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20180614.aems450k1.analysis.ewas.smoking.R
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20180614.aems450k1.analysis.ewas.smoking.R
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cat("===========================================================================================
ANALYSIS ATHERO-EXPRESS METHYLATION STUDIES 450K 1
Version: v2.9
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) EWAS on smoking.
Based on DNAmArray:
- https://molepi.github.io/DNAmArray_workflow/index.html
- https://github.com/molepi/DNAmArray
Minimum requirements: R version 3.4.1 (2017-06-30) -- 'Single Candle', 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 = \"https://cloud.r-project.org/\")", 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("limma")
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.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"
# INP_AE_loc = paste0(ROOT_loc, "/PLINK/_AE_Originals")
### MacBook
ROOT_loc = "/Users/swvanderlaan"
INP_AE_loc = paste0(ROOT_loc, "/PLINK/_AE_Originals")
### HPC
# ROOT_loc = "/hpc/dhl_ec"
# INP_AE_loc = paste0(ROOT_loc, "/data/_ae_originals")
### SOME VARIABLES WE NEED DOWN THE LINE
PROJECTDATASET = "AEMS450K1"
PROJECTNAME = "smoking"
EWAS_trait = "SmokerCurrent" # Phenotype
INP_AEMS450K1_loc = paste0(INP_AE_loc, "/AEMS450K1")
INP_AEMS450K2_loc = paste0(INP_AE_loc, "/AEMS450K2")
### Mac
EPIGENETICS_loc = paste0(ROOT_loc, "/PLINK/analyses/epigenetics")
# ### HPC
# EPIGENETICS_loc = paste0(ROOT_loc, "/svanderlaan/projects/epigenetics")
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 1 DATASETS")
setwd(INP_loc)
list.files()
cat(paste0("\n\n* Load ",PROJECTDATASET," data..."))
cat("\n - loading B/Mvalues of blood samples...")
load(paste0(INP_AEMS450K1_loc,"/20171229.aems450k1.BvaluesQCIMP.blood.RData"))
load(paste0(INP_AEMS450K1_loc,"/20171229.aems450k1.MvaluesQCIMP.blood.RData"))
cat("\n - loading B/Mvalues of plaque samples...")
load(paste0(INP_AEMS450K1_loc,"/20171229.aems450k1.BvaluesQCIMP.plaque.RData"))
load(paste0(INP_AEMS450K1_loc,"/20171229.aems450k1.MvaluesQCIMP.plaque.RData"))
cat("----------------------------------------------------------------------------")
cat(paste0("\n[ EPIGENOME-WIDE ASSOCIATION STUDIES on ",EWAS_trait," in PLAQUE in ",PROJECTDATASET," ]"))
# Reference: https://molepi.github.io/DNAmArray_workflow/06_EWAS.html
cat("\n* Sannity checking the data.")
cat("\n - for blood...")
pdf(paste0(QC_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.MethylationDensity.pdf"),
width = 12, height = 8, onefile = TRUE)
par(mfrow = c(1,2), oma = c(0, 0, 2, 0))
densityPlot(assays(aems450k1.BvaluesQCblood)$data, sampGroups = aems450k1.BvaluesQCblood$SmokerCurrent, main = "Beta-values",
legend = FALSE,
xlab = "Beta-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k1.BvaluesQCblood$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
densityPlot(assays(aems450k1.MvaluesQCblood)$data, sampGroups = aems450k1.MvaluesQCblood$SmokerCurrent, main = "M-values",
legend = FALSE,
xlab = "M-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k1.MvaluesQCblood$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
mtext(paste0("Overall methylation density (blood, ",PROJECTDATASET,")"), outer = TRUE, cex = 1.5)
par(mfrow = c(1,1), oma = c(0,0,0,0))
dev.off()
cat("\n - for plaque...")
pdf(paste0(QC_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.MethylationDensity.pdf"),
width = 12, height = 8, onefile = TRUE)
par(mfrow = c(1,2), oma = c(0, 0, 2, 0))
densityPlot(assays(aems450k1.BvaluesQCplaque)$data, sampGroups = aems450k1.BvaluesQCplaque$SmokerCurrent, main = "Beta-values",
legend = FALSE,
xlab = "Beta-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k1.BvaluesQCplaque$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
densityPlot(assays(aems450k1.MvaluesQCplaque)$data, sampGroups = aems450k1.MvaluesQCplaque$SmokerCurrent, main = "M-values",
legend = FALSE,
xlab = "M-values",
pal = c("#9FC228", "#E55738"),
bty = "n")
legend("topright", legend = levels(factor(aems450k1.MvaluesQCplaque$SmokerCurrent)),
text.col = c("#9FC228", "#E55738"),
lty = 1, lwd = 1, col = c("#9FC228", "#E55738"),
bty = "n")
mtext(paste0("Overall methylation density (plaque, ",PROJECTDATASET,")"), outer = TRUE, cex = 1.5)
par(mfrow = c(1,1), oma = c(0,0,0,0))
dev.off()
cat("\nRemoving BvaluesQCIMP objects - as we don't use these anymore.\n")
rm(aems450k1.BvaluesQCplaque, aems450k1.BvaluesQCblood)
cat("===========================================================================================")
cat("\n[ CONTINUE EPIGENOME-WIDE ASSOCIATION STUDIES on ",EWAS_trait," in PLAQUE in ",PROJECTDATASET," ]")
cat("\n* Setup the analysis.")
require(FDb.InfiniumMethylation.hg19)
feats <- features(FDb.InfiniumMethylation.hg19)
chr.list <- levels(seqnames(feats))
regions <- feats[seqnames(feats) %in% chr.list]
cat("\n - Setup the model, first covariate is the variable/phenotype of interest.")
#blood
MvaluesQCbloodClean <- aems450k1.MvaluesQCblood
metadata(MvaluesQCbloodClean)$formula <- ~SmokerCurrent + Sample_Sex + Age + Hospital
#plaque
MvaluesQCplaqueClean <- aems450k1.MvaluesQCplaque
metadata(MvaluesQCplaqueClean)$formula <- ~SmokerCurrent + Sample_Sex + Age + Hospital
cat("\n - Next, we extract those samples having a complete set of covariates.
Notice that we subset the SummarizedExperiment-object!")
#blood
covariates <- get_all_vars(metadata(MvaluesQCbloodClean)$formula, data = colData(MvaluesQCbloodClean))
nasb <- apply(covariates, 1, anyNA)
MvaluesQCbloodClean <- MvaluesQCbloodClean[, !nasb]
# plaque
covariatesp <- get_all_vars(metadata(MvaluesQCplaqueClean)$formula, data = colData(MvaluesQCplaqueClean))
nasp <- apply(covariatesp, 1, anyNA)
MvaluesQCplaqueClean <- MvaluesQCplaqueClean[, !nasp]
cat("\n - Remove probes on the X and Y chromosomes and probes containing
SNPs or does mapping to multiple locations (ref: W. Zhou, P.W. Laird, and H. Shen. 2016. Nucleic Acids Res.).")
cat("\n > first chromosome X and Y... [NOTE: we skip this as we are interested in X, Y, and M(t)]")
# blood
# MvaluesQCbloodClean <- MvaluesQCbloodClean[!(seqnames(MvaluesQCbloodClean) %in% c("chrX","chrY")),]
# plaque
# MvaluesQCplaqueClean <- MvaluesQCplaqueClean[!(seqnames(MvaluesQCplaqueClean) %in% c("chrX","chrY")),]
cat("\n > next probes with SNPs...")
data(hm450.manifest.pop.GoNL) ##From DNAmArray
#hm450.manifest.pop.GoNL
hm450.manifest.pop.GoNL <- hm450.manifest.pop.GoNL[!is.na(hm450.manifest.pop.GoNL$MASK.general.GoNL) &
hm450.manifest.pop.GoNL$MASK.general.GoNL == TRUE, ]
# blood
MvaluesQCbloodClean <- MvaluesQCbloodClean[!(names(MvaluesQCbloodClean) %in% names(hm450.manifest.pop.GoNL)),]
# plaque
MvaluesQCplaqueClean <- MvaluesQCplaqueClean[!(names(MvaluesQCplaqueClean) %in% names(hm450.manifest.pop.GoNL)),]
cat("\n > we started with:")
dim(aems450k1.MvaluesQCblood) # 483731 CpGs, 93 samples
dim(aems450k1.MvaluesQCplaque) # 483731 CpGs, 485 samples
cat("\n > we end up with:")
dim(MvaluesQCbloodClean) # 443370 CpGs, 93 samples
dim(MvaluesQCplaqueClean) # 443370 CpGs, 477 samples
cat("\n* Run the analysis.")
library("limma")
# blood
design <- model.matrix(metadata(MvaluesQCbloodClean)$formula, data = colData(MvaluesQCbloodClean))
data <- assays(MvaluesQCbloodClean)$data
fit <- lmFit(data, design)
cat(" - calculate T-statistic = beta/standard error...")
tstat <- fit$coef/fit$stdev.unscaled/fit$sigma
effectsize <- fit$coef[,2]
cat(" - standard error is stdev.unscaled/sigma in limma prior to eBayes()...")
SE <- fit$stdev.unscaled[,2]/fit$sigma
cat(" - calculate p-value from T-statistic, and adjust using FDR (Benjamin-Hochberg)...")
pval <- 2*pnorm(-abs(tstat[,2]))
padj <- p.adjust(sort(pval, decreasing = FALSE), method = "BH")
head(padj[padj < 0.05])
# plaque
designp <- model.matrix(metadata(MvaluesQCplaqueClean)$formula, data = colData(MvaluesQCplaqueClean))
datap <- assays(MvaluesQCplaqueClean)$data
fitp <- lmFit(datap, designp)
cat(" - calculate T-statistic = beta/standard error...")
tstatp <- fitp$coef/fitp$stdev.unscaled/fitp$sigma
effectsizep <- fitp$coef[,2]
cat(" - standard error is stdev.unscaled/sigma in limma prior to eBayes()...")
SEp <- fitp$stdev.unscaled[,2]/fitp$sigma
cat(" - calculate p-value from T-statistic, and adjust using FDR (Benjamin-Hochberg)...")
pvalp <- 2*pnorm(-abs(tstatp[,2]))
padjp <- p.adjust(sort(pvalp, decreasing = FALSE), method = "BH")
head(padjp[padjp < 0.05])
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")
# blood
tstats <- cbind(limmaT = tstat[,2], limmaB = effectsize, limmaSE = SE)
# plaque
tstatsp <- cbind(limmaT = tstatp[,2], limmaB = effectsizep, limmaSE = SEp)
library(BiocParallel)
register(MulticoreParam(2))
# blood
bc <- bacon(teststatistics = tstats[,1], effectsizes = tstats[,2], standarderrors = tstats[,3],
verbose = TRUE)
cat("\n* Bacon model result.")
bc
cat("\n - estimates of the mixture fit")
estimates(bc)
cat("\n - the inflation")
inflation(bc)
cat("\n - the bias")
bias(bc)
# PLOT FOR SANITY CHECK -- is the meta-analysis behaving as expected?
# blood *with* hospital
pdf(paste0(QC_loc,"/",Today,".aems450k1.traces.",EWAS_trait,".blood.pdf"),
width = 10, height = 10, onefile = TRUE)
traces(bc)
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k1.posteriors.",EWAS_trait,".blood.pdf"),
width = 10, height = 10, onefile = TRUE)
posteriors(bc)
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k1.fittedbacon.",EWAS_trait,".blood.pdf"),
width = 10, height = 10, onefile = TRUE)
fit(bc, n = 100) # visualization of the fitting using the Gibbs Sampling algorithm
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k1.histogram.",EWAS_trait,".blood.pdf"),
width = 10, height = 10, onefile = TRUE)
print(plot(bc, type = "hist"))
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k1.qq.",EWAS_trait,".blood.pdf"),
width = 10, height = 10, onefile = TRUE)
print(plot(bc, type = "qq"))
dev.off()
# plaque
bcp <- bacon(teststatistics = tstatsp[,1], effectsizes = tstatsp[,2], standarderrors = tstatsp[,3],
verbose = TRUE)
cat("\n* Bacon model result.")
bcp
cat("\n - estimates of the mixture fit")
estimates(bcp)
cat("\n - the inflation")
inflation(bcp)
cat("\n - the bias")
bias(bcp)
# PLOT FOR SANITY CHECK -- is the meta-analysis behaving as expected?
# plaque *with* hospital
pdf(paste0(QC_loc,"/",Today,".aems450k2.traces.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
traces(bcp)
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k2.posteriors.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
posteriors(bcp)
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k2.fittedbacon.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
fit(bcp, n = 100) # visualization of the fitting using the Gibbs Sampling algorithm
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k2.histogram.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
print(plot(bcp, type = "hist"))
dev.off()
pdf(paste0(QC_loc,"/",Today,".aems450k2.qq.",EWAS_trait,".plaque.pdf"),
width = 10, height = 10, onefile = TRUE)
print(plot(bcp, type = "qq"))
dev.off()
cat("\n * Uncorrected results.")
# cat("\n - in blood...")
pvalsb <- pval(bc, corrected = FALSE)
head(pvalsb[order(pvalsb[,1]),])
zb = qnorm(pvalsb[,1]/2)
lambdab = round(median(zb^2)/0.4549364,3)
cat(paste0("\n - lambda is: [",lambdab,"].")) # 1.199
cat("\n - in plaque...")
pvalsp <- pval(bcp, corrected = FALSE)
head(pvalsp[order(pvalsp[,1]),])
zp = qnorm(pvalsp[,1]/2)
lambdap = round(median(zp^2)/0.4549364,3)
cat(paste0("\n - lambda is: [",lambdap,"].")) # 1.717
cat("\n * Corrected results.")
cat("\n - in blood...")
pvalsbcb <- pval(bc, corrected = TRUE)
head(pvalsbcb[order(pvalsbcb[,1]),])
zbcb = qnorm(pvalsbcb[,1]/2)
lambdabcb = round(median(zbcb^2)/0.4549364,3)
cat(paste0("\n - lambda is: [",lambdabcb,"].")) # 1.097
cat("\n - in plaque...")
pvalsbcp <- pval(bcp, corrected = TRUE)
head(pvalsbcp[order(pvalsbcp[,1]),])
zbcp = qnorm(pvalsbcp[,1]/2)
lambdabcp = round(median(zbcp^2)/0.4549364,3)
cat(paste0("\n - lambda is: [",lambdabcp,"].")) # 1.228
rm(zb, zbcb, zp, zbcp)
cat("\n * Annotating corrected results for plotting and other purposes.")
cat("\n - annotating...")
# for some reason does this function take forever since the last update to R 3.4.3.
# we do this via the HPC, because there you can get more processor power and memory
# blood
# infobcb <- cpgInfo(rownames(pvalsbcb), TxDb = "TxDb.Hsapiens.UCSC.hg19.knownGene")
# head(infobcb)
# plaque
# infobcp <- cpgInfo(rownames(pvalsbcp), TxDb = "TxDb.Hsapiens.UCSC.hg19.knownGene")
# head(infobcp)
infobcb <- as.data.frame(fread(paste0(OUT_loc, "/20180115.aems450k1.infobcb.hm450kannot.txt"),
header = TRUE, na.strings = "NA", dec = ".", verbose = TRUE, showProgress = TRUE), keep.rownames = TRUE)
rownames(infobcb) <- infobcb$V1
infobcb$V1 <- NULL
# head(infobcb)
infobcp <- as.data.frame(fread(paste0(OUT_loc, "/20180115.aems450k1.infobcp.hm450kannot.txt"),
header = TRUE, na.strings = "NA", dec = ".", verbose = TRUE, showProgress = TRUE), keep.rownames = TRUE)
rownames(infobcp) <- infobcp$V1
infobcp$V1 <- NULL
# head(infobcp)
cat("\n - merging with results...")
cat("\n > in blood...")
temp.results <- merge(infobcb, tstats, by = "row.names")
rownames(temp.results) <- temp.results$Row.names
temp.results2 <- temp.results[, -which(names(temp.results) %in% c("Row.names"))]
temp.results3 <- merge(temp.results2, pvalsb, by = "row.names")
names(temp.results3)[names(temp.results3) == "V1"] <- "Pval_limma"
resultsb <- merge(temp.results3, pvalsbcb, by.x = "Row.names", by.y = "row.names")
names(resultsb)[names(resultsb) == "V1"] <- "Pval_limma_bacon"
names(resultsb)[names(resultsb) == "Row.names"] <- "CpG"
resultsb$Pval_limma_baconAdj <- p.adjust(resultsb$Pval_limma_bacon, method = "BH")
rownames(resultsb) <- resultsb$CpG
utils::head(resultsb)
str(resultsb)
dim(resultsb)
cat("\n > in plaque...")
temp.results <- merge(infobcp, tstatsp, by = "row.names")
rownames(temp.results) <- temp.results$Row.names
temp.results2 <- temp.results[, -which(names(temp.results) %in% c("Row.names"))]
temp.results3 <- merge(temp.results2, pvalsp, by = "row.names")
names(temp.results3)[names(temp.results3) == "V1"] <- "Pval_limma"
resultsp <- merge(temp.results3, pvalsbcp, by.x = "Row.names", by.y = "row.names")
names(resultsp)[names(resultsp) == "V1"] <- "Pval_limma_bacon"
names(resultsp)[names(resultsp) == "Row.names"] <- "CpG"
resultsp$Pval_limma_baconAdj <- p.adjust(resultsp$Pval_limma_bacon, method = "BH")
rownames(resultsp) <- resultsp$CpG
utils::head(resultsp)
str(resultsp)
dim(resultsp)
cat("\n - removing intermediate file...")
rm(temp.results, temp.results2, temp.results3)
cat("\n - recreating the chromosomes...")
cat("\n > in blood...")
list.chr <- levels(as.factor(resultsb$seqnames))[1:25]
# list.chr
resultsbf <- resultsb[(resultsb$seqnames %in% list.chr), ]
resultsbf$chr <- gsub("chr", "",resultsbf$seqnames)
resultsbf$chr <- gsub("X", "23",resultsbf$chr)
resultsbf$chr <- gsub("Y", "24",resultsbf$chr)
resultsbf$chr <- gsub("XY", "25",resultsbf$chr)
resultsbf$chr <- gsub("M", "26",resultsbf$chr)
resultsbf$chr <- as.numeric(resultsbf$chr)
cat("\n > in plaque...")
list.chr <- levels(as.factor(resultsp$seqnames))[1:25]
# list.chr
resultspf <- resultsp[(resultsp$seqnames %in% list.chr), ]
resultspf$chr <- gsub("chr", "",resultspf$seqnames)
resultspf$chr <- gsub("X", "23",resultspf$chr)
resultspf$chr <- gsub("Y", "24",resultspf$chr)
resultspf$chr <- gsub("XY", "25",resultspf$chr)
resultspf$chr <- gsub("M", "26",resultspf$chr)
resultspf$chr <- as.numeric(resultspf$chr)
cat("\n * Annotating results with additional CpG information.")
cat("\n - annotating...")
# plaque
# install.packages.auto("IlluminaHumanMethylation450kmanifest")
# install.packages.auto("IlluminaHumanMethylation450kanno.ilmn12.hg19")
# ?IlluminaHumanMethylation450kmanifest
# data(IlluminaHumanMethylation450kmanifest)
# IlluminaHumanMethylation450kmanifest
# ?IlluminaHumanMethylation450kanno.ilmn12.hg19
data(IlluminaHumanMethylation450kanno.ilmn12.hg19)
IlluminaHumanMethylation450kanno.ilmn12.hg19
data(Locations)
# data(Manifest)
data(SNPs.137CommonSingle)
data(Islands.UCSC)
data(Other)
Locations
# Manifest
SNPs.137CommonSingle
Islands.UCSC
Other
Islands.UCSC.forannotate <- as.data.frame(Islands.UCSC)
Locations.forannotate <- as.data.frame(Locations)
SNPs.137CommonSingle.forannotate <- as.data.frame(SNPs.137CommonSingle)
Other.forannotate <- as.data.frame(Other)
Islands.UCSC.forannotate$CpG <- rownames(Islands.UCSC.forannotate)
Locations.forannotate$CpG <- rownames(Locations.forannotate)
SNPs.137CommonSingle.forannotate$CpG <- rownames(SNPs.137CommonSingle.forannotate)
Other.forannotate$CpG <- rownames(Other.forannotate)
# BLOOD
aems450k1.resultsbfCGI.temp <- merge(resultsbf, Islands.UCSC.forannotate, by = "row.names")
aems450k1.resultsbfCGI.temp$Row.names <- NULL
aems450k1.resultsbfCGI.temp$CpG.y <- NULL
rownames(aems450k1.resultsbfCGI.temp) <- aems450k1.resultsbfCGI.temp$CpG.x
names(aems450k1.resultsbfCGI.temp)[names(aems450k1.resultsbfCGI.temp) == "CpG.x"] <- "CpG"
aems450k1.resultsbfCGI <- merge(aems450k1.resultsbfCGI.temp, Other.forannotate, by = "row.names")
aems450k1.resultsbfCGI$Row.names <- NULL
rownames(aems450k1.resultsbfCGI) <- aems450k1.resultsbfCGI$CpG.x
aems450k1.resultsbfCGI$CpG.y <- NULL
names(aems450k1.resultsbfCGI)[names(aems450k1.resultsbfCGI) == "CpG.x"] <- "CpG"
utils::head(aems450k1.resultsbfCGI)
str(aems450k1.resultsbfCGI)
dim(aems450k1.resultsbfCGI)
rm(resultsbf, Other, SNPs.137CommonSingle, Locations, Islands.UCSC, aems450k1.resultsbfCGI.temp)
# PLAQUE
aems450k1.resultspfCGI.temp <- merge(resultspf, Islands.UCSC.forannotate, by = "row.names")
aems450k1.resultspfCGI.temp$Row.names <- NULL
aems450k1.resultspfCGI.temp$CpG.y <- NULL
rownames(aems450k1.resultspfCGI.temp) <- aems450k1.resultspfCGI.temp$CpG.x
names(aems450k1.resultspfCGI.temp)[names(aems450k1.resultspfCGI.temp) == "CpG.x"] <- "CpG"
aems450k1.resultspfCGI <- merge(aems450k1.resultspfCGI.temp, Other.forannotate, by = "row.names")
aems450k1.resultspfCGI$Row.names <- NULL
rownames(aems450k1.resultspfCGI) <- aems450k1.resultspfCGI$CpG.x
aems450k1.resultspfCGI$CpG.y <- NULL
names(aems450k1.resultspfCGI)[names(aems450k1.resultspfCGI) == "CpG.x"] <- "CpG"
utils::head(aems450k1.resultspfCGI)
str(aems450k1.resultspfCGI)
dim(aems450k1.resultspfCGI)
rm(resultspf, aems450k1.resultspfCGI.temp)
cat("\n* Plotting...")
cat("\n - QQ-plot in blood...")
png(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.QQPlot.png"),
width = 1024, height = 800)
pdf(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.QQPlot.pdf"),
width = 12, height = 10, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.QQPlot.ps"),
width = 12, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.QQPlot.tiff"),
width = 1024, height = 800, units = "px", pointsize = 12,
compression = "none", bg = "transparent",
type = "quartz")
par(mfrow = c(1,2), oma = c(0, 0, 2, 0), mar = c(5, 6, 4, 2) + 0.1)
qq(aems450k1.resultsbfCGI$Pval_limma,
main = bquote("Uncorrected " ~ lambda == .(lambdab)),
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(aems450k1.resultsbfCGI$Pval_limma_bacon,
main = bquote("Corrected" ~ lambda == .(lambdabcb)),
col = "#1290D9", pch = 16, xlim = c(0,8), ylim = c(0,25),
cex = 1.75, cex.lab = 1.75, cex.axis = 1.75, cex.main = 1.50,
bty = "n")
abline(0, 1, col = "#E55738")
mtext("QQ-plots", outer = TRUE, cex = 2.0)
dev.off()
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mar = c(5, 4, 4, 2) + 0.1)
cat("\n - QQ-plot in plaque...")
png(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.QQPlot.png"),
width = 1024, height = 800)
pdf(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.QQPlot.pdf"),
width = 12, height = 10, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.QQPlot.ps"),
width = 12, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.QQPlot.tiff"),
width = 1024, height = 800, units = "px", pointsize = 12,
compression = "none", bg = "transparent",
type = "quartz")
par(mfrow = c(1,2), oma = c(0, 0, 2, 0), mar = c(5, 6, 4, 2) + 0.1)
qq(aems450k1.resultspfCGI$Pval_limma,
main = bquote("Uncorrected " ~ lambda == .(lambdap)),
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(aems450k1.resultspfCGI$Pval_limma_bacon,
main = bquote("Corrected" ~ lambda == .(lambdabcp)),
col = "#1290D9", pch = 16, xlim = c(0,8), ylim = c(0,25),
cex = 1.75, cex.lab = 1.75, cex.axis = 1.75, cex.main = 1.50,
bty = "n")
abline(0, 1, col = "#E55738")
mtext("QQ-plots", outer = TRUE, cex = 2.0)
dev.off()
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mar = c(5, 4, 4, 2) + 0.1)
cat("\n - Manhattan-plot in blood...")
# blood
# head(resultsbf[order(resultsbf[,24]),], 17)
# example of known smoking-related CpG
# There is a top 17 with FDR <= 0.05, and 10 genes:
# AHHR
# F2RL3
# LINC00299
# ZMIZ1
# ALPI
# RUBCN
# TBC1D16
# ITPK1
# OR7E2P
# MIR3649
# (other) top hits: AHRR (cg05575921, cg03991871, cg12806681, cg21161138, cg26703534, cg23916896
# resp. p = 3.383287e-21, p = 1.019843e-13, p = 5.355407e-13, p = 1.586751e-12, p = 5.028840e-11, p = 7.294823e-08, chr5),
# F2RL3 (cg03636183, p = 1.071662e-09, chr19),
# LINC00299 (cg23079012, p = 1.340555e-08, chr2)
# ZMIZ1 (cg03450842, p = 6.397912e-08, chr2),
# ALPI (cg05951221, cg21566642, resp. p = 2.989385e-07, p = 3.846215e-07, chr2),
# RUBCN (cg03358636, p = 4.957482e-07, chr3)
png(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.ManhattanPlot.png"),
width = 1920, height = 1080)
pdf(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.ManhattanPlot.pdf"),
width = 28, height = 8, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.ManhattanPlot.ps"),
width = 28, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.ManhattanPlot.tiff"),
width = 1920, height = 1080, units = "px", pointsize = 12,
compression = "none", bg = "transparent",
type = "quartz")
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mai = c(1, 1, 1, 0))
ahrr <- aems450k1.resultsbfCGI$CpG[grep("AHRR", aems450k1.resultsbfCGI$SYMBOL)]
f2rl3 <- aems450k1.resultsbfCGI$CpG[grep("F2RL3", aems450k1.resultsbfCGI$SYMBOL)]
linc00299 <- aems450k1.resultsbfCGI$CpG[grep("LINC00299", aems450k1.resultsbfCGI$SYMBOL)]
zmiz1 <- aems450k1.resultsbfCGI$CpG[grep("ZMIZ1", aems450k1.resultsbfCGI$SYMBOL)]
alpi <- aems450k1.resultsbfCGI$CpG[grep("ALPI", aems450k1.resultsbfCGI$SYMBOL)]
rubcn <- aems450k1.resultsbfCGI$CpG[grep("RUBCN", aems450k1.resultsbfCGI$SYMBOL)]
tbc1d16 <- aems450k1.resultsbfCGI$CpG[grep("TBC1D16", aems450k1.resultsbfCGI$SYMBOL)]
itpk1 <- aems450k1.resultsbfCGI$CpG[grep("ITPK1", aems450k1.resultsbfCGI$SYMBOL)]
or7e2p <- aems450k1.resultsbfCGI$CpG[grep("OR7E2P", aems450k1.resultsbfCGI$SYMBOL)]
mir3649 <- aems450k1.resultsbfCGI$CpG[grep("MIR3649", aems450k1.resultsbfCGI$SYMBOL)]
# hilight.topb <- c(ahrr, f2rl3, linc00299, zmiz1, alpi, rubcn)
hilight.topb <- c(ahrr, f2rl3, linc00299, zmiz1,
alpi, rubcn, tbc1d16, itpk1, or7e2p, mir3649)
ewas_p = 0.05/length(aems450k1.resultsbfCGI$CpG)
manhattan.uithof(aems450k1.resultsbfCGI, chr = "chr", bp = "start", p = "Pval_limma_bacon", snp = "CpG",
highlight = hilight.topb, 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(ahrr, f2rl3, linc00299, zmiz1, alpi, rubcn, tbc1d16, itpk1, or7e2p, mir3649, hilight.topb, ewas_p)
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mai = c(1, 1, 1, 1))
cat("\n - Manhattan-plot in plaque...")
# plaque
# 0.05/nrow(aems450k1.resultspfCGI)*10
# utils::head(aems450k1.resultspfCGI[order(aems450k1.resultspfCGI[,23]),], 12)
# example of known smoking-related CpG
# (other) top hits: AHRR (cg25648203, cg05575921, resp. p = 1.123136e-19, p = 1.391187e-09, chr5),
# NTHL1 (cg16650073, p = 1.137520e-11, chr16),
# CRLF1 (cg22702618, p = 3.923536e-09, chr19),
# GLIS1 (cg18767735, p = 8.335704e-08 , chr1)
# Top 20 CpG at p < 1.0 e-5 in *AEMS450K1*, equal to 7 genes:
# AHRR
# NTHL1
# CRLF1
# GLIS1
# EEFSEC
# PRDM16
# OLFM2
#
# As of these are FDR > 0.05, and p = 0.05/nrow(aems450k1.resultspfCGI)*100 = 1.127726e-05
# ALPI
# RARA
# SDCCAG8
# ITPK1
# C2CD4A
# SORBS1
png(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.ManhattanPlot.png"),
width = 1920, height = 1080)
pdf(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.ManhattanPlot.pdf"),
width = 28, height = 8, onefile = TRUE)
postscript(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".plaque.ManhattanPlot.ps"),
width = 28, height = 10, onefile = TRUE, bg = "transparent", family = "Helvetica")
tiff(paste0(PLOT_loc,"/",Today,".aems450k1.",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 <- aems450k1.resultspfCGI$CpG[grep("AHRR", aems450k1.resultspfCGI$SYMBOL)]
nthl1 <- aems450k1.resultspfCGI$CpG[grep("NTHL1", aems450k1.resultspfCGI$SYMBOL)]
crlf1 <- aems450k1.resultspfCGI$CpG[grep("CRLF1", aems450k1.resultspfCGI$SYMBOL)]
glis1 <- aems450k1.resultspfCGI$CpG[grep("GLIS1", aems450k1.resultspfCGI$SYMBOL)]
eefsec <- aems450k1.resultspfCGI$CpG[grep("EEFSEC", aems450k1.resultspfCGI$SYMBOL)]
prdm16 <- aems450k1.resultspfCGI$CpG[grep("PRDM16", aems450k1.resultspfCGI$SYMBOL)]
olfm2 <- aems450k1.resultspfCGI$CpG[grep("OLFM2", aems450k1.resultspfCGI$SYMBOL)]
hilight.top <- c(ahrr, nthl1, crlf1, glis1, eefsec, prdm16, olfm2)
ewas_p = 0.05/length(aems450k1.resultspfCGI$CpG)
manhattan.uithof(aems450k1.resultspfCGI, chr = "chr", bp = "start", p = "Pval_limma_bacon", snp = "CpG",
highlight = hilight.top, suggestiveline = FALSE,
genomewideline = FALSE,
cex = 2.5, cex.axis = 1.75, cex.lab = 1.75,
col = uithof_color, ylim = c(0,20),
chrlabs = c(1:22, "X", "Y"),
annotatePval = NULL, annotateTop = NULL)
dev.off()
rm(ahrr, nthl1, crlf1, glis1, eefsec, prdm16, olfm2, hilight.top, ewas_p)
par(mfrow = c(1,1), oma = c(0, 0, 0, 0), mai = c(1, 1, 1, 1))
cat("\n - Top results correlation plots...")
cat("\n > in blood...")
pdf(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.TopCor.pdf"))
top.data <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), ][1,]
top.symbol <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "SYMBOL"][1]
top <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][1]
x <- data[rownames(data) == top, ]
y <- design[, 2]
pearsonR <- signif(cor(x, y), 3)
par(mfrow = c(1,1), mar = c(5,4,4,2))
boxplot(x~y, main = bquote(Pearson~r^2 == .(pearsonR)),
xlab = "Current Smoking",
ylab = bquote(.(top)~" methylation near "~italic(.(top.symbol))),
plot = TRUE, notch = FALSE, outline = TRUE,
names = c("no smoker", "current smoker"),
whisklty = 1, staplelty = 0, # no whisker-ends, but with lines
frame = FALSE)
stripchart(x~y, vertical = TRUE,
method = "jitter", add = TRUE, pch = 20, col = "#1290D9")
dev.off()
# BLOOD: TOP 12
# head(aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI[,24]),], 17)
# (other) top hits: AHRR (cg05575921, cg03991871, cg12806681, cg21161138, cg26703534, cg23916896
# resp. p = 3.383287e-21, p = 1.019843e-13, p = 5.355407e-13, p = 1.586751e-12, p = 5.028840e-11, p = 7.294823e-08, chr5),
# F2RL3 (cg03636183, p = 1.071662e-09, chr19),
# LINC00299 (cg23079012, p = 1.340555e-08, chr2)
# ZMIZ1 (cg03450842, p = 6.397912e-08, chr2),
# ALPI (cg05951221, cg21566642, resp. p = 2.989385e-07, p = 3.846215e-07, chr2),
# RUBCN (cg03358636, p = 4.957482e-07, chr3)
pdf(paste0(PLOT_loc,"/",Today,".aems450k1.",EWAS_trait,".blood.Top12.pdf"), paper = "a4", onefile = TRUE)
top.ahrr1 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][1]
top.ahrr2 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][2]
top.ahrr3 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][3]
top.ahrr4 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][4]
top.ahrr5 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][5]
top.ahrr6 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][9]
top.f2rl3 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][6]
top.linc00299 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][7]
top.zmiz1 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][8]
top.alpi1 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][10]
top.alpi2 <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][11]
top.rubcn <- aems450k1.resultsbfCGI[order(aems450k1.resultsbfCGI$Pval_limma_bacon), "CpG"][12]
par(mfrow = c(2,3), mar = c(5,8,4,2), oma = c(0, 0, 2, 0))
# AHRR 1 through 6
x.ahrr1 <- data[rownames(data) == top.ahrr1, ]
y.ahrr1 <- design[, 2]