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additional_codes
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# Additional analyses were not included in the final draft
# downstream analysis for each patient (downstream of the original code)
# patients with matched RNAseq data
bvals_pat <- as.data.frame(bvals)
bvals_p0506 <- bvals_pat[,c(9,44,37,39)]
bvals_p2301 <- bvals_pat[,c(22,41,42,23,43,45)]
bvals_p2302 <- bvals_pat[,c(46,47,48,49)]
mvals_p0506 <- apply(bvals_p0506, 2, function(x) log2(x/(1-x)))
mvals_p2301 <- apply(bvals_p2301, 2, function(x) log2(x/(1-x)))
mvals_p2302 <- apply(bvals_p2302, 2, function(x) log2(x/(1-x)))
# bvals patient delta-beta
bvals_p0506 <- mutate(bvals_p0506, p0506_C1D1 = apply(bvals_p0506[c(1:2)], 1, mean))
bvals_p0506 <- mutate(bvals_p0506, p0506_C2D8 = apply(bvals_p0506[c(3:4)], 1, mean))
bvals_p0506 <- mutate(bvals_p0506, p0506_DB = p0506_C2D8 - p0506_C1D1)
bvals_p2301 <- mutate(bvals_p2301, p2301_C1D1 = apply(bvals_p2301[c(1:3)], 1, mean))
bvals_p2301 <- mutate(bvals_p2301, p2301_C2D8 = apply(bvals_p2301[c(4:6)], 1, mean))
bvals_p2301 <- mutate(bvals_p2301, p2301_DB = p2301_C2D8 - p2301_C1D1)
bvals_p2302 <- mutate(bvals_p2302, p2302_C1D1 = apply(bvals_p2302[c(1:2)], 1, mean))
bvals_p2302 <- mutate(bvals_p2302, p2302_C2D8 = apply(bvals_p2302[c(3:4)], 1, mean))
bvals_p2302 <- mutate(bvals_p2302, p2302_DB = p2302_C2D8 - p2302_C1D1)
# per patient limma
# p0506
treatment_p0506 <- factor(rep(c("C1D1","C2D8"),each = 2))
design_p0506 <- model.matrix(~treatment_p0506)
colnames(design_p0506) <- c("C1D1","C2D8")
treatment.fit_p0506 <- lmFit(mvals_p0506, design_p0506)
treatment.fit_p0506 <- eBayes(treatment.fit)
annot450kSub_p0506 <- annot450k[match(rownames(mvals_p0506),annot450k$Name),
c(4,19,24,26)]
mvals.output_p0506 <- topTable(treatment.fit_p0506, coef=2,
genelist=annot450kSub, number=nrow(mvals_p0506),
adjust.method="fdr")
# p2301
treatment_p2301 <- factor(rep(c("C1D1","C2D8"),each = 3))
design_p2301 <- model.matrix(~treatment_p2301)
colnames(design_p2301) <- c("C1D1","C2D8")
treatment.fit_p2301 <- lmFit(mvals_p2301, design_p2301)
treatment.fit_p2301 <- eBayes(treatment.fit)
annot450kSub_p2301 <- annot450k[match(rownames(mvals_p2301),annot450k$Name),
c(4,19,24,26)]
mvals.output_p2301 <- topTable(treatment.fit_p2301, coef=2,
genelist=annot450kSub, number=nrow(mvals_p2301),
adjust.method="fdr")
# p2302
treatment_p2302 <- factor(rep(c("C1D1","C2D8"),each = 2))
design_p2302 <- model.matrix(~treatment_p2302)
colnames(design_p2302) <- c("C1D1","C2D8")
treatment.fit_p2302 <- lmFit(mvals_p2302, design_p2302)
treatment.fit_p2302 <- eBayes(treatment.fit)
annot450kSub_p2302 <- annot450k[match(rownames(mvals_p2302),annot450k$Name),
c(4,19,24,26)]
mvals.output_p2302 <- topTable(treatment.fit_p2302, coef=2,
genelist=annot450kSub, number=nrow(mvals_p2302),
adjust.method="fdr")
bvals_p0506 <- bvals_p0506[which(is.element(rownames(bvals_p0506),mvals.output_p0506$Name)),]
mvals.output_p0506 <- mvals.output_p0506[which(is.element(mvals.output_p0506$Name,rownames(bvals_p0506))),]
mvals.output_p0506 <- mvals.output_p0506[order(rownames(mvals.output_p0506), decreasing=F),]
bvals_p0506 <- bvals_p0506[order(rownames(bvals_p0506), decreasing=F),]
# mvals.output_p0506$UCSC_RefGene_Name<- sub(";.*","",mvals.output_p0506$UCSC_RefGene_Name, perl=TRUE)
# mvals.output_p0506$UCSC_RefGene_Group<- sub(";.*","",mvals.output_p0506$UCSC_RefGene_Group, perl=TRUE)
mvals.output_p0506$C1D1 <- bvals_p0506[, "p0506_C1D1"]
mvals.output_p0506$C2D8 <- bvals_p0506[, "p0506_C2D8"]
mvals.output_p0506$delta_beta <- bvals_p0506[, "p0506_DB"]
bvals_p2301 <- bvals_p2301[which(is.element(rownames(bvals_p2301),mvals.output_p2301$Name)),]
mvals.output_p2301 <- mvals.output_p2301[which(is.element(mvals.output_p2301$Name,rownames(bvals_p2301))),]
mvals.output_p2301 <- mvals.output_p2301[order(rownames(mvals.output_p2301), decreasing=F),]
bvals_p2301 <- bvals_p2301[order(rownames(bvals_p2301), decreasing=F),]
# mvals.output_p2301$UCSC_RefGene_Name<- sub(";.*","",mvals.output_p2301$UCSC_RefGene_Name, perl=TRUE)
# mvals.output_p2301$UCSC_RefGene_Group<- sub(";.*","",mvals.output_p2301$UCSC_RefGene_Group, perl=TRUE)
mvals.output_p2301$C1D1 <- bvals_p2301[, "p2301_C1D1"]
mvals.output_p2301$C2D8 <- bvals_p2301[, "p2301_C2D8"]
mvals.output_p2301$delta_beta <- bvals_p2301[, "p2301_DB"]
bvals_p2302 <- bvals_p2302[which(is.element(rownames(bvals_p2302),mvals.output_p2302$Name)),]
mvals.output_p2302 <- mvals.output_p2302[which(is.element(mvals.output_p2302$Name,rownames(bvals_p2302))),]
mvals.output_p2302 <- mvals.output[order(rownames(mvals.output_p2302), decreasing=F),]
bvals_p2302 <- bvals_p2302[order(rownames(bvals_p2302), decreasing=F),]
# mvals.output_p2302$UCSC_RefGene_Name<- sub(";.*","",mvals.output_p2302$UCSC_RefGene_Name, perl=TRUE)
# mvals.output_p2302$UCSC_RefGene_Group<- sub(";.*","",mvals.output_p2302$UCSC_RefGene_Group, perl=TRUE)
mvals.output_p2302$C1D1 <- bvals_p2302[, "p2302_C1D1"]
mvals.output_p2302$C2D8 <- bvals_p2302[, "p2302_C2D8"]
mvals.output_p2302$delta_beta <- bvals_p2302[, "p2302_DB"]
mvals.output_p0506 <- mvals.output_p0506[!mvals.output_p0506$UCSC_RefGene_Name == "",]
mvals.output_p0506 <- mvals.output_p0506[!is.na(mvals.output_p0506$UCSC_RefGene_Name),]
mvals.output_p2301 <- mvals.output_p2301[!mvals.output_p2301$UCSC_RefGene_Name == "",]
mvals.output_p2301 <- mvals.output_p2301[!is.na(mvals.output_p2301$UCSC_RefGene_Name),]
mvals.output_p2302 <- mvals.output_p2302[!mvals.output_p2302$UCSC_RefGene_Name == "",]
mvals.output_p2302 <- mvals.output_p2302[!is.na(mvals.output_p2302$UCSC_RefGene_Name),]
mvals.output_p0506 <- mvals.output_p0506[,c(3,13,8,1,4,2)]
mvals.output_p2301 <- mvals.output_p2301[,c(3,13,8,1,4,2)]
mvals.output_p2302 <- mvals.output_p2302[,c(3,13,8,1,4,2)]
# merged DNA methylation and RNA sequencing for each invidual patient with matched datasets
write.csv(mvals.output_p0506, file = "mvals.output_p0506.csv")
write.csv(mvals.output_p2301, file = "mvals.output_p2301.csv")
write.csv(mvals.output_p2302, file = "mvals.output_p2302.csv")
write.csv(mvals.output_p0506, file = "dds.p0506.csv")
write.csv(mvals.output_p0506, file = "dds.p2301.csv")
write.csv(mvals.output_p0506, file = "dds.p2302.csv")
# Individual data integration was executed in Microsoft Excel
# =IFERROR(INDEX($X:$X,MATCH(Y,$Z:$Z,0)),"")
# where ‘$X:$X’ represents the gene list from RNA-seq analysis, ‘Y’ represents the specific gene, and $Z:$Z’ represents the specific value
# Created new .CSV file for this
# mvals.output_p0506.csv
# mvals.output_p2301.csv
# mvals.output_p2302.csv
#
p0506 <-read.csv("mvals.output_p0506.csv", as.is=T)
p0506$stat <- NA
p0506$stat[p0506$RNA_log2FoldChange >= 1 & p0506$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC > ± 1"
p0506$stat[p0506$RNA_log2FoldChange >= 1 & p0506$delta_beta <= -0.15]<- "delta-beta > 15% + Log2FC > ± 1"
p0506$stat[p0506$RNA_log2FoldChange <= -1 & p0506$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC > ± 1"
p0506$stat[p0506$RNA_log2FoldChange <= -1 & p0506$delta_beta <= -0.15]<- "delta-beta > 15% + Log2FC > ± 1"
p0506$stat[p0506$RNA_log2FoldChange >= -1 & p0506$RNA_log2FoldChange <= 1] <- "Not Changed"
p0506$stat[is.na(p0506$stat)] <- "delta-beta < 15% + Log2FC > ± 1"
p0506$stat[p0506$stat == "Not Changed" & p0506$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC < ± 1"
p0506$stat[p0506$stat == "Not Changed" & p0506$delta_beta <= -0.15] <- "delta-beta > 15% + Log2FC < ± 1"
p0506_2 <- p0506[p0506$stat == "delta-beta > 15% + Log2FC > ± 1",]
p0506_2_sig <- as.data.frame(unique(as.character(p0506_2$UCSC_RefGene_Name)))
p0506.plot <- ggplot(p0506, aes(x=delta_beta, y=RNA_log2FoldChange)) +
facet_wrap(~ UCSC_RefGene_Group, scales = "free_y") +
geom_point(aes(colour=factor(stat))) +
scale_colour_manual(values=c("black", "blue", "green","red"))+
scale_y_continuous(name = "Log2FC (Expression)")+
scale_x_continuous(name = "Delta-Beta (Methylation)")+
geom_hline(yintercept = 1, linetype="dashed", size=1, color="gray30")+
geom_hline(yintercept = -1, linetype="dashed", size=1, color="gray30")+
geom_vline(xintercept = -0.15, linetype="dashed", size=1, color="gray30")+
geom_vline(xintercept = 0.15, linetype="dashed", size=1, color="gray30")+
labs(title = "p0506")+
theme_bw() %+replace%
theme(axis.text.x = element_text(angle=0, hjust = 1, size = 12),
axis.text.y = element_text(angle=0, hjust = 0.5, size = 12),
axis.ticks.x=element_blank(),
axis.title.x = element_text(face="bold", vjust = -1, angle=0, size=12),
axis.title.y = element_text(face="bold", vjust = 2, angle=90, size=12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.title=element_blank(),
legend.position= "bottom",
legend.key = element_rect(fill = "white", 0.1))
p0506.plot
#
p2301 <-read.csv("mvals.output_p2301.csv", as.is=T)
p2301$stat <- NA
p2301$stat[p2301$RNA_log2FoldChange >= 1 & p2301$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC > ± 1"
p2301$stat[p2301$RNA_log2FoldChange >= 1 & p2301$delta_beta <= -0.15]<- "delta-beta > 15% + Log2FC > ± 1"
p2301$stat[p2301$RNA_log2FoldChange <= -1 & p2301$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC > ± 1"
p2301$stat[p2301$RNA_log2FoldChange <= -1 & p2301$delta_beta <= -0.15]<- "delta-beta > 15% + Log2FC > ± 1"
p2301$stat[p2301$RNA_log2FoldChange >= -1 & p2301$RNA_log2FoldChange <= 1] <- "Not Changed"
p2301$stat[is.na(p2301$stat)] <- "delta-beta < 15% + Log2FC > ± 1"
p2301$stat[p2301$stat == "Not Changed" & p2301$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC < ± 1"
p2301$stat[p2301$stat == "Not Changed" & p2301$delta_beta <= -0.15] <- "delta-beta > 15% + Log2FC < ± 1"
p2301_2 <- p2301[p2301$stat == "delta-beta > 15% + Log2FC > ± 1",]
p2301_2_sig <- as.data.frame(unique(as.character(p2301_2$UCSC_RefGene_Name)))
p2301.plot <- ggplot(p2301, aes(x=delta_beta, y=RNA_log2FoldChange)) +
facet_wrap(~ UCSC_RefGene_Group, scales = "free_y") +
geom_point(aes(colour=factor(stat))) +
scale_colour_manual(values=c("black", "blue", "green","red"))+
scale_y_continuous(name = "Log2FC (Expression)")+
scale_x_continuous(name = "Delta-Beta (Methylation)")+
geom_hline(yintercept = 1, linetype="dashed", size=1, color="gray30")+
geom_hline(yintercept = -1, linetype="dashed", size=1, color="gray30")+
geom_vline(xintercept = -0.15, linetype="dashed", size=1, color="gray30")+
geom_vline(xintercept = 0.15, linetype="dashed", size=1, color="gray30")+
labs(title = "p2301")+
theme_bw() %+replace%
theme(axis.text.x = element_text(angle=0, hjust = 1, size = 12),
axis.text.y = element_text(angle=0, hjust = 0.5, size = 12),
axis.ticks.x=element_blank(),
axis.title.x = element_text(face="bold", vjust = -1, angle=0, size=12),
axis.title.y = element_text(face="bold", vjust = 2, angle=90, size=12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.title=element_blank(),
legend.position= "bottom",
legend.key = element_rect(fill = "white", 0.1))
p2301.plot
#
p2302 <-read.csv("mvals.output_p2302.csv", as.is=T)
p2302$stat <- NA
p2302$stat[p2302$RNA_log2FoldChange >= 1 & p2302$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC > ± 1"
p2302$stat[p2302$RNA_log2FoldChange >= 1 & p2302$delta_beta <= -0.15]<- "delta-beta > 15% + Log2FC > ± 1"
p2302$stat[p2302$RNA_log2FoldChange <= -1 & p2302$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC > ± 1"
p2302$stat[p2302$RNA_log2FoldChange <= -1 & p2302$delta_beta <= -0.15]<- "delta-beta > 15% + Log2FC > ± 1"
p2302$stat[p2302$RNA_log2FoldChange >= -1 & p2302$RNA_log2FoldChange <= 1] <- "Not Changed"
p2302$stat[is.na(p2302$stat)] <- "delta-beta < 15% + Log2FC > ± 1"
p2302$stat[p2302$stat == "Not Changed" & p2302$delta_beta >= 0.15] <- "delta-beta > 15% + Log2FC < ± 1"
p2302$stat[p2302$stat == "Not Changed" & p2302$delta_beta <= -0.15] <- "delta-beta > 15% + Log2FC < ± 1"
p2302_2 <- p2302[p2302$stat == "delta-beta > 15% + Log2FC > ± 1",]
p2302_2_sig <- as.data.frame(unique(as.character(p2302_2$UCSC_RefGene_Name)))
p2302.plot <- ggplot(p2302, aes(x=delta_beta, y=RNA_log2FoldChange)) +
facet_wrap(~ UCSC_RefGene_Group, scales = "free_y") +
geom_point(aes(colour=factor(stat))) +
scale_colour_manual(values=c("black", "blue", "green","red"))+
scale_y_continuous(name = "Log2FC (Expression)")+
scale_x_continuous(name = "Delta-Beta (Methylation)")+
geom_hline(yintercept = 1, linetype="dashed", size=1, color="gray30")+
geom_hline(yintercept = -1, linetype="dashed", size=1, color="gray30")+
geom_vline(xintercept = -0.15, linetype="dashed", size=1, color="gray30")+
geom_vline(xintercept = 0.15, linetype="dashed", size=1, color="gray30")+
labs(title = "p2302")+
theme_bw() %+replace%
theme(axis.text.x = element_text(angle=0, hjust = 1, size = 12),
axis.text.y = element_text(angle=0, hjust = 0.5, size = 12),
axis.ticks.x=element_blank(),
axis.title.x = element_text(face="bold", vjust = -1, angle=0, size=12),
axis.title.y = element_text(face="bold", vjust = 2, angle=90, size=12),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_blank(),
legend.title=element_blank(),
legend.position= "bottom",
legend.key = element_rect(fill = "white", 0.1))
p2302.plot
# stat log2FC > 1 and DB ±15%
# p0506
p0506_prom <- p0506_2[p0506_2$UCSC_RefGene_Group == "TSS1500" |
p0506_2$UCSC_RefGene_Group == "TSS200",]
length(unique(p0506_prom$UCSC_RefGene_Name)) # 192
p0506_5UTR <- p0506_2[p0506_2$UCSC_RefGene_Group == "5'UTR",]
length(unique(p0506_5UTR$UCSC_RefGene_Name)) # 62
p0506_body <- p0506_2[p0506_2$UCSC_RefGene_Group == "Body",]
length(unique(p0506_body$UCSC_RefGene_Name)) # 140
p0506_3UTR <- p0506_2[p0506_2$UCSC_RefGene_Group == "3'UTR",]
length(unique(p0506_3UTR$UCSC_RefGene_Name)) # 53
p0506_exon <- p0506_2[p0506_2$UCSC_RefGene_Group == "1stExon",]
length(unique(p0506_exon$UCSC_RefGene_Name)) # 66
# p2301
p2301_prom <- p2301_2[p2301_2$UCSC_RefGene_Group == "TSS1500" |
p2301_2$UCSC_RefGene_Group == "TSS200",]
length(unique(p2301_prom$UCSC_RefGene_Name)) # 163
p2301_5UTR <- p2301_2[p2301_2$UCSC_RefGene_Group == "5'UTR",]
length(unique(p2301_5UTR$UCSC_RefGene_Name)) # 55
p2301_body <- p2301_2[p2301_2$UCSC_RefGene_Group == "Body",]
length(unique(p2301_body$UCSC_RefGene_Name)) # 123
p2301_3UTR <- p2301_2[p2301_2$UCSC_RefGene_Group == "3'UTR",]
length(unique(p2301_3UTR$UCSC_RefGene_Name)) # 35
p2301_exon <- p2301_2[p2301_2$UCSC_RefGene_Group == "1stExon",]
length(unique(p2301_exon$UCSC_RefGene_Name)) # 44
# p2302
p2302_prom <- p2302_2[p2302_2$UCSC_RefGene_Group == "TSS1500" |
p2302_2$UCSC_RefGene_Group == "TSS200",]
length(unique(p2302_prom$UCSC_RefGene_Name)) # 139
p2302_5UTR <- p2302_2[p2302_2$UCSC_RefGene_Group == "5'UTR",]
length(unique(p2302_5UTR$UCSC_RefGene_Name)) # 47
p2302_body <- p2302_2[p2302_2$UCSC_RefGene_Group == "Body",]
length(unique(p2302_body$UCSC_RefGene_Name)) # 117
p2302_3UTR <- p2302_2[p2302_2$UCSC_RefGene_Group == "3'UTR",]
length(unique(p2302_3UTR$UCSC_RefGene_Name)) # 35
p2302_exon <- p2302_2[p2302_2$UCSC_RefGene_Group == "1stExon",]
length(unique(p2302_exon$UCSC_RefGene_Name)) # 37