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Wanda Brown
WandaB/
LymphomaFLserial
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[4ff317]: / Figures / Heatmap_Figure5C.R

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################################################

#  File Name:Heatmap_Figure5C.R

#  Author: Baoyan Bai





#################################################



###plot heatmap for de-histone genes identified with the new filter (Figure 5C)

###load required libraries

###expression data: output from CIBERSORTx





library(pheatmap)

library(dplyr)

library(plyr)

library(RColorBrewer)

setwd("~/Desktop/FL/Ankush_CIBERSORT")



###import expression data and remove samples not relative

match_names<- read.csv(file="hgnc-symbol-check.csv",sep=";", header=T)

flciber<- read.csv(file="CIBERSORTxHiRes_LM4_protein_coding_fl_Normal_Bcells_Window20.txt",header=T, sep="\t", stringsAsFactors = F)

samples.exp<- flciber[!grepl("BC", names(flciber))]

samples.exp.76<- select(samples.exp,-c("JP6_2", "P34_1", "P35_1"))



samples.hist.76<- subset(samples.exp.76, Gene %in% match_names$Input)





##update gene names





samples.hist.76$Gene[match(match_names$Input, samples.hist.76$Gene)]<- match_names$Approved.symbol







hist.new.matrix<- samples.hist.76[,2:77]

row.names(hist.new.matrix)<- samples.hist.76$Gene



###clinical information

clinic.anno.clean<- read.table(file="clinic_infor.text",sep="\t")





###correct P23 POD24 status

clinic.anno.clean$POD24[rownames(clinic.anno.clean)=="P23_1"]<- "No" 

clinic.anno.clean$POD24[rownames(clinic.anno.clean)=="P23_2"]<- "No" 

clinic.anno.clean$POD24[rownames(clinic.anno.clean)=="P23_3"]<- "No" 



aka3 = list(POD24 = c(No = "grey", Yes="black"),

            Type= c(pre = "dark cyan", relapse="#fbb4ae", transform="purple", Naive= "#7570B3",GC="#E7298A", Memory="#66A61E"),

            

            Group =c(nFL="#998EC3",tFL= "#F1A340", Tonsil="white"),

            EZH2=c(Wt = "grey", Mut="black"))



brewer.pal(n = 8, name = "Dark2")



#####new colors

paletteLength <- 100

mycolor<- colorRampPalette(c("blue",  "yellow"))(7)

pdf(file="hist_heatmap_20230912_scale_color.pdf", width=10, height=6.5)

pheatmap(log(hist.new.matrix), annotation_col = clinic.anno.clean, annotation_colors   = aka3, 

         fontsize = 6, show_colnames = F, scale ="row",

         color = mycolor)



dev.off()



###Add mutation status of histone genes

library(reshape)

flreseq<- read.csv(file="../fl_latest/16_combined/fl44_reseq_final_060619.txt", sep="\t",header=T, stringsAsFactors = F)

flreseq.76<-subset(flreseq, SAMPLE %in% names(hist.new.matrix))

flreseq.76.hist4<- subset(flreseq.76, SYMBOL %in% c("HIST1H1B", "HIST1H1C","HIST1H1D", "HIST1H1E"))

flreseq.76.hist4.coding<- flreseq.76.hist4 %>% filter(Region=="Coding")%>% filter(VARIANT!="Silent")





summ.1<-ddply(flreseq.76.hist4.coding, .(SAMPLE, SYMBOL), summarise, n=length(SYMBOL))

summ.cast<-cast(summ.1, SAMPLE~SYMBOL, value="n")

###samples without mutations

missing.samples<- setdiff(names(hist.new.matrix),summ.cast$SAMPLE)

####add these info to summ.cast

summ.total<- summ.cast %>% add_row(SAMPLE = missing.samples, HIST1H1B = c(NA), HIST1H1C=c(NA), HIST1H1D=c(NA),HIST1H1E=c(NA))

names(summ.total)<-c("SAMPLE", "H1-5","H1-2","H1-3","H1-4")







summ.total[2:5][!is.na(summ.total[2:5])]<- "Mut"

summ.total[2:5][is.na(summ.total[2:5])]<- "WT"



his_info<-summ.total[2:5]

row.names(his_info)<- summ.total$SAMPLE



clinic.all<- merge(clinic.anno.clean, his_info, by="row.names")

clinic.all.clean<- clinic.all[2:9]

row.names(clinic.all.clean)<-clinic.all$Row.names



aka4 = list(POD24 = c(No = "grey", Yes="black"),

            Type= c(pre = "dark cyan", relapse="#fbb4ae", transform="purple", Naive= "#7570B3",GC="#E7298A", Memory="#66A61E"),

            

            Group =c(nFL="#998EC3",tFL= "#F1A340", Tonsil="white"),

            `EZH2`=c(WT = "green", Mut="red"),

            `H1-5`=c(WT = "green", Mut="red"),

            `H1-2`=c(WT = "green", Mut="red"),

            `H1-3`=c(WT = "green", Mut="red"),

            `H1-4`=c(WT = "green", Mut="red"))

clinic.all.clean<- clinic.all.clean[, c(6,7,8,5,1,2,3,4)]

names(clinic.all.clean)[5]<-"EZH2"



pdf(file="hist_heatmap_20230918_scale_color.pdf", width=10, height=6.5)

pheatmap(log(hist.new.matrix), annotation_col = clinic.all.clean, annotation_colors   = aka4, 

         fontsize = 6, show_colnames = F, scale ="row",

         color = mycolor)

dev.off()