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20180614.aems450k.meta.analysis.ewas.smoking.plaque.R
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20180614.aems450k.meta.analysis.ewas.smoking.plaque.R
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cat("===========================================================================================
META-ANALYSIS ATHERO-EXPRESS METHYLATION STUDIES 450K 1 & 2
Version: v2.8
Last update: 2018-06-27
Written by: Sander W. van der Laan ([email protected]);
Marten A. Siemelink
Description: Script to do meta-analysis of Athero-Express Methylation
Study 450K 1 (2013) & 2 (2016) EWAS on smoking.
Based on DNAmArray:
- https://molepi.github.io/DNAmArray_workflow/index.html
- https://github.com/molepi/DNAmArray
Requirements: R version 3.4.1 (2017-06-30) -- 'Single Candle', Linux CentOS7, Mac OS X El Capitan+
===========================================================================================")
cat("\n===========================================================================================")
cat("CLEAR THE BOARD")
rm(list = ls())
cat("\n===========================================================================================")
cat("GENERAL R SETUP")
### FUNCTION TO INSTALL PACKAGES
### This function will automatically check in both CRAN and Bioconductor. This is
### a function found by Sander W. van der Laan online from @Samir:
### http://stackoverflow.com/questions/4090169/elegant-way-to-check-for-missing-packages-and-install-them
###
cat("\n* Creating funxtion to install and load packages...")
install.packages.auto <- function(x) {
x <- as.character(substitute(x))
if (isTRUE(x %in% .packages(all.available = TRUE))) {
eval(parse(text = sprintf("require(\"%s\")", x)))
} else {
# Update installed packages - this may mean a full upgrade of R, which in turn
# may not be warrented.
#update.packages(ask = FALSE)
eval(parse(text = sprintf("install.packages(\"%s\", dependencies = TRUE, repos = \"http://cran-mirror.cs.uu.nl/\")", x)))
}
if (isTRUE(x %in% .packages(all.available = TRUE))) {
eval(parse(text = sprintf("require(\"%s\")", x)))
} else {
source("http://bioconductor.org/biocLite.R")
# Update installed packages - this may mean a full upgrade of R, which in turn
# may not be warrented.
#biocLite(character(), ask = FALSE)
eval(parse(text = sprintf("biocLite(\"%s\")", x)))
eval(parse(text = sprintf("require(\"%s\")", x)))
}
}
# In this case I'm keeping track of the various packages, as versions and
# actual loading of the libraries gave issues before.
cat("\n* General packages...\n")
# for survival analyses
install.packages.auto("survival")
install.packages.auto("survminer")
# for general statistics
install.packages.auto("Hmisc")
install.packages.auto("openxlsx")
install.packages.auto("devtools")
install.packages.auto("dplyr")
install.packages.auto("data.table")
install.packages.auto("tableone")
install.packages.auto("haven")
# for methylation/rna data
# install.packages.auto("RMySQL") # install this one from CRAN!
install.packages.auto("GenomicFeatures")
install.packages.auto("bumphunter")
install.packages.auto("minfi")
install.packages.auto("SummarizedExperiment")
install.packages.auto("IlluminaHumanMethylation450kmanifest")
install.packages.auto("IlluminaHumanMethylation450kanno.ilmn12.hg19")
install.packages.auto("FDb.InfiniumMethylation.hg19")
install.packages.auto("TxDb.Hsapiens.UCSC.hg19.knownGene")
install.packages.auto("org.Hs.eg.db")
install.packages.auto("AnnotationDbi")
# for plotting
install.packages.auto("pheatmap")
install.packages.auto("qqman")
install.packages.auto("forestplot")
# for meta-analysis
install.packages.auto("meta")
install.packages.auto("bacon")
# The actual DNAmArray package
cat("\n* DNAmArray package...\n")
# Also refer to:
# - https://molepi.github.io/DNAmArray_workflow/index.html
# - https://github.com/molepi/DNAmArray
# - https://github.com/bbmri-nl/BBMRIomics
library(devtools)
install_github("molepi/DNAmArray", force = FALSE)
library(DNAmArray)
install_github("molepi/omicsPrint", ref = "R3.4", force = FALSE)
library(omicsPrint)
install_github("bbmri-nl/BBMRIomics", subdir = "BBMRIomics", force = FALSE)
library(BBMRIomics)
cat("\n* Manhattan plotting function, based on library(\"qqman\")...")
manhattan.uithof <- function(x, chr = "CHR", bp = "BP", p = "P", snp = "SNP",
col = c("gray10", "gray60"), chrlabs = NULL,
suggestiveline = -log10(1e-05), genomewideline = -log10(5e-08),
highlight = NULL, logp = TRUE, annotatePval = NULL, annotateTop = TRUE, ...) {
CHR = BP = P = index = NULL
if (!(chr %in% names(x)))
stop(paste("Column", chr, "not found!"))
if (!(bp %in% names(x)))
stop(paste("Column", bp, "not found!"))
if (!(p %in% names(x)))
stop(paste("Column", p, "not found!"))
if (!(snp %in% names(x)))
warning(paste("No SNP/CpG column found. OK unless you're trying to highlight."))
if (!is.numeric(x[[chr]]))
stop(paste(chr, "column should be numeric. Do you have 'X', 'Y', 'MT', etc? If so change to numbers and try again."))
if (!is.numeric(x[[bp]]))
stop(paste(bp, "column should be numeric."))
if (!is.numeric(x[[p]]))
stop(paste(p, "column should be numeric."))
d = data.frame(CHR = x[[chr]], BP = x[[bp]], P = x[[p]])
if (!is.null(x[[snp]]))
d = transform(d, SNP = x[[snp]])
d <- subset(d, (is.numeric(CHR) & is.numeric(BP) & is.numeric(P)))
d <- d[order(d$CHR, d$BP), ]
if (logp) {
d$logp <- -log10(d$P)
}
else {
d$logp <- d$P
}
d$pos = NA
d$index = NA
ind = 0
for (i in unique(d$CHR)) {
ind = ind + 1
d[d$CHR == i, ]$index = ind
}
nchr = length(unique(d$CHR))
if (nchr == 1) {
d$pos = d$BP
ticks = floor(length(d$pos))/2 + 1
xlabel = paste("Chromosome", unique(d$CHR), "position")
labs = ticks
}
else {
lastbase = 0
ticks = NULL
for (i in unique(d$index)) {
if (i == 1) {
d[d$index == i, ]$pos = d[d$index == i, ]$BP
}
else {
lastbase = lastbase + tail(subset(d, index ==
i - 1)$BP, 1)
d[d$index == i, ]$pos = d[d$index == i, ]$BP +
lastbase
}
ticks = c(ticks, (min(d[d$index == i, ]$pos) + max(d[d$index ==
i, ]$pos))/2 + 1)
}
xlabel = "Chromosome"
labs <- unique(d$CHR)
}
xmax = ceiling(max(d$pos) * 1.03)
xmin = floor(max(d$pos) * -0.03)
def_args <- list(xaxt = "n", bty = "n", xaxs = "i", yaxs = "i",
las = 1, pch = 20, xlim = c(xmin, xmax), ylim = c(0,
ceiling(max(d$logp))), xlab = xlabel, ylab = expression(-log[10](italic(p))))
dotargs <- list(...)
do.call("plot", c(NA, dotargs, def_args[!names(def_args) %in%
names(dotargs)]))
if (!is.null(chrlabs)) {
if (is.character(chrlabs)) {
if (length(chrlabs) == length(labs)) {
labs <- chrlabs
}
else {
warning("You're trying to specify chromosome labels but the number of labels != number of chromosomes.")
}
}
else {
warning("If you're trying to specify chromosome labels, chrlabs must be a character vector")
}
}
if (nchr == 1) {
axis(1, ...)
}
else {
axis(1, at = ticks, labels = labs, ...)
}
col = rep(col, max(d$CHR))
if (nchr == 1) {
with(d, points(pos, logp, pch = 20, col = col[1], ...))
}
else {
icol = 1
for (i in unique(d$index)) {
with(d[d$index == unique(d$index)[i], ], points(pos,
logp, col = col[icol], pch = 20, ...))
icol = icol + 1
}
}
if (suggestiveline)
abline(h = suggestiveline, col = "#1290D9", lty = 2, lwd = 2)
if (genomewideline)
abline(h = genomewideline, col = "#E55738", lty = 2, lwd = 2)
if (!is.null(highlight)) {
if (any(!(highlight %in% d$SNP)))
warning("You're trying to highlight SNPs/CpGs that don't exist in your results.")
d.highlight = d[which(d$SNP %in% highlight), ]
with(d.highlight, points(pos, logp, col = "#595A5C", pch = 20,
...))
}
if (!is.null(annotatePval)) {
topHits = subset(d, P <= annotatePval)
par(xpd = TRUE)
if (annotateTop == FALSE) {
with(subset(d, P <= annotatePval), textxy(pos, -log10(P),
offset = 0.625, labs = topHits$SNP, cex = 1.25),
...)
}
else {
topHits <- topHits[order(topHits$P), ]
topSNPs <- NULL
for (i in unique(topHits$CHR)) {
chrSNPs <- topHits[topHits$CHR == i, ]
topSNPs <- rbind(topSNPs, chrSNPs[1, ])
}
library("calibrate")
textxy(topSNPs$pos, -log10(topSNPs$P), offset = 0.625,
labs = topSNPs$SNP, cex = 1.25, ...)
}
}
par(xpd = FALSE)
}
### Create datestamp
Today = format(as.Date(as.POSIXlt(Sys.time())), "%Y%m%d")
Today.Report = format(as.Date(as.POSIXlt(Sys.time())), "%A, %B %d, %Y")
### UtrechtSciencePark Colours Scheme
###
### Website to convert HEX to RGB: http://hex.colorrrs.com.
### For some functions you should divide these numbers by 255.
###
### No. Color HEX RGB CMYK CHR MAF/INFO
### --------------------------------------------------------------------------------------------------------------------
### 1 yellow #FBB820 (251,184,32) (0,26.69,87.25,1.57) => 1 or 1.0 > INFO
### 2 gold #F59D10 (245,157,16) (0,35.92,93.47,3.92) => 2
### 3 salmon #E55738 (229,87,56) (0,62.01,75.55,10.2) => 3 or 0.05 < MAF < 0.2 or 0.4 < INFO < 0.6
### 4 darkpink #DB003F ((219,0,63) (0,100,71.23,14.12) => 4
### 5 lightpink #E35493 (227,84,147) (0,63,35.24,10.98) => 5 or 0.8 < INFO < 1.0
### 6 pink #D5267B (213,38,123) (0,82.16,42.25,16.47) => 6
### 7 hardpink #CC0071 (204,0,113) (0,0,0,0) => 7
### 8 lightpurple #A8448A (168,68,138) (0,0,0,0) => 8
### 9 purple #9A3480 (154,52,128) (0,0,0,0) => 9
### 10 lavendel #8D5B9A (141,91,154) (0,0,0,0) => 10
### 11 bluepurple #705296 (112,82,150) (0,0,0,0) => 11
### 12 purpleblue #686AA9 (104,106,169) (0,0,0,0) => 12
### 13 lightpurpleblue #6173AD (97,115,173/101,120,180) (0,0,0,0) => 13
### 14 seablue #4C81BF (76,129,191) (0,0,0,0) => 14
### 15 skyblue #2F8BC9 (47,139,201) (0,0,0,0) => 15
### 16 azurblue #1290D9 (18,144,217) (0,0,0,0) => 16 or 0.01 < MAF < 0.05 or 0.2 < INFO < 0.4
### 17 lightazurblue #1396D8 (19,150,216) (0,0,0,0) => 17
### 18 greenblue #15A6C1 (21,166,193) (0,0,0,0) => 18
### 19 seaweedgreen #5EB17F (94,177,127) (0,0,0,0) => 19
### 20 yellowgreen #86B833 (134,184,51) (0,0,0,0) => 20
### 21 lightmossgreen #C5D220 (197,210,32) (0,0,0,0) => 21
### 22 mossgreen #9FC228 (159,194,40) (0,0,0,0) => 22 or MAF > 0.20 or 0.6 < INFO < 0.8
### 23 lightgreen #78B113 (120,177,19) (0,0,0,0) => 23/X
### 24 green #49A01D (73,160,29) (0,0,0,0) => 24/Y
### 25 grey #595A5C (89,90,92) (0,0,0,0) => 25/XY or MAF < 0.01 or 0.0 < INFO < 0.2
### 26 lightgrey #A2A3A4 (162,163,164) (0,0,0,0) => 26/MT
###
### ADDITIONAL COLORS
### 27 midgrey #D7D8D7
### 28 very lightgrey #ECECEC
### 29 white #FFFFFF
### 30 black #000000
### --------------------------------------------------------------------------------------------------------------------
uithof_color = c("#FBB820","#F59D10","#E55738","#DB003F","#E35493","#D5267B",
"#CC0071","#A8448A","#9A3480","#8D5B9A","#705296","#686AA9",
"#6173AD","#4C81BF","#2F8BC9","#1290D9","#1396D8","#15A6C1",
"#5EB17F","#86B833","#C5D220","#9FC228","#78B113","#49A01D",
"#595A5C","#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
uithof_color_legend = c("#FBB820", "#F59D10", "#E55738", "#DB003F", "#E35493",
"#D5267B", "#CC0071", "#A8448A", "#9A3480", "#8D5B9A",
"#705296", "#686AA9", "#6173AD", "#4C81BF", "#2F8BC9",
"#1290D9", "#1396D8", "#15A6C1", "#5EB17F", "#86B833",
"#C5D220", "#9FC228", "#78B113", "#49A01D", "#595A5C",
"#A2A3A4", "#D7D8D7", "#ECECEC", "#FFFFFF", "#000000")
### ----------------------------------------------------------------------------
cat("===========================================================================================")
cat("\nSETUP ANALYSIS")
# Assess where we are
getwd()
# Set locations
### Operating System Version
### Mac Pro
ROOT_loc = "/Volumes/EliteProQx2Media"
### MacBook
# ROOT_loc = "/Users/swvanderlaan"
### SOME VARIABLES WE NEED DOWN THE LINE
PROJECTDATASET = "AEMS450KMETA"
PROJECTNAME = "metasmoke"
SUBPROJECTNAME1 = "AEMS450K1"
SUBPROJECTNAME2 = "AEMS450K2"
EWAS_trait = "SmokerCurrent" # Phenotype
INP_AE_loc = paste0(ROOT_loc, "/PLINK/_AE_Originals")
INP_AEMS450K1_loc = paste0(INP_AE_loc, "/AEMS450K1")
INP_AEMS450K2_loc = paste0(INP_AE_loc, "/AEMS450K2")
EPIGENETICS_loc = paste0(ROOT_loc, "/PLINK/analyses/epigenetics")
RES_AEMS450K1_loc = paste0(EPIGENETICS_loc, "/AEMS450K1")
RES_AEMS450K2_loc = paste0(EPIGENETICS_loc, "/AEMS450K2")
ifelse(!dir.exists(file.path(EPIGENETICS_loc, "/",PROJECTDATASET)),
dir.create(file.path(EPIGENETICS_loc, "/",PROJECTDATASET)),
FALSE)
INP_loc = paste0(EPIGENETICS_loc, "/",PROJECTDATASET)
cat("\nCreate a new analysis directory...")
ifelse(!dir.exists(file.path(INP_loc, "/",PROJECTNAME)),
dir.create(file.path(INP_loc, "/",PROJECTNAME)),
FALSE)
ANALYSIS_loc = paste0(INP_loc,"/",PROJECTNAME)
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/PLOTS")),
dir.create(file.path(ANALYSIS_loc, "/PLOTS")),
FALSE)
PLOT_loc = paste0(ANALYSIS_loc,"/PLOTS")
ifelse(!dir.exists(file.path(PLOT_loc, "/COX")),
dir.create(file.path(PLOT_loc, "/COX")),
FALSE)
COX_loc = paste0(PLOT_loc,"/COX")
ifelse(!dir.exists(file.path(PLOT_loc, "/QC")),
dir.create(file.path(PLOT_loc, "/QC")),
FALSE)
QC_loc = paste0(PLOT_loc,"/QC")
ifelse(!dir.exists(file.path(ANALYSIS_loc, "/OUTPUT")),
dir.create(file.path(ANALYSIS_loc, "/OUTPUT")),
FALSE)
OUT_loc = paste0(ANALYSIS_loc, "/OUTPUT")
cat("===========================================================================================")
cat("\nLOAD ATHERO-EXPRESS METHYLATION STUDY DATASETS")
setwd(INP_loc)
list.files()
cat("\n - loading quality controlled & imputed B/Mvalues of plaque samples...")
load(paste0(INP_AEMS450K1_loc,"/20180625.aems450k1.BvaluesQCIMPSE.plaque.RData"))
load(paste0(INP_AEMS450K1_loc,"/20180625.aems450k1.MvaluesQCIMPSE.plaque.RData"))
load(paste0(INP_AEMS450K2_loc,"/20180625.aems450k2.BvaluesQCIMPSE.plaque.RData"))
load(paste0(INP_AEMS450K2_loc,"/20180625.aems450k2.MvaluesQCIMPSE.plaque.RData"))
cat("===========================================================================================")
cat("\n[ META-ANALYSIS of EPIGENOME-WIDE ASSOCIATION STUDY on ",EWAS_trait," in PLAQUE in AEMS450K1 & 2 ]")
# Reference: https://molepi.github.io/DNAmArray_workflow/06_EWAS.html
cat("===========================================================================================")
cat("\n[ SANNITY CHECKING THE DATA ]")
cat("\n * AEMS450K1, in plaque...")
pdf(paste0(QC_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.MethylationDensity.pdf"),
width = 12, height = 8, onefile = TRUE)
par(mfrow = c(1,2), oma = c(0, 0, 2, 0))
densityPlot(assays(aems450k1.BvaluesQCIMPplaqueSE)$data, sampGroups = aems450k1.BvaluesQCIMPplaqueSE$SmokerCurrent, main = "Beta-values",
legend = FALSE,
xlab = "Beta-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k1.BvaluesQCIMPplaqueSE$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
densityPlot(assays(aems450k1.MvaluesQCIMPplaqueSE)$data, sampGroups = aems450k1.MvaluesQCIMPplaqueSE$SmokerCurrent, main = "M-values",
legend = FALSE,
xlab = "M-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k1.MvaluesQCIMPplaqueSE$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
mtext(paste0("Overall methylation density (plaque, ",SUBPROJECTNAME1,")"), outer = TRUE, cex = 1.5)
par(mfrow = c(1,1), oma = c(0,0,0,0))
dev.off()
cat("\n * AEMS450K2, in plaque...")
pdf(paste0(QC_loc,"/",Today,".aems450k2.",EWAS_trait,".plaque.MethylationDensity.pdf"),
width = 12, height = 8, onefile = TRUE)
par(mfrow = c(1,2), oma = c(0, 0, 2, 0))
densityPlot(assays(aems450k2.BvaluesQCIMPplaqueSE)$data, sampGroups = aems450k2.BvaluesQCIMPplaqueSE$SmokerCurrent, main = "Beta-values",
legend = FALSE,
xlab = "Beta-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k2.BvaluesQCIMPplaqueSE$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
densityPlot(assays(aems450k2.MvaluesQCIMPplaqueSE)$data, sampGroups = aems450k2.MvaluesQCIMPplaqueSE$SmokerCurrent, main = "M-values",
legend = FALSE,
xlab = "M-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k2.MvaluesQCIMPplaqueSE$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
mtext(paste0("Overall methylation density (plaque, ",SUBPROJECTNAME2,")"), outer = TRUE, cex = 1.5)
par(mfrow = c(1,1), oma = c(0,0,0,0))
dev.off()
cat("\nRemoving BvaluesQCIMP objects - as we don't use these anymore.")
rm(aems450k1.BvaluesQCIMPplaqueSE, aems450k2.BvaluesQCIMPplaqueSE)
cat("===========================================================================================")
cat("\n[ CONTINUE (META-)ANALYSIS AEMS450K STUDIES ]")
cat("\n* Setup the analysis.")
require(FDb.InfiniumMethylation.hg19)
feats <- features(FDb.InfiniumMethylation.hg19)
chr.list <- levels(seqnames(feats))
regions <- feats[seqnames(feats) %in% chr.list]
cat("\n - Setup the model, first covariate is the variable/phenotype of interest.")
cat(" > AEMS450K1...")
aems450k1.MvaluesQCIMPplaqueSEClean <- aems450k1.MvaluesQCIMPplaqueSE
dim(aems450k1.MvaluesQCIMPplaqueSE)
metadata(aems450k1.MvaluesQCIMPplaqueSEClean)$formula <- ~SmokerCurrent + Sample_Sex + Age + Hospital
cat(" > AEMS450K2...")
aems450k2.MvaluesQCIMPplaqueSEClean <- aems450k2.MvaluesQCIMPplaqueSE
dim(aems450k2.MvaluesQCIMPplaqueSE)
metadata(aems450k2.MvaluesQCIMPplaqueSEClean)$formula <- ~SmokerCurrent + Sample_Sex + Age + Hospital
cat("\n - Next, we extract those samples having a complete set of covariates.
Notice that we subset the SummarizedExperiment-object!")
cat(" > AEMS450K1...")
aems450k1.covariates <- get_all_vars(metadata(aems450k1.MvaluesQCIMPplaqueSEClean)$formula, data = colData(aems450k1.MvaluesQCIMPplaqueSEClean))
aems450k1.nas <- apply(aems450k1.covariates, 1, anyNA)
aems450k1.MvaluesQCIMPplaqueSEClean <- aems450k1.MvaluesQCIMPplaqueSEClean[, !aems450k1.nas]
dim(aems450k1.MvaluesQCIMPplaqueSEClean)
cat(" > AEMS450K2...")
aems450k2.covariates <- get_all_vars(metadata(aems450k2.MvaluesQCIMPplaqueSEClean)$formula, data = colData(aems450k2.MvaluesQCIMPplaqueSEClean))
aems450k2.nas <- apply(aems450k2.covariates, 1, anyNA)
aems450k2.MvaluesQCIMPplaqueSEClean <- aems450k2.MvaluesQCIMPplaqueSEClean[, !aems450k2.nas]
dim(aems450k2.MvaluesQCIMPplaqueSEClean)
cat("\n - Remove probes containing
SNPs or does mapping to multiple locations (ref: W. Zhou, P.W. Laird, and H. Shen. 2016. Nucleic Acids Res.).")
cat(" > next probes with SNPs...")
data(hm450.manifest.pop.GoNL) ##From DNAmArray
#hm450.manifest.pop.GoNL
hm450.manifest.pop.GoNL <- hm450.manifest.pop.GoNL[!is.na(hm450.manifest.pop.GoNL$MASK.general.GoNL) &
hm450.manifest.pop.GoNL$MASK.general.GoNL == TRUE, ]
cat(" > AEMS450K1...")
aems450k1.MvaluesQCIMPplaqueSEClean <- aems450k1.MvaluesQCIMPplaqueSEClean[!(names(aems450k1.MvaluesQCIMPplaqueSEClean) %in% names(hm450.manifest.pop.GoNL)),]
cat(" > AEMS450K2...")
aems450k2.MvaluesQCIMPplaqueSEClean <- aems450k2.MvaluesQCIMPplaqueSEClean[!(names(aems450k2.MvaluesQCIMPplaqueSEClean) %in% names(hm450.manifest.pop.GoNL)),]
cat("\n - Subsetting AEMS450K1/AEMS450K2.")
cat(" > get a list of probes that difference between the two datasets")
aems450k1.ranges <- unlist(names(rowRanges(aems450k1.MvaluesQCIMPplaqueSEClean)))
aems450k2.ranges <- unlist(names(rowRanges(aems450k2.MvaluesQCIMPplaqueSEClean)))
cat(" > what is in both?")
aems450k.meta.intersect <- intersect(aems450k1.ranges,aems450k2.ranges)
length(aems450k.meta.intersect)
cat(" > what is different in AEMS450K1...?")
aems450k1.meta.difs <- setdiff(aems450k1.ranges,aems450k2.ranges)
length(aems450k1.meta.difs)
cat(" > what is different in AEMS450K2...?")
aems450k2.meta.difs <- setdiff(aems450k2.ranges,aems450k1.ranges)
length(aems450k2.meta.difs)
cat(" > subsetting...")
aems450k1.MvaluesQCIMPplaqueSEClean <- aems450k1.MvaluesQCIMPplaqueSEClean[(names(aems450k1.MvaluesQCIMPplaqueSEClean) %in% aems450k.meta.intersect),]
aems450k2.MvaluesQCIMPplaqueSEClean <- aems450k2.MvaluesQCIMPplaqueSEClean[(names(aems450k2.MvaluesQCIMPplaqueSEClean) %in% aems450k.meta.intersect),]
cat(" > we started with:")
dim(aems450k1.MvaluesQCIMPplaqueSE) # 483731 CpGs, 485 samples
dim(aems450k2.MvaluesQCIMPplaqueSE) # 484249 CpGs, 190 samples
# rowRanges(aems450k1.MvaluesQCIMPplaqueSEClean)
cat(" > we end up with:")
dim(aems450k1.MvaluesQCIMPplaqueSEClean) # 443084 CpGs, 477 samples
dim(aems450k2.MvaluesQCIMPplaqueSEClean) # 443084 CpGs, 187 samples
# rowRanges(aems450k2.MvaluesQCIMPplaqueSEClean)
cat("\n * Run the analysis.")
install.packages.auto("limma")
cat(" - AEMS450K1 *with* hospital.")
aems450k1.designph <- model.matrix(metadata(aems450k1.MvaluesQCIMPplaqueSEClean)$formula, data = colData(aems450k1.MvaluesQCIMPplaqueSEClean))
aems450k1.dataph <- assays(aems450k1.MvaluesQCIMPplaqueSEClean)$data
aems450k1.fitph <- limma::lmFit(aems450k1.dataph, aems450k1.designph)
cat(" > calculate T-statistic = beta/standard error...")
aems450k1.effectsizeph <- aems450k1.fitph$coefficients
aems450k1.SEph <- aems450k1.fitph$stdev.unscaled * aems450k1.fitph$sigma
aems450k1.tstatph <- aems450k1.effectsizeph/aems450k1.SEph
cat(" > calculate p-value from T-statistic, and adjust using FDR (Benjamin-Hochberg)...")
aems450k1.pvalph <- 2*pnorm(-abs(aems450k1.tstatph[,2]))
aems450k1.padjph <- p.adjust(sort(aems450k1.pvalph, decreasing = FALSE), method = "BH")
head(aems450k1.padjph[aems450k1.padjph < 0.05])
# summary
summary(aems450k1.fitph)
cat(" - AEMS450K2 *with* hospital.")
aems450k2.designph <- model.matrix(metadata(aems450k2.MvaluesQCIMPplaqueSEClean)$formula, data = colData(aems450k2.MvaluesQCIMPplaqueSEClean))
aems450k2.dataph <- assays(aems450k2.MvaluesQCIMPplaqueSEClean)$data
aems450k2.fitph <- limma::lmFit(aems450k2.dataph, aems450k2.designph)
cat(" > calculate T-statistic = beta/standard error...")
aems450k2.effectsizeph <- aems450k2.fitph$coefficients
aems450k2.SEph <- aems450k2.fitph$stdev.unscaled * aems450k2.fitph$sigma
aems450k2.tstatph <- aems450k2.effectsizeph/aems450k2.SEph
cat(" > calculate p-value from T-statistic, and adjust using FDR (Benjamin-Hochberg)...")
aems450k2.pvalph <- 2*pnorm(-abs(aems450k2.tstatph[,2]))
aems450k2.padjph <- p.adjust(sort(aems450k2.pvalph, decreasing = FALSE), method = "BH")
head(aems450k2.padjph[aems450k2.padjph < 0.05])
# summary
summary(aems450k2.fitph)
cat("\n * Correct for bias and inflation using 'BACON'.
(ref: M. van Iterson, E.W. van Zwet, and B.T. Heijmans. 2017. Genome Biol. 18 (1): 19.).")
library("bacon")
# plaque *with* hospital
aems450k.meta.th <- cbind(limmaT.aems450k1 = aems450k1.tstatph[,2], limmaT.aems450k2 = aems450k2.tstatph[,2])
aems450k.meta.esh <- cbind(limmaB.aems450k1 = aems450k1.effectsizeph[,2], limmaB.aems450k2 = aems450k2.effectsizeph[,2])
aems450k.meta.seh <- cbind(limmaSE.aems450k1 = aems450k1.SEph[,2], limmaSE.aems450k2 = aems450k2.SEph[,2])
cat("\n * Getting a handle on the test statistics of both datasets.")
cat(" - of the data 'as-is'...")
summary(aems450k.meta.th)
summary(aems450k.meta.esh)
summary(aems450k.meta.seh)
cat(" - of the data 'absolutely'...")
summary(abs(aems450k.meta.th))
summary(abs(aems450k.meta.esh))
summary(abs(aems450k.meta.seh))
# plaque *with* hospital
mean.betah = mean(aems450k.meta.esh)
sd.betah = sd(aems450k.meta.esh)
neg.cutoffh = mean.betah - 4 * sd.betah
pos.cutoffh = mean.betah + 4 * sd.betah
aems450k1.plothisth <- hist(aems450k.meta.esh[,1], breaks = 100, border = FALSE)
aems450k2.plothisth <- hist(aems450k.meta.esh[,2], breaks = 100, border = FALSE)
aems450k1.col <- adjustcolor(229,87,56, alpha.f = 0.25)
aems450k2.col <- adjustcolor(18,144,217, alpha.f = 0.25)
# PLOT FOR SANITY CHECK -- are distributions of the two cohorts similar?
pdf(paste0(QC_loc,"/",Today,".aems450k.cohorts.",EWAS_trait,".plaque.EffectSizeDistributions.pdf"),
width = 10, height = 10, onefile = TRUE)
par(mfrow = c(1,1), oma = c(0, 0, 2, 0))
plot(aems450k1.plothisth, col = aems450k1.col,
ylim = c(0,80000),
main = "Model corrected for age + sex + hospital",
xlab = bquote("effect size ["~beta~"]")) # first histogram
plot(aems450k2.plothisth, col = aems450k2.col, add = TRUE) # second
abline(v = mean.betah, col = "#595A5C", lty = 2, lwd = 1)
abline(v = neg.cutoffh, col = "#595A5C", lty = 2, lwd = 1)
abline(v = pos.cutoffh, col = "#595A5C", lty = 2, lwd = 1)
legend("topright",c("AEMS450K1", "AEMS450K2"),
col = c(aems450k1.col, aems450k2.col),
pch = 16, bty = "n")
mtext("Distribution of effect estimates", outer = TRUE, cex = 1.5)
dev.off()
par(mfrow = c(1,1), oma = c(0, 0, 0, 0))
# actual bacon correction - run in parallel
library(BiocParallel)
register(MulticoreParam(8))
# plaque *with* hospital
aems450k.meta.bcp.th <- bacon(teststatistics = aems450k.meta.th,
effectsizes = NULL, standarderrors = NULL,
verbose = TRUE)
aems450k.meta.bcp.bh <- bacon(teststatistics = NULL,
effectsizes = aems450k.meta.esh, standarderrors = aems450k.meta.seh,
verbose = TRUE)
cat(" - Bacon model result.")
aems450k.meta.bcp.th
aems450k.meta.bcp.bh
cat("\n - estimates of the mixture fit")
estimates(aems450k.meta.bcp.th)
estimates(aems450k.meta.bcp.bh)
cat("\n - the inflation")
inflation(aems450k.meta.bcp.th)
inflation(aems450k.meta.bcp.bh)
cat("\n - the bias")
bias(aems450k.meta.bcp.th)
bias(aems450k.meta.bcp.bh)
# PLOT FOR SANITY CHECK -- is the meta-analysis behaving as expected?
# plaque *with* hospital
pdf(paste0(QC_loc,"/",Today,".aems450k.cohorts.traces.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
traces(aems450k.meta.bcp.th)
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k.cohorts.posteriors.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
posteriors(aems450k.meta.bcp.th)
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k.cohorts.fittedbacon.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
fit(aems450k.meta.bcp.th, n = 100) # visualization of the fitting using the Gibbs Sampling algorithm
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k.cohorts.histogram.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
print(plot(aems450k.meta.bcp.th, type = "hist"))
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k.cohorts.qq.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
print(plot(aems450k.meta.bcp.th, type = "qq"))
dev.off()
cat("\n * Perform meta-analysis.")
# actual meta-analysis in plaque *with* hospital
aems450k.meta.bcpm.bh <- meta(aems450k.meta.bcp.bh)
aems450k.meta.pvalsbcpm.bh <- pval(aems450k.meta.bcpm.bh)
head(aems450k.meta.pvalsbcpm.bh[order(aems450k.meta.pvalsbcpm.bh[,1]),])
print(bacon::topTable(aems450k.meta.bcpm.bh))
pdf(paste0(QC_loc,"/",Today,".aems450k.meta.qq.",EWAS_trait,".plaque.pdf"),
width = 12, height = 12, onefile = TRUE)
print(plot(aems450k.meta.bcpm.bh, type = "qq"))
dev.off()
png(paste0(QC_loc,"/",Today,".aems450k.meta.qq.",EWAS_trait,".plaque.png"),
width = 800, height = 800)
print(plot(aems450k.meta.bcpm.bh, type = "qq"))
dev.off()
cat("\n * Uncorrected results.")
# plaque *with* hospital
aems450k.meta.pvalsph <- pval(aems450k.meta.bcpm.bh, corrected = FALSE)
head(aems450k.meta.pvalsph[order(aems450k.meta.pvalsph[,3]),], 10)
aems450k.meta.zph = qnorm(aems450k.meta.pvalsph[,3]/2)
aems450k.meta.lambdaph = round(median(aems450k.meta.zph^2)/0.4549364,3)
cat(paste0("\n - lambda is: [",aems450k.meta.lambdaph,"].")) # 1.2713076331
cat("\n * Corrected results.")
# plaque *with* hospital
aems450k.meta.pvalsbcph <- pval(aems450k.meta.bcpm.bh, corrected = TRUE)
head(aems450k.meta.pvalsbcph[order(aems450k.meta.pvalsbcph[,3]),], 10)
aems450k.meta.zbcph = qnorm(aems450k.meta.pvalsbcph[,3]/2)
aems450k.meta.lambdabcph = round(median(aems450k.meta.zbcph^2)/0.4549364,3)
cat(paste0("\n - lambda is: [",aems450k.meta.lambdabcph,"].")) # 1.2713076331
cat("\n * Annotating corrected results for plotting and other purposes.")
cat("\n - annotating...")
# plaque
# for some reason does this function take forever since the last update to R 3.4.3.
# infobcp <- cpgInfo(rownames(aems450k.meta.pvalsbcph), TxDb = "TxDb.Hsapiens.UCSC.hg19.knownGene")
infobcp <- as.data.frame(fread(paste0(OUT_loc, "/20171229.aems450k.meta.infobcp.hm450kannot.txt"),
header = TRUE, na.strings = "NA", dec = ".", verbose = TRUE, showProgress = TRUE), keep.rownames = TRUE)
rownames(infobcp) <- infobcp$V1
infobcp$V1 <- NULL
# head(infobcp)
cat("\n - merging with results...")
# add in effect sizes
temp.results <- merge(infobcp, es(aems450k.meta.bcpm.bh), by = "row.names")
rownames(temp.results) <- temp.results$Row.names
temp.results2 <- temp.results[, -which(names(temp.results) %in% c("Row.names"))]
names(temp.results2)[names(temp.results2) == "limmaB.aems450k1"] <- "EffectSize.aems450k1"
names(temp.results2)[names(temp.results2) == "limmaB.aems450k2"] <- "EffectSize.aems450k2"
names(temp.results2)[names(temp.results2) == "meta"] <- "EffectSize.meta"
# add in standard errors
temp.results3 <- merge(temp.results2, se(aems450k.meta.bcpm.bh), by = "row.names")
rownames(temp.results3) <- temp.results3$Row.names
temp.results4 <- temp.results3[, -which(names(temp.results3) %in% c("Row.names"))]
names(temp.results4)[names(temp.results4) == "limmaSE.aems450k1"] <- "SE.aems450k1"
names(temp.results4)[names(temp.results4) == "limmaSE.aems450k2"] <- "SE.aems450k2"
names(temp.results4)[names(temp.results4) == "meta"] <- "SE.meta"
# add in uncorrected p-values
temp.results5 <- merge(temp.results4, aems450k.meta.pvalsph, by = "row.names")
rownames(temp.results5) <- temp.results5$Row.names
temp.results6 <- temp.results5[, -which(names(temp.results5) %in% c("Row.names"))]
names(temp.results6)[names(temp.results6) == "limmaB.aems450k1"] <- "Pval.aems450k1"
names(temp.results6)[names(temp.results6) == "limmaB.aems450k2"] <- "Pval.aems450k2"
temp.results7 <- temp.results6[, -which(names(temp.results6) %in% c("meta"))]
# add in corrected p-values
aems450k.meta.resultsp <- merge(temp.results7, aems450k.meta.pvalsbcph, by = "row.names")
rownames(aems450k.meta.resultsp) <- aems450k.meta.resultsp$Row.names
names(aems450k.meta.resultsp)[names(aems450k.meta.resultsp) == "Row.names"] <- "CpG"
names(aems450k.meta.resultsp)[names(aems450k.meta.resultsp) == "limmaB.aems450k1"] <- "PvalCor.aems450k1"
names(aems450k.meta.resultsp)[names(aems450k.meta.resultsp) == "limmaB.aems450k2"] <- "PvalCor.aems450k2"
names(aems450k.meta.resultsp)[names(aems450k.meta.resultsp) == "meta"] <- "PvalCor.meta"
aems450k.meta.resultsp$Pval.meta <- pval(aems450k.meta.bcpm.bh, corrected = FALSE)[,3]
aems450k.meta.resultsp$PvalCorAdj.meta <- p.adjust(aems450k.meta.resultsp$PvalCor.meta, method = "BH")
head(aems450k.meta.resultsp)
str(aems450k.meta.resultsp)
dim(aems450k.meta.resultsp)
cat("\n - removing intermediate file...")
rm(temp.results, temp.results2, temp.results3, temp.results4, temp.results5, temp.results6, temp.results7)
cat("\n - recreating the chromosomes...")
list.chr <- levels(as.factor(aems450k.meta.resultsp$seqnames))[1:25]
# list.chr
aems450k.meta.resultspf <- aems450k.meta.resultsp[(aems450k.meta.resultsp$seqnames %in% list.chr), ]
aems450k.meta.resultspf$chr <- gsub("chr", "",aems450k.meta.resultspf$seqnames)
aems450k.meta.resultspf$chr <- gsub("X", "23",aems450k.meta.resultspf$chr)
aems450k.meta.resultspf$chr <- gsub("Y", "24",aems450k.meta.resultspf$chr)
aems450k.meta.resultspf$chr <- gsub("XY", "25",aems450k.meta.resultspf$chr)
aems450k.meta.resultspf$chr <- gsub("M", "26",aems450k.meta.resultspf$chr)
aems450k.meta.resultspf$chr <- as.numeric(aems450k.meta.resultspf$chr)
cat("\n * Annotating results with additional CpG information.")
cat("\n - annotating...")
# plaque
# install.packages.auto("IlluminaHumanMethylation450kmanifest")
# install.packages.auto("IlluminaHumanMethylation450kanno.ilmn12.hg19")
# ?IlluminaHumanMethylation450kmanifest
# data(IlluminaHumanMethylation450kmanifest)
# IlluminaHumanMethylation450kmanifest
# ?IlluminaHumanMethylation450kanno.ilmn12.hg19
data(IlluminaHumanMethylation450kanno.ilmn12.hg19)
IlluminaHumanMethylation450kanno.ilmn12.hg19
# data(Locations)
# data(Manifest)
# data(SNPs.137CommonSingle)
data(Islands.UCSC)
# data(Other)
# Locations
# Manifest
# SNPs.137CommonSingle
# Islands.UCSC
# Other
Islands.UCSC.forannotate <- as.data.frame(Islands.UCSC)
Islands.UCSC.forannotate$CpG <- rownames(Islands.UCSC.forannotate)
Other.forannotate <- as.data.frame(Other)
aems450k.meta.resultspfCGI.temp <- merge(aems450k.meta.resultspf, Islands.UCSC.forannotate, by = "row.names")
aems450k.meta.resultspfCGI.temp$Row.names <- NULL
aems450k.meta.resultspfCGI.temp$CpG.y <- NULL
rownames(aems450k.meta.resultspfCGI.temp) <- aems450k.meta.resultspfCGI.temp$CpG.x
names(aems450k.meta.resultspfCGI.temp)[names(aems450k.meta.resultspfCGI.temp) == "CpG.x"] <- "CpG"
aems450k.meta.resultspfCGI <- merge(aems450k.meta.resultspfCGI.temp, Other.forannotate, by = "row.names")
aems450k.meta.resultspfCGI$Row.names <- NULL
rownames(aems450k.meta.resultspfCGI) <- aems450k.meta.resultspfCGI$CpG
head(aems450k.meta.resultspfCGI)
str(aems450k.meta.resultspfCGI)
dim(aems450k.meta.resultspfCGI)
rm(aems450k.meta.resultspf, Islands.UCSC.forannotate, Other.forannotate, Islands.UCSC, aems450k.meta.resultspfCGI.temp)
cat("\n* Plotting...")
cat("\n - QQ-plot in plaque...")
png(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.png"),
width = 1024, height = 800)
pdf(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.pdf"),
width = 12, height = 10, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.ps"),
width = 12, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.tiff"),
width = 1024, height = 800, units = "px", pointsize = 12,
compression = "none", bg = "transparent",
type = "quartz")
par(mfrow = c(1,2), oma = c(0, 0, 2, 0), mar = c(5, 6, 4, 2) + 0.1)
qq(aems450k.meta.resultspfCGI$Pval.meta,
main = bquote("Uncorrected " ~ lambda == .(aems450k.meta.lambdaph)),
col = "#1290D9", pch = 16, xlim = c(0,8), ylim = c(0,25),
cex = 1.75, cex.lab = 1.75, cex.axis = 1.75, cex.main = 1.50,
bty = "n")
abline(0, 1, col = "#E55738")
qq(aems450k.meta.resultspfCGI$PvalCor.meta,
main = bquote("Corrected" ~ lambda == .(aems450k.meta.lambdabcph)),
col = "#1290D9", pch = 16, xlim = c(0,8), ylim = c(0,25),
cex = 1.75, cex.lab = 1.75, cex.axis = 1.75, cex.main = 1.50,
bty = "n")
abline(0, 1, col = "#E55738")
mtext("QQ-plots", outer = TRUE, cex = 2.0)
dev.off()
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mar = c(5, 4, 4, 2) + 0.1)
cat("\n - Manhattan-plot in plaque...")
# plaque
# meta-analyse
# head(aems450k.meta.resultspfCGI[order(aems450k.meta.resultspfCGI[,31]),], 10)
# example of known smoking-related CpG
# (other) top hits: AHRR (cg25648203, cg05575921, cg03991871, cg12806681, cg02385153
# resp. p = 5.823467e-19, p = 8.715543e-15, p = 1.101234e-10, p = 1.162238e-07, p = 1.705505e-07, chr5),
# NTHL1 (cg16650073, cg01815912, p = 7.853257e-10, p = 1.103098e-06, chr16),
# ALPI (cg05951221, p = 1.172635e-07, chr2)
# ITPK1 (cg05284742, p = 3.044496e-07, chr14),
# CRLF1 (cg22702618, p = 1.103098e-06, chr1)
# EEFSEC (cg19505196, p = 5.787613e-07, chr3)
png(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.png"),
width = 1920, height = 1080)
pdf(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.pdf"),
width = 28, height = 8, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.ps"),
width = 28, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.tiff"),
width = 1920, height = 1080, units = "px", pointsize = 12,
compression = "none", bg = "transparent",
type = "quartz")
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mai = c(1, 1, 1, 0))
ahrr <- aems450k.meta.resultspfCGI$CpG[grep("AHRR", aems450k.meta.resultspfCGI$SYMBOL)]
itpk1 <- aems450k.meta.resultspfCGI$CpG[grep("ITPK1", aems450k.meta.resultspfCGI$SYMBOL)]
hilight.top <- c(ahrr, itpk1)
manhattan.uithof(aems450k.meta.resultspfCGI, chr = "chr", bp = "start", p = "PvalCor.meta", snp = "CpG",
highlight = hilight.top, suggestiveline = FALSE,
genomewideline = FALSE,
cex = 2.5, cex.axis = 1.75, cex.lab = 1.75,
col = uithof_color, ylim = c(0,20),
chrlabs = c(1:22, "X", "Y"),
annotatePval = NULL, annotateTop = NULL)
dev.off()
rm(hilight.top, ahrr, itpk1)
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mai = c(1, 1, 1, 1))
cat("\n* Filter on *REPLICATED* signal only...")
aems450k.meta.resultspfCGIQC <- subset(aems450k.meta.resultspfCGI, PvalCor.aems450k2 < 0.05)
dim(aems450k.meta.resultspfCGIQC)
aems450k.meta.zph.QC = qnorm(aems450k.meta.resultspfCGIQC$Pval.meta/2)
aems450k.meta.lambdaph.QC = round(median(aems450k.meta.zph.QC^2)/0.4549364,3) # 1.293
aems450k.meta.zph.cor.QC = qnorm(aems450k.meta.resultspfCGIQC$PvalCor.meta/2)
aems450k.meta.lambdaph.cor.QC = round(median(aems450k.meta.zph.cor.QC^2)/0.4549364,3) # 3.273
cat("\n* Plotting...")
cat("\n - QQ-plot in plaque...")
png(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.replicated.png"),
width = 1024, height = 800)
pdf(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.replicated.pdf"),
width = 12, height = 10, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.replicated.ps"),
width = 12, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.QQPlot.replicated.tiff"),
width = 1024, height = 800, units = "px", pointsize = 12,
compression = "none", bg = "transparent",
type = "quartz")
par(mfrow = c(1,2), oma = c(0, 0, 2, 0), mar = c(5, 6, 4, 2) + 0.1)
qq(aems450k.meta.resultspfCGIQC$Pval.meta,
main = bquote("Uncorrected " ~ lambda == .(aems450k.meta.lambdaph.QC)),
# main = bquote("Uncorrected "),
col = "#1290D9", pch = 16, xlim = c(0,8), ylim = c(0,25),
cex = 1.75, cex.lab = 1.75, cex.axis = 1.75, cex.main = 1.50,
bty = "n")
abline(0, 1, col = "#E55738")
qq(aems450k.meta.resultspfCGIQC$PvalCor.meta,
main = bquote("Corrected" ~ lambda == .(aems450k.meta.lambdaph.cor.QC)),
# main = bquote("Corrected"),
col = "#1290D9", pch = 16, xlim = c(0,8), ylim = c(0,25),
cex = 1.75, cex.lab = 1.75, cex.axis = 1.75, cex.main = 1.50,
bty = "n")
abline(0, 1, col = "#E55738")
mtext("QQ-plots", outer = TRUE, cex = 2.0)
dev.off()
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mar = c(5, 4, 4, 2) + 0.1)
cat("\n - Manhattan-plot in plaque...")
# plaque
# head(aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC[,31]),], 10)
# example of known smoking-related CpG
# (other) top hits: AHRR (cg25648203, cg05575921, cg03991871, cg12806681, cg02385153
# resp. p = 5.823467e-19, p = 8.715543e-15, p = 1.101234e-10, p = 1.162238e-07, p = 1.705505e-07, chr5),
# NTHL1 (cg16650073, cg01815912, p = 7.853257e-10, p = 1.103098e-06, chr16),
# ALPI (cg05951221, p = 1.172635e-07, chr2)
# ITPK1 (cg05284742, p = 3.044496e-07, chr14),
# CRLF1 (cg22702618, p = 1.103098e-06, chr1)
# EEFSEC (cg19505196, p = 5.787613e-07, chr3)
png(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.replicated.png"),
width = 1920, height = 1080)
pdf(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.replicated.pdf"),
width = 28, height = 8, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.replicated.ps"),
width = 28, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.ManhattanPlot.replicated.tiff"),
width = 1920, height = 1080, units = "px", pointsize = 12,
compression = "none", bg = "transparent",
type = "quartz")
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mai = c(1, 1, 1, 0))
ahrr <- aems450k.meta.resultspfCGIQC$CpG[grep("AHRR", aems450k.meta.resultspfCGIQC$SYMBOL)]
itpk1 <- aems450k.meta.resultspfCGIQC$CpG[grep("ITPK1", aems450k.meta.resultspfCGIQC$SYMBOL)]
hilight.top <- c(ahrr, itpk1)
manhattan.uithof(aems450k.meta.resultspfCGIQC, chr = "chr", bp = "start", p = "PvalCor.meta", snp = "CpG",
highlight = hilight.top, suggestiveline = FALSE,
genomewideline = FALSE,
cex = 2.50, cex.axis = 1.75, cex.lab = 1.75,
col = uithof_color, ylim = c(0,20),
chrlabs = c(1:22, "X", "Y"),
annotatePval = NULL, annotateTop = NULL)
dev.off()
rm(hilight.top, ahrr, itpk1)
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mai = c(1, 1, 1, 1))
cat("\n > in plaque...")
pdf(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.TopCor.pdf"))
top.symbol <- aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC$PvalCor.meta), "SYMBOL"][1]
top <- aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC$PvalCor.meta), "CpG"][1]
aems450k1.x <- aems450k1.dataph[rownames(aems450k1.dataph) == top, ]
aems450k1.y <- aems450k1.designph[, 2]
aems450k1.pearsonR <- signif(cor(aems450k1.x, aems450k1.y), 3)
aems450k2.x <- aems450k2.dataph[rownames(aems450k2.dataph) == top, ]
aems450k2.y <- aems450k2.designph[, 2]
aems450k2.pearsonR <- signif(cor(aems450k2.x, aems450k2.y), 3)
par(mfrow = c(1,2), oma = c(0, 0, 2, 0), mar = c(5, 5, 4, 0) + 0.1)
# AEMS450K1
boxplot(aems450k1.x~aems450k1.y, main = bquote(Pearson~r^2 == .(aems450k1.pearsonR)),
sub = "(AEMS450K1)",
cex.sub = 0.8,
xlab = "Current smoker",
ylab = bquote(.(top)~" -"~italic(.(top.symbol))),
plot = TRUE, notch = FALSE, outline = TRUE,
names = c("no", "yes"),
whisklty = 1, staplelty = 0, # no whisker-ends, but with lines
frame = FALSE)
stripchart(aems450k1.x~aems450k1.y, vertical = TRUE,
method = "jitter", add = TRUE, pch = 20, col = "#1290D9")
# AEMS450K2
boxplot(aems450k2.x~aems450k2.y, main = bquote(Pearson~r^2 == .(aems450k2.pearsonR)),
sub = "(AEMS450K2)",
cex.sub = 0.8,
xlab = "Current smoker",
# ylab = bquote(.(top)~" methylation near "~italic(.(top.symbol))),
ylab = "",
plot = TRUE, notch = FALSE, outline = TRUE,
names = c("no", "yes"),
whisklty = 1, staplelty = 0, # no whisker-ends, but with lines
frame = FALSE)
stripchart(aems450k2.x~aems450k2.y, vertical = TRUE,
method = "jitter", add = TRUE, pch = 20, col = "#1290D9")
mtext("Top correlated CpG", outer = TRUE, cex = 1.25)
dev.off()
rm(top.data, top.symbol, top,
aems450k1.x, aems450k1.y, aems450k1.pearsonR,
aems450k2.x, aems450k2.y, aems450k2.pearsonR)
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mar = c(5, 4, 4, 2) + 0.1)
pdf(paste0(PLOT_loc,"/",Today,".aems450k.meta.",EWAS_trait,".plaque.Top4.pdf"), paper = "a4r",
width = 12, height = 8)
# head(aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC[,31]),], 4)
# example of known smoking-related CpG
# No. CpG seqnames SYMBOL PvalCor.meta PvalCorAdj.meta
# 1 cg05575921 chr5 AHRR 1.713638e-13 3.796429e-08
# 2 cg03991871 chr5 AHRR 2.896744e-11 4.278337e-06
# 3 cg12806681 chr5 AHRR 5.949100e-09 5.271902e-04
# 4 cg05284742 chr14 ITPK1 2.045460e-07 1.510517e-02
top.ahrr1 <- aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC$PvalCor.meta), "CpG"][1]
top.ahrr2 <- aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC$PvalCor.meta), "CpG"][2]
top.ahrr3 <- aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC$PvalCor.meta), "CpG"][3]
top.itpk1 <- aems450k.meta.resultspfCGIQC[order(aems450k.meta.resultspfCGIQC$PvalCor.meta), "CpG"][4]
par(mfrow = c(2,2), mar = c(5,6,4,0), oma = c(0, 0, 2, 0))
# AHRR 1 - 3
x.ahrr1 <- aems450k1.dataph[rownames(aems450k1.dataph) == top.ahrr1, ]
y.ahrr1 <- aems450k1.designph[, 2]
pearsonR.ahrr1 <- signif(cor(x.ahrr1, y.ahrr1), 3)
x.ahrr2 <- aems450k1.dataph[rownames(aems450k1.dataph) == top.ahrr2, ]
y.ahrr2 <- aems450k1.designph[, 2]
pearsonR.ahrr2 <- signif(cor(x.ahrr2, y.ahrr2), 3)
x.ahrr3 <- aems450k1.dataph[rownames(aems450k1.dataph) == top.ahrr3, ]
y.ahrr3 <- aems450k1.designph[, 2]
pearsonR.ahrr3 <- signif(cor(x.ahrr3, y.ahrr3), 3)
boxplot(x.ahrr1~y.ahrr1, main = bquote(Pearson~r^2 == .(pearsonR.ahrr1)),
xlab = "Current Smoking",
ylab = bquote(.(top.ahrr1)~" -"~italic("AHRR")),
# ylim = c(0, 4.5),
plot = TRUE, notch = FALSE, outline = TRUE,
cex = 1.25, cex.lab = 1.50, cex.axis = 1.50, cex.main = 2.0,
names = c("no", "yes"),
whisklty = 1, staplelty = 0, # no whisker-ends, but with lines
frame = FALSE)
stripchart(x.ahrr1~y.ahrr1, vertical = TRUE,
method = "jitter", add = TRUE, pch = 20, col = "#1290D9")
boxplot(x.ahrr2~y.ahrr2, main = bquote(Pearson~r^2 == .(pearsonR.ahrr2)),
xlab = "Current Smoking",
ylab = bquote(.(top.ahrr2)~" -"~italic("AHRR")),