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fig1_mtDNAcnRaw.R
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fig1_mtDNAcnRaw.R
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library(ggplot2)
library(ggpubr)
library(gridExtra)
library(gtable)
library(grid)
library(cowplot)
# Fig 1A-D - Boxplot raw mtDNAcn for ROSMAP
plotMtDNAcnRosmap <- function(csv="rosmap.csv") {
tissueCols <- c("DLPFC"="royalblue", "PCC"="deepskyblue", "CB"="forestgreen")
relEffectSize <- rep(NaN, 4)
names(relEffectSize) <- c("dlpfcQ", "dlpfcA", "pcc", "cb")
mt <- read.csv(csv, colClasses=c(ProjID="character", apoe_genotype="character"),
stringsAsFactors=FALSE)
mtDlpfcQ <- mt[mt$BrainRegion == "DLPFC" & mt$ExtractionKit == "QIAamp", ]
mtDlpfcA <- mt[mt$BrainRegion == "DLPFC" & mt$ExtractionKit == "AllPrepUniversal", ]
mtPCC <- mt[mt$BrainRegion == "PCC", ]
mtCB <- mt[mt$BrainRegion == "CB", ]
# DLPFC QIAamp
n <- table(mtDlpfcQ$pathoAD)
labels <- c("0"=paste("Non AD\n(n=", n["0"] , ")", sep=""),
"1"=paste("AD\n(n=", n["1"] , ")", sep=""))
mtDlpfcQ$Dx <- factor(labels[as.character(mtDlpfcQ$pathoAD)], levels=labels)
compare <- list(c(labels["0"], labels["1"]))
pDQ <- ggplot(mtDlpfcQ, aes(x=Dx, y=mtDNAcnRaw)) +
geom_boxplot(fill=tissueCols["DLPFC"], outlier.shape=NA) +
geom_jitter(width=0.1, size=1) +
stat_compare_means(comparisons=compare, method="wilcox.test") +
xlab("") +
ylab("mtDNAcn in DLPFC\n(QIAamp)") +
ylim(c(floor(min(mtDlpfcQ$mtDNAcnRaw)), max(mtDlpfcQ$mtDNAcnRaw) + 600)) +
theme_light() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1))
relEffectSize["dlpfcQ"] <- median(mtDlpfcQ$mtDNAcnRaw[mtDlpfcQ$pathoAD == 1]) / median(mtDlpfcQ$mtDNAcnRaw[mtDlpfcQ$pathoAD == 0])
# DLPFC AllPrepUniversal
n <- table(mtDlpfcA$pathoAD)
labels <- c("0"=paste("Non AD\n(n=", n["0"] , ")", sep=""),
"1"=paste("AD\n(n=", n["1"] , ")", sep=""))
mtDlpfcA$Dx <- factor(labels[as.character(mtDlpfcA$pathoAD)], levels=labels)
compare <- list(c(labels["0"], labels["1"]))
pDA <- ggplot(mtDlpfcA, aes(x=Dx, y=mtDNAcnRaw)) +
geom_boxplot(fill=tissueCols["DLPFC"], outlier.shape=NA) +
geom_jitter(width=0.1, size=1) +
stat_compare_means(comparisons=compare, method="wilcox.test") +
xlab("") +
ylab("mtDNAcn in DLPFC\n(AllPrep Universal)") +
ylim(c(floor(min(mtDlpfcA$mtDNAcnRaw)), max(mtDlpfcA$mtDNAcnRaw) + 250)) +
theme_light() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1))
relEffectSize["dlpfcA"] <- median(mtDlpfcA$mtDNAcnRaw[mtDlpfcA$pathoAD == 1]) / median(mtDlpfcA$mtDNAcnRaw[mtDlpfcA$pathoAD == 0])
# PCC
n <- table(mtPCC$pathoAD)
labels <- c("0"=paste("Non AD\n(n=", n["0"] , ")", sep=""),
"1"=paste("AD\n(n=", n["1"] , ")", sep=""))
mtPCC$Dx <- factor(labels[as.character(mtPCC$pathoAD)], levels=labels)
compare <- list(c(labels["0"], labels["1"]))
pPCC <- ggplot(mtPCC, aes(x=Dx, y=mtDNAcnRaw)) +
geom_boxplot(fill=tissueCols["PCC"], outlier.shape=NA) +
geom_jitter(width=0.1, size=1) +
stat_compare_means(comparisons=compare, method="wilcox.test") +
xlab("") +
ylab("mtDNAcn in PCC\n(AllPrep Universal)") +
ylim(c(floor(min(mtPCC$mtDNAcnRaw)), max(mtPCC$mtDNAcnRaw) + 200)) +
theme_light() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1))
relEffectSize["pcc"] <- median(mtPCC$mtDNAcnRaw[mtPCC$pathoAD == 1]) / median(mtPCC$mtDNAcnRaw[mtPCC$pathoAD == 0])
# CB
n <- table(mtCB$pathoAD)
labels <- c("0"=paste("Non AD\n(n=", n["0"] , ")", sep=""),
"1"=paste("AD\n(n=", n["1"] , ")", sep=""))
mtCB$Dx <- factor(labels[as.character(mtCB$pathoAD)], levels=labels)
compare <- list(c(labels["0"], labels["1"]))
pCB <- ggplot(mtCB, aes(x=Dx, y=mtDNAcnRaw)) +
geom_boxplot(fill=tissueCols["CB"], outlier.shape=NA) +
geom_jitter(width=0.1, size=1) +
stat_compare_means(comparisons=compare, method="wilcox.test") +
xlab("") +
ylab("mtDNAcn in CB\n(Gentra Puregene)") +
ylim(c(floor(min(mtCB$mtDNAcnRaw)), max(mtCB$mtDNAcnRaw) + 500)) +
theme_light() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1))
relEffectSize["cb"] <- median(mtCB$mtDNAcnRaw[mtCB$pathoAD == 1]) / median(mtCB$mtDNAcnRaw[mtCB$pathoAD == 0])
return(list(dlpfcQ=pDQ, dlpfcA=pDA, pcc=pPCC, cb=pCB, relChange=relEffectSize))
}
# Fig 1E - Boxplot raw mtDNAcn for Mayo
plotMtDNAcnMayo <- function(csv="mayo.csv") {
mt <- read.csv(csv, colClasses=c(SampleID="character", apoe_genotype="character"),
stringsAsFactors=FALSE)
relEffectSize <- rep(NaN, 3)
names(relEffectSize) <- c("AD", "PSP", "PathoAging")
n <- table(mt$Diagnosis)
labels <- c("Control"=paste("Control\n(n=", n["Control"] , ")", sep=""),
"AD"=paste("AD\n(n=", n["AD"] , ")", sep=""),
"PSP"=paste("PSP\n(n=", n["PSP"] , ")", sep=""),
"PathologicAging"=paste("Path. Aging\n(n=", n["PathologicAging"] , ")", sep=""))
mt$Dx <- factor(labels[as.character(mt$Diagnosis)], levels=labels)
compare <- list(c(labels["Control"], labels["AD"]), c(labels["Control"], labels["PSP"]), c(labels["Control"], labels["PathologicAging"]))
p <- ggplot(mt, aes(x=Dx, y=mtDNAcnRaw)) +
geom_boxplot(fill="salmon", outlier.shape=NA) +
geom_jitter(width=0.1, size=1) +
stat_compare_means(comparisons=compare, method="wilcox.test") +
xlab("") +
ylab("mtDNAcn in TCX") +
ylim(c(floor(min(mt$mtDNAcnRaw)), max(mt$mtDNAcnRaw) + 1200)) +
theme_light() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1))
relEffectSize["AD"] <- median(mt$mtDNAcnRaw[mt$Diagnosis == "AD"]) / median(mt$mtDNAcnRaw[mt$Diagnosis == "Control"])
relEffectSize["PSP"] <- median(mt$mtDNAcnRaw[mt$Diagnosis == "PSP"]) / median(mt$mtDNAcnRaw[mt$Diagnosis == "Control"])
relEffectSize["PathoAging"] <- median(mt$mtDNAcnRaw[mt$Diagnosis == "PathologicAging"]) / median(mt$mtDNAcnRaw[mt$Diagnosis == "Control"])
return(list(p=p, relChange=relEffectSize))
}
# Fig 1F - Boxplot raw mtDNAcn for MSBB
plotMtDNAcnMsbb <- function(csv="msbb.csv") {
mt <- read.csv(csv, colClasses=c(SampleID="character", apoe_genotype="character"),
stringsAsFactors=FALSE)
relEffectSize <- c("AD"=NaN)
n <- table(mt$pathoAD)
labels <- c("0"=paste("Control\n(n=", n["0"] , ")", sep=""),
"1"=paste("AD\n(n=", n["1"] , ")", sep=""))
mt$Dx <- factor(labels[as.character(mt$pathoAD)], levels=labels)
compare <- list(c(labels["0"], labels["1"]))
p <- ggplot(mt, aes(x=Dx, y=mtDNAcnRaw)) +
geom_boxplot(fill="aquamarine3", outlier.shape=NA) +
geom_jitter(width=0.1, size=1) +
stat_compare_means(comparisons=compare, method="wilcox.test") +
xlab("") +
ylab("mtDNAcn in FP") +
ylim(c(floor(min(mt$mtDNAcnRaw)), max(mt$mtDNAcnRaw) + 400)) +
theme_light() + theme(axis.text.x=element_text(angle=45, vjust=1, hjust=1))
relEffectSize["AD"] <- median(mt$mtDNAcnRaw[mt$pathoAD == 1]) / median(mt$mtDNAcnRaw[mt$pathoAD == 0])
return(list(p=p, relChange=relEffectSize))
}
# Fig S1A-E - QQ plots of normalized mtDNAcn for each region/study
generateQqPlots <- function(rosmap="rosmap.csv", mayo="mayo.csv", msbb="msbb.csv") {
tissueCols <- c(DLPFC="royalblue", PCC="deepskyblue", CB="forestgreen",
TCX="salmon", FP="aquamarine3")
# ROSMAP
mt <- read.csv(rosmap, colClasses=c(ProjID="character", apoe_genotype="character"),
stringsAsFactors=FALSE)
mt <- split(mt, mt$BrainRegion)
mtDNAcn <- data.frame(mtDNAcn=mt$DLPFC$mtDNAcn)
stopifnot(all(is.na(mtDNAcn$mtDNAcn) == FALSE))
ks <- ks.test(x=mtDNAcn$mtDNAcn, y="pnorm")$p.value # 0.49
label <- paste("p=", round(ks, digits=2), ", n=", nrow(mtDNAcn), sep="")
p1 <- ggplot(data=mtDNAcn, aes(sample=mtDNAcn)) +
geom_qq(distribution = "qnorm") +
geom_abline(intercept=0, slope=1, color=tissueCols["DLPFC"]) +
annotate(geom="text", x=-1.6, y=2.7, label=label) +
ylab("DLPFC mtDNAcn quantiles") +
xlab("Theoretical quantiles") +
theme_light()
mtDNAcn <- data.frame(mtDNAcn=mt$PCC$mtDNAcn)
stopifnot(all(is.na(mtDNAcn$mtDNAcn) == FALSE))
ks <- ks.test(x=mtDNAcn$mtDNAcn, y="pnorm")$p.value # 0.89
label <- paste("p=", round(ks, digits=2), ", n=", nrow(mtDNAcn), sep="")
p2 <- ggplot(data=mtDNAcn, aes(sample=mtDNAcn)) +
geom_qq(distribution = "qnorm") +
geom_abline(intercept=0, slope=1, color=tissueCols["PCC"]) +
annotate(geom="text", x=-1.3, y=1.65, label=label) +
ylab("PCC mtDNAcn quantiles") +
xlab("Theoretical quantiles") +
theme_light()
mtDNAcn <- data.frame(mtDNAcn=mt$CB$mtDNAcn)
stopifnot(all(is.na(mtDNAcn$mtDNAcn) == FALSE))
ks <- ks.test(x=mtDNAcn$mtDNAcn, y="pnorm")$p.value # 0.56
label <- paste("p=", round(ks, digits=2), ", n=", nrow(mtDNAcn), sep="")
p3 <- ggplot(data=mtDNAcn, aes(sample=mtDNAcn)) +
geom_qq(distribution = "qnorm") +
geom_abline(intercept=0, slope=1, color=tissueCols["CB"]) +
annotate(geom="text", x=-1.5, y=3.05, label=label) +
ylab("CB mtDNAcn quantiles") +
xlab("Theoretical quantiles") +
theme_light()
# Mayo
mt <- mt <- read.csv(mayo, colClasses=c(SampleID="character", apoe_genotype="character"),
stringsAsFactors=FALSE)
mtDNAcn <- data.frame(mtDNAcn=mt$mtDNAcn)
stopifnot(all(is.na(mtDNAcn$mtDNAcn) == FALSE))
ks <- ks.test(x=mtDNAcn$mtDNAcn, y="pnorm")$p.value # 0.91
label <- paste("p=", round(ks, digits=2), ", n=", nrow(mtDNAcn), sep="")
p4 <- ggplot(data=mtDNAcn, aes(sample=mtDNAcn)) +
geom_qq(distribution = "qnorm") +
geom_abline(intercept=0, slope=1, color=tissueCols["TCX"]) +
annotate(geom="text", x=-1.55, y=2.2, label=label) +
ylab("TCX mtDNAcn quantiles") +
xlab("Theoretical quantiles") +
theme_light()
# MSBB
mt <- read.csv(msbb, colClasses=c(SampleID="character", apoe_genotype="character"),
stringsAsFactors=FALSE)
mtDNAcn <- data.frame(mtDNAcn=mt$mtDNAcn)
stopifnot(all(is.na(mtDNAcn$mtDNAcn) == FALSE))
ks <- ks.test(x=mtDNAcn$mtDNAcn, y="pnorm")$p.value # 0.23
label <- paste("p=", round(ks, digits=2), ", n=", nrow(mtDNAcn), sep="")
p5 <- ggplot(data=mtDNAcn, aes(sample=mtDNAcn)) +
geom_qq(distribution = "qnorm") +
geom_abline(intercept=0, slope=1, color=tissueCols["FP"]) +
annotate(geom="text", x=-1.55, y=3.05, label=label) +
ylab("FP mtDNAcn quantiles") +
xlab("Theoretical quantiles") +
theme_light()
return(list(p1, p2, p3, p4, p5))
}
# Figure 1 panel
generateFigure1 <- function() {
rosmap <- plotMtDNAcnRosmap("rosmap.csv")
mayo <- plotMtDNAcnMayo("mayo.csv")
msbb <- plotMtDNAcnMsbb("msbb.csv")
effSizes <- data.frame("Region/Study"=c("DLPFC/ROSMAP\n(QIAamp)", "DLPFC/ROSMAP\n(AllPrep Universal)", "PCC/ROSMAP", "CB/ROSMAP", "TCX/Mayo", "FP/MSBB"),
"mtDNAcn in AD\nrelative to controls"=paste(sprintf("%3.1f", round(c(rosmap$relChange, mayo$relChange["AD"], msbb$relChange), digits=3)*100), "%", sep=""),
stringsAsFactors=FALSE, check.names=FALSE)
# Generate table as last figure in panel
table1g <- tableGrob(effSizes, rows=NULL,
theme=ttheme_minimal(core=list(fg_params=list(hjust=c(rep(0, 6), rep(1, 6)), x=c(rep(0.05, 6), rep(0.95, 6)))),
colhead=list(fg_params=list(hjust=c(0, 1), x=c(0.05, 0.95))),
padding=unit(c(5,5), "mm"), base_size=9))
table1g <- gtable_add_grob(table1g,
grobs=segmentsGrob(x0=c(unit(0, "npc"), x0=unit(0, "npc")) , y0=c(unit(1, "npc"), unit(0, "npc")),
x1=c(unit(1, "npc"), unit(1, "npc")), y1=c(unit(1, "npc"), unit(0, "npc")), gp=gpar(lwd=2.0)),
t=1, b=1, l=1, r=ncol(table1g))
table1g <- gtable_add_grob(table1g,
grobs=segmentsGrob(x0=unit(0, "npc"), y0=unit(0, "npc"), x1=unit(1, "npc"), y1=unit(0, "npc"), gp=gpar(lwd=2.0)),
t=nrow(table1g), b=1, l=1, r=ncol(table1g))
# Generate panel
upperRow <- plot_grid(rosmap$dlpfcQ, rosmap$dlpfcA, rosmap$pcc, rosmap$cb, nrow=1,
labels=c("A", "B", "C", "D"))
lowerRow <- plot_grid(mayo$p, msbb$p, table1g, nrow=1, rel_widths=c(0.43, 0.23, 0.34),
labels=c("E", "F", "G"))
p <- plot_grid(upperRow, lowerRow, nrow=2)
ggsave("figures/figure1.pdf", plot=p, width=11, height=6)
}
# Figure S1 panel
generateFigureS1 <- function() {
ps <- generateQqPlots(rosmap="rosmap.csv", mayo="mayo.csv", msbb="msbb.csv")
p <- plot_grid(ps[[1]], ps[[2]], ps[[3]],
ps[[4]], ps[[5]], NULL,
labels=c("A", "B", "C", "D", "E", "", nrow=2, ncol=3))
ggsave("figures/figureS1.pdf", plot=p, width=12, height=7)
}
generateFigure1()
generateFigureS1()