
mutate(category = 'FoxP3-Tumor') %>%
mutate(Group = Group)
list5_subgroups2 <- split(list5[[2]], list5[[2]]$interval)
list5_subgroups2_g1 <- list5_subgroups2$`1` %>%
mutate(category = 'Tumor-FoxP3') %>%
mutate(Group = Group)
list5_subgroups2_g2 <- list5_subgroups2$`2` %>%
mutate(category = 'Tumor-FoxP3') %>%
mutate(Group = Group)
list5_subgroups2_g3 <- list5_subgroups2$`3` %>%
mutate(category = 'Tumor-FoxP3') %>%
mutate(Group = Group)
list5_AUCs <- bivarAnalysis.Kcross_AUC('posFoxP3', 'posTumor', Region = list(Tissue_all), neigbor_thresh = 60)
# Tumor + CD163
list6 <- bivarAnalysis.Kcross('posTumor', 'posCD163', Region = list(Tissue_all), neigbor_thresh = 60, step_thresh = 1)
list6_subgroups1 <- split(list6[[1]], list6[[1]]$interval)
list6_subgroups1_g1 <- list1_subgroups1$`1` %>%
mutate(category = 'Tumor-CD163') %>%
mutate(Group = Group)
list6_subgroups1_g2 <- list1_subgroups1$`2` %>%
mutate(category = 'Tumor-CD163') %>%
mutate(Group = Group)
list6_subgroups1_g3 <- list1_subgroups1$`3` %>%
mutate(category = 'Tumor-CD163') %>%
mutate(Group = Group)
list6_subgroups2 <- split(list6[[2]], list6[[2]]$interval)
list6_subgroups2_g1 <- list1_subgroups2$`1` %>%
mutate(category = 'CD163-Tumor') %>%
mutate(Group = Group)
list6_subgroups2_g2 <- list1_subgroups2$`2` %>%
mutate(category = 'CD163-Tumor') %>%
mutate(Group = Group)
list6_subgroups2_g3 <- list1_subgroups2$`3` %>%
mutate(category = 'CD163-Tumor') %>%
mutate(Group = Group)
list6_AUCs <- bivarAnalysis.Kcross_AUC('posTumor', 'posCD163', Region = list(Tissue_all), neigbor_thresh = 60)
#------------compute AUC -----------#
group1_all <- rbind.data.frame(group1_all, list1_subgroups1_g1, list1_subgroups2_g1, list2_subgroups1_g1, list2_subgroups2_g1, list3_subgroups1_g1,
list3_subgroups2_g1, list4_subgroups1_g1, list4_subgroups2_g1, list5_subgroups1_g1, list5_subgroups2_g1,
list6_subgroups1_g1, list6_subgroups2_g1)
group2_all <- rbind.data.frame(group2_all, list1_subgroups1_g2, list1_subgroups2_g2, list2_subgroups1_g2, list2_subgroups2_g2, list3_subgroups1_g2,
list3_subgroups2_g2, list4_subgroups1_g2, list4_subgroups2_g2, list5_subgroups1_g2, list5_subgroups2_g2,
list6_subgroups1_g2, list6_subgroups2_g2)
group3_all <- rbind.data.frame(group3_all, list1_subgroups1_g3, list1_subgroups2_g3, list2_subgroups1_g3, list2_subgroups2_g3, list3_subgroups1_g3,
list3_subgroups2_g3, list4_subgroups1_g3, list4_subgroups2_g3, list5_subgroups1_g3, list5_subgroups2_g3,
list6_subgroups1_g3, list6_subgroups2_g3)
Mutual.dist <- rbind.data.frame(Mutual.dist, cbind.data.frame(Sample, list1_AUCs[[1]], list1_AUCs[[2]], list2_AUCs[[1]], list2_AUCs[[2]], list3_AUCs[[1]], list3_AUCs[[2]],
list4_AUCs[[1]], list4_AUCs[[2]], list5_AUCs[[1]], list5_AUCs[[2]], list6_AUCs[[1]], list6_AUCs[[2]], Sample, PatientID, Group))
print(Sample)
}
},
error = function(e){
# (Optional)
# Do this if an error is caught...
}
)
}
#------------------ Voronoi Tesselation -------------------#
colnames(Mutual.dist)[1] <- 'core'
newdata <- Mutual.dist[order(-Mutual.dist[,8]),]
test <- merge(newdata, cur_df, by = 'core')
cellPos <- NSCLCdata %>%
filter(Core == 'E6') %>%
#filter(Phenotype != 'Other') %>%
select('CellXPos', 'CellYPos', 'Phenotype', 'ExprPhenotype', 'CellID') #%>%
tisse_files <- list.files(paste0('./QuPath/Tissue_Boundary/E6'))
Tissue_all <- data.frame(matrix(nrow = 0, ncol = 0))
for(piece in tisse_files){
Tissue <- fromJSON(file = paste0('./QuPath/Tissue_Boundary/E6', '/', piece))%>%
with(geometry) %>%
with(coordinates) %>%
`[[`(1) %>%
as.data.frame() %>%
t() %>%
as.data.frame()
flag <- clockwise(data.frame(x = Tissue$V1, y = Tissue$V2))
# if clockwise, counter-clockwise it
if(flag){
Tissue$V1 <- rev(Tissue$V1)
Tissue$V2 <- rev(Tissue$V2)
}
Tissue_all <- rbind(Tissue_all, cbind(Tissue, piece))
}
p <- ggplot(cellPos, aes(CellXPos, CellYPos, fill = Phenotype)) +
geom_voronoi(outline = Tissue_all[,1:2],
color = 1, size = 0.1) +
scale_fill_manual(values = c('CD8' = '#f8f8f8', 'FoxP3' = '#33a02c', 'CD163' = '#cab2d6', 'Other' = '#f8f8f8',
'Tumor' = '#f8f8f8', 'FoxP3CD8' = '#f8f8f8')) + #f8f8f8
#scale_fill_gradient(low = "#B9DDF1",
# high = "#2A5783",
# guide = FALSE) +
theme_void() +
theme(legend.position = 'none') +
coord_fixed()
p
ggsave(p, file=paste0("./Figures/E6_CD163_FoxP3.jpeg"), width = 6, height = 4, units = "in", dpi = 300)
# statistical test to find significant pairs
p_all <- c()
for(id in 2:13){
id <- 10
g1 <- Mutual.dist[Mutual.dist$Group == 1, id]
g2 <- Mutual.dist[Mutual.dist$Group == 3, id]
mean(g1)
mean(g2)
test <- wilcox.test(g1, g2)
p_all <- c(p_all, test$p.value)
}
p.adjust(p_all)
#---------- FoxP3 -> CD163 ---------#
signif_pairs <- Mutual.dist[, c(1,8:10, 13, 15:16)]
colnames(signif_pairs)[2:4] <- c('FoxP3 -> CD163', 'CD163 -> FoxP3', 'FoxP3 -> Tumor')
signif_pairs <- signif_pairs %>%
select(-'PatientID') %>%
melt(id.vars = c('core', 'Group')) %>%
select(-'core')
stat.test <- signif_pairs %>%
group_by(variable) %>%
wilcox_test(value ~ Group) %>%
add_significance()
stat.test