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secondaryAnalysis.R
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secondaryAnalysis.R
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library(tidyverse)
library(HDCytoData)
library(flowCore)
library(CATALYST)
library(SingleCellExperiment)
library(scater)
library(plyr)
setwd("../../cyTOF/merged/out/clustering_B1plusB2")
fcsFiles <- list.files(path = ".", pattern = ".fcs$", full = TRUE, ignore.case = TRUE)
fcsFiles <- fcsFiles[which(substr(fcsFiles,5,9) %in% lol)]
sceFiles <- mclapply(fcsFiles, function(x) {
fs <- read.flowSet(x,
transformation = FALSE, truncate_max_range = FALSE,
column.pattern = "Y89Di|Sm147Di|Nd146Di|Nd150Di|Nd143Di|Tm169Di|Pr141Di|Gd160Di|Bi209Di|Nd144Di|Lu175Di|Eu151Di|Bi209Di|Dy163Di|Dy164Di|Er167Di|Er168Di|Er170Di|Eu151Di|Eu153Di|Gd155Di|Gd156Di|Gd158Di|Gd160Di|Ho165Di|Lu175Di|Nd142Di|Nd143Di|Nd144Di|Nd145Di|Nd146Di|Nd150Di|Pr141Di|Sm147Di|Sm149Di|Sm152Di|Tb159Di|Tm169Di|Y89Di|Yb171Di|Yb172Di|Yb173Di|Yb174Di|Yb176Di", invert.pattern = FALSE
)
3>1.5
names <- rownames(fs@phenoData)
for (i in names) {
fs@frames[[i]]@exprs <- fs@frames[[i]]@exprs[
which(fs@frames[[i]]@exprs[, 27] > 0),
]
}
names <- rownames(fs@phenoData)
for (i in names) {
fs@frames[[i]]@exprs <- fs@frames[[i]]@exprs[
which(fs@frames[[i]]@exprs[, 1] > 1.5),
]
}
names <- rownames(fs@phenoData)
for (i in names) {
fs@frames[[i]]@exprs <- fs@frames[[i]]@exprs[
which(fs@frames[[i]]@exprs[, 6] < 1.5),
]
}
names <- rownames(fs@phenoData)
for (i in names) {
fs@frames[[i]]@exprs <- fs@frames[[i]]@exprs[sample
(nrow(fs@frames[[i]]@exprs), 80000), ]
}
write.flowSet(fs)
})
fcsFiles <- list.files(path = ".", pattern = ".*fcs$", full = TRUE, ignore.case = TRUE)
# nie wczytujemy pr141(cd49d i cd5 bo nie sa w obu batchach) i Er170 - CD3 (bo usunelismy wysokie wartosci, a potem zostaly 0-0,3 i przeskalowane na 0-1 bardzo mocno wplywaja na klastrowaniae, a nie powinny)
fs <- read.flowSet(fcsFiles,
transformation = FALSE, truncate_max_range = FALSE,
column.pattern = "Er166Di|Y89Di|Sm147Di|Nd146Di|Nd150Di|Nd143Di|Tm169Di|Gd160Di|Bi209Di|Nd144Di|Lu175Di|Eu151Di|Bi209Di|Dy163Di|Dy164Di|Er167Di|Er168Di|Er170Di|Eu151Di|Eu153Di|Gd155Di|Gd156Di|Gd158Di|Gd160Di|Ho165Di|Lu175Di|Nd143Di|Nd144Di|Nd145Di|Nd146Di|Nd150Di|Sm147Di|Sm149Di|Sm152Di|Tb159Di|Tm169Di|Y89Di|Yb171Di|Yb172Di|Yb173Di|Yb174Di|Yb176Di", invert.pattern = FALSE
)
panel <- pData(parameters(fs[[1]]))
panel[4,2] <- "166Er_CADM1"
md <- read.csv2("../../../clinical_data.csv", na.strings = c("", "N/A"), sep = "\t")
md <- md[, c(1, 2, 3, 5, 6)]
md <- md[which(md$ID %in% substr(fcsFiles,13,17)),] #wybieramy tylko pacjentow GBM
md$tumor_type <- md$Diagnosis
md[md$Diagnosis %in% c(
"Endometrium carcinoma BrM", "Squamous cell carcinoma BrM",
"Melanoma BrM", "NSCLC BrM"
), "tumor_type"] <- "BrM"
md[md$Diagnosis %in% c("Glioblastoma", "Glioblastoma recurrent"), "tumor_type"] <- "GBM"
md[md$Diagnosis %in% c(
"Anaplastic astrocytoma", "Anaplastic ependymoma", "Anaplastic oligodendroglioma",
"Diffuse astrocytoma"
), "tumor_type"] <- "Glioma IDHwt"
md$Sex <- factor(md$Sex, levels = c("m", "f"))
md$file_name <- substr(fcsFiles, 3, 30)
sce <- prepData(fs, panel, md,
cofactor = 5,
transform = TRUE,
panel_cols = list(channel = "name", antigen = "desc"),
md_cols = list(
file = "file_name", id = "ID",
factors = c("tumor_type", "Sex", "Age")
)
)
# zmieniamy wyniki >99percentyla na 99percentyl
for (i in colnames(macExpAsinh)) {
q99 <- quantile(macExpAsinh[, i], probs = 0.99)
macExpAsinh[(which(macExpAsinh[, i] > q99)), i] <- q99
}
macExpAsinh <- t(macExpAsinh)
colnames(macExpAsinh) <- NULL
dim(macExpAsinh) == dim(counts(sce))
colnames(macExpAsinh) == colnames(counts(sce))
rownames(macExpAsinh) == rownames(counts(sce))
assay(sce, "exprs") <- macExpAsinh
#### normalizacja 0-1 #####
macExp <- assay(sce, i = "exprs")
macExp <- t(macExp)
normalize <- function(x) {
return((x - min(x)) / (max(x) - min(x)))
}
for (x in 1:length(colnames(macExp))) {
macExp[, x] <- normalize(macExp[, x])
}
macExpAsinh <- macExp
colnames(macExpAsinh) <- names(sce)
#### koniec:normalizacja 0-1 #####
set.seed(1234)
sceCluster <- cluster(x = sce, features = rownames(sce), maxK = 20, seed = 1234)
#
set.seed(1)
sceClusterUMAP <- runDR(sceCluster, "UMAP", cells = 10000, features = rownames(sceCluster))
plotDR(sceClusterUMAP, "UMAP", color_by = "meta12", scale = FALSE)
markers <- rownames(sceClusterUMAP)
exp_plots <- mclapply(markers, function(i){
plotDR(sceClusterUMAP, "UMAP", color_by = i, scale = FALSE) +
ggtitle(i)+
theme_classic()+
theme( title = element_text(size=40),
axis.text = element_blank(), axis.ticks = element_blank(),
axis.title = element_text(hjust=0), legend.title = element_blank(),
legend.position = "right", legend.text=element_text(size=14),
axis.title.x=element_text(size=16),
axis.title.y=element_text(size=16))
},mc.cores=31)
library(ggpubr)
plotName <- paste("sceClusterUMAP",".png",sep="")
print(plotName)
png(plotName,
width=4800, height=2700)
print(ggarrange(plotlist = exp_plots, ncol =7, nrow=5))
dev.off()
macExpr <- t(assay(sceClusterUMAP, i="exprs"))
metadata <- sceClusterUMAP@colData
redDim <- reducedDim(sceClusterUMAP)
metadata <- cbind(metadata,redDim)
Tmac <- cbind(metadata,macExpr)
colnames(Tmac)[6] <- "UMAP1"
colnames(Tmac)[7] <- "UMAP2"
library(Rphenograph)
library(gridExtra)
library(ggpubr)
macExp <- Tmac[8:38]
macExp <- as.data.frame(macExp)
colnames(macExp) <- substr(colnames(macExp),2,50)
phenograph_out <- Rphenograph(macExp, k=70)
modularity(phenograph_out[[2]])
membership(phenograph_out[[2]])
macExp$phenograph_cluster <- factor(membership(phenograph_out[[2]]))
macExp <- cbind(Tmac$sample_id,Tmac$Sex,Tmac$tumor_type,Tmac$UMAP1,Tmac$UMAP2,macExp)
colnames(macExp)[[1]] <- "sample_id"
colnames(macExp)[[2]] <- "Sex"
colnames(macExp)[[3]] <- "tumor_type"
colnames(macExp)[[4]] <- "UMAP1"
colnames(macExp)[[5]] <- "UMAP2"
saveRDS(macExp,"myeloMacExpKmeans_minust141Pr_170EU.rds")
umapClusters <- ggplot(macExp, aes(x=UMAP1, y=UMAP2, col=phenograph_cluster)) + geom_point(size = 0.1) +
ggtitle("kMeans full 1 batch")+
theme_classic()+
guides(color = guide_legend(override.aes = list(size=20)))+
theme( title = element_text(size=40),
axis.text = element_blank(), axis.ticks = element_blank(),
axis.title = element_text(hjust=0), legend.title = element_blank(),
legend.position = "right", legend.text=element_text(size=30),
#legend.key.size=unit(30,"line"),
axis.title.x=element_text(size=16),
axis.title.y=element_text(size=16))
png("kMeansClusters.png",
width=1500, height=1500)
print(umapClusters)
dev.off()
##################### SZUKAMY ROZNIC POMIEDZY M/F ########################
library(tidyverse)
library(plyr)
library(ggplot2)
library(ggpubr)
library(viridis)
#CD56 - NK
#CD5 - T-cells
setwd("~/cyTOF/merged/out/clustering_B1plusB2")
macExp <- readRDS("B1plusB2ExprsMatrixesKmeans_minust141Pr.rds")
data <- macExp
metadata<-md
#patient occuring in both panels ZH683
#Glioblastoma patients
# ID Sex Diagnosis Myeloid.focused.panel.CyTOF
# 7 ZH678 m Glioblastoma 1
# 9 ZH720 m Glioblastoma 1
# 10 ZH736 f Glioblastoma 1
# 11 ZH746 m Glioblastoma 2
# 14 ZH761 m Glioblastoma 2
# 15 ZH784 m Glioblastoma recurrent 2
# 16 ZH791 m Glioblastoma 2
# 17 ZH794 f Glioblastoma 2
# 18 ZH802 f Glioblastoma 2
# 19 ZH810 f Glioblastoma 2
# 20 ZH816 f Glioblastoma 2
# 21 ZH818 m Glioblastoma 2
# 23 ZH813 f Glioblastoma 2
# 24 ZH961 f Glioblastoma recurrent 1
clustersUMAP <- ggplot(full, aes(x=UMAP1, y=UMAP2, color=phenograph_cluster))+
geom_jitter(size=0.7, alpha=0.5)+
geom_text(aes(label=phenograph_cluster), size=4, alpha=0.5, check_overlap=T, color="black")+
#facet_grid(.~day)+
guides(colour = guide_legend(override.aes = list(size=7)))
png(file = "clustersUMAP_minus141.png", width = 1500, height = 1500)
clustersUMAP
dev.off()
#expresion plots
theme_feature<-theme_classic()+
theme(axis.text = element_blank(), axis.ticks = element_blank(),
axis.title = element_text(hjust=0), legend.title = element_blank(),
legend.position = "right", legend.text=element_text(size=14),
title = element_text(size=40), axis.title.x=element_text(size=16),
axis.title.y=element_text(size=16))
YlOrRd<-colorRampPalette(c("Yellow","Orange", "Red"))
genes<-colnames(full)
graphs<-list()
graphs <- mclapply(7:37, function(i) {
max <- round(max(full[, genes[i]]), digits = 1)
min <- 0 # ceiling(min(panel_99[,genes[i]]))
plot <- ggplot(full, aes(x = UMAP1, y = UMAP2)) +
geom_jitter(size = 1, alpha = 0.5, aes_string(color = as.name(genes[i]))) +
labs(title = genes[i]) +
scale_color_gradientn(colors = (c(
viridis(50),
YlOrRd(50)
))) +
# breaks=c(min,max))+
# , labels=c("min", "max"))+
xlab("UMAP_1") +
ylab("UMAP_2") +
theme_feature +
theme(legend.text = element_text(size = 20), legend.position = "right")
# facet_grid(sex~day)
return(plot)
}, mc.cores = 31)
png(file = "expression_minus141.png", width = 4000, height = 2000)
ggarrange(plotlist = graphs, ncol = 8, nrow = 4, common.legend = F)
dev.off()
#annotate clusters
#ZH683_1
anno<-data.frame( "cluster"=c( 5, 11, 8, 1, 2, 14, 15, 9, 10, 3, 13),
"cell_annotation"=c("NK", "NK", "Mo", rep("Mphi", 5),rep("MG", 3)))
#ZH683_2
anno<-data.frame( "cluster"=c( 3, 9, 16, 10, 11, 13, 5,4,14, 8, 6, 2),
"cell_annotation"=c(rep("NK",3), "Mo", rep("Mphi", 5), rep("MG",3)))
a<-length(unique(patient$phenograph_cluster))
anno<-rbind(anno,
data.frame("cluster"=c(1:a)[!c(1:a) %in% anno$cluster],
"cell_annotation"=rep("UN", length(c(1:a)[!c(1:a) %in% anno$cluster]))))
patient$cell_annotation<-anno[match(patient$phenograph_cluster, anno$cluster),2]
table(patient$cell_annotation)
#veryfy annotation on a plot
png(paste("plots/Cell_annotation", patient_id), width=500, height=500)
ggplot(patient, aes(x=UMAP1, y=UMAP2, color=cell_annotation))+
geom_jitter(size=0.7, alpha=0.5)+
geom_text(aes(label=phenograph_cluster), size=4, alpha=0.5, check_overlap=T, color="black")+
guides(colour = guide_legend(override.aes = list(size=7)))+
theme_bw()
dev.off()
#save new patient list with annotated clusters
#patients_anno<-list()
a<-list(patient)
names(a)<-patient_id
patients_anno<-c(patients_anno, a)
saveRDS(patients_anno, "data/patients_anno.RDS")
#____________________________________________________________________________________
#append cell type distribution
group<-c("MG", "Mo", "Mphi")
patient<-patient[patient$cell_annotation %in% group,]
#cell_type_distribution<-list()
b<-table(patient$cell_annotation) %>% as.vector() %>% list
names(b)<-patient_id
cell_type_distribution<-c(cell_type_distribution, b)
saveRDS(cell_type_distribution, "data/cell_type_distribution.RDS")
colnames(cell_type_distribution)<-c("patient_id", group)
#____________________________________________________________________________________
#extract MHC level
#MHCII<-list()
c<-c(aggregate(patient[,"150Nd_HLA-DR"], list(patient$cell_annotation), mean)$x,
mean(patient[,"150Nd_HLA-DR"]))%>% list()
names(c)<-patient_id
MHCII<-c(MHCII, c)
saveRDS(MHCII, "data/MHCII.RDS")
colnames<-c(group, "total")
mikroglej <- c(11,2,1)
monocyty <- c(8,23)
makrofagi <- c(10,3,9,4,26)
females <- nmacExp[which(nmacExp$Sex == "f"),]
males <- nmacExp[which(nmacExp$Sex == "m"),]
malesMikro <- males[which(males$phenograph_cluster == 11 |males$phenograph_cluster == 2 |males$phenograph_cluster == 1),]
malesMakro <- males[which(males$phenograph_cluster == 10 |males$phenograph_cluster == 3 |males$phenograph_cluster == 9|males$phenograph_cluster == 26|males$phenograph_cluster == 4),]
malesMono <- males[which(males$phenograph_cluster == 8 |males$phenograph_cluster == 23),]
valuesPie <- c(dim(malesMikro)[[1]],dim(malesMakro)[[1]],dim(malesMono)[[1]])
labelsPie <- c("Mikroglej","Makrofagi","Monocyty")
pct <- round(valuesPie/sum(valuesPie)*100)
labelsPie <- paste(labelsPie,pct,"%")
pie(valuesPie, labels=labelsPie, main = "Male")
femalesMikro <- females[which(females$phenograph_cluster == 11 |females$phenograph_cluster == 2 |females$phenograph_cluster == 1),]
femalesMakro <- females[which(females$phenograph_cluster == 10 |females$phenograph_cluster == 3 |females$phenograph_cluster == 9|females$phenograph_cluster == 26|females$phenograph_cluster == 4),]
femalesMono <- females[which(females$phenograph_cluster == 8 |females$phenograph_cluster == 23),]
valuesPie <- c(dim(femalesMikro)[[1]],dim(femalesMakro)[[1]],dim(femalesMono)[[1]])
labelsPie <- c("Mikroglej","Makrofagi","Monocyty")
pct <- round(valuesPie/sum(valuesPie)*100)
labelsPie <- paste(labelsPie,pct,"%")
pie(valuesPie, labels=labelsPie, main = "Female")
#annotate clusters variant II
cell_types<-list(
NK=c(2, 12, 14, 17, 25), #CD56
Granulocytes=c(15,19), #CD66b
cDC=c(8), #CD1c
TAM=c(1,4,6, 11,22,23), #CCR2-, CD49d, CD45, CD68, CD14
Mo=c(3,20,21), #CCR2+, CD141
BAMs= c(5,7), #CD206, CD169
"Plasma cells"=c(18),
UN=c(9,10,13,16,20,24))
#annotate clusters variant III
cell_types<-list(
NK=c(2, 12, 14, 17, 25), #CD56
Granulocytes=c(15,19), #CD66b
cDC=c(8), #CD1c
"TAM HLA-DR low"=c(1, 4, 11),
"TAM HLA-DR high"=c(6,22,23), #CCR2-, CD49d, CD45, CD68, CD14
Mo=c(3,20,21), #CCR2+, CD141
BAMs= c(5,7), #CD206, CD169
"Plasma cells"=c(18),
UN=c(9,10,13,16,20,24))
male <- full[which(full$Sex=="m"),]
female <- full[which(full$Sex=="f"),]