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GWASRiyan.R
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GWASRiyan.R
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#-------------------------------------------------
# This program is developed for the Borevitz lab
# You may use it without any warranty
# Distribution is not permitted
# Copyright - Riyan Cheng 2016
#-------------------------------------------------
#
# This program is to process raw data
# and perform GWAS
#
# Notes:
# 1) This program runs one data file at a time
# 2) Row 1: plantid; row 2: pot number
# 3) Column 1: timestamp
# 4) Manually select days 'ud' to reliably smooth if needed
# 5) File "*-traitcapture-db-import-Plants.csv" about design
# e.g. "BVZ0063-traitcapture-db-import-Plants.csv"
# must immediately under the experiment folder e.g. "BVZ0063"
###################
# basic functions #
#############################################################
strf<- function(strList, pos=1){
strTmp<- NULL
for(i in 1:length(strList)){
strTmp<- c(strTmp,strList[[i]][pos])
}
strTmp
}
# smoothing span
nf<- function(n){
5+(30-4)/(40-5)*(n-5)
}
# remove outliers
drop1f<- function(dat, min.n=5, span=NA){
# dat: data.frame(x=, y=)
# min.n: min no. of useable observations
# span: pass to 'loess'
drop<- NA
predicted<- NA
if(sum(!is.na(dat$y)) >= min.n){
n<- sum(!is.na(dat$y))
if(missing(span) || is.na(span))
spn<- nf(n)/n
lf<- loess(y ~ x, data=dat, na.action=na.exclude, degree=2, span=spn,
control=loess.control(surface="direct"))
rm(n)
lfp<- predict(lf, dat$x)
rss<- Inf
cv<- 0
for(i in 1:length(dat$y)){
if(is.na(dat$y[i])) next
.dtt<- dat; .dtt$y[i]<- NA
.n<- sum(!is.na(.dtt$y))
if(missing(span) || is.na(span))
spn<- nf(.n)/.n
.lf<- loess(y ~ x, data=.dtt, na.action=na.exclude, degree=2, span=spn,
control=loess.control(surface="direct"))
.lfp<- predict(.lf, .dtt$x)
.sd<- sd(dat$y-.lfp, na.rm=TRUE) #too stringent to use .dtt
.cv<- abs(dat$y-.lfp)[i]/(.sd + sqrt(.Machine$double.eps))
if(FALSE){
# large but minimize RSS
.sd<- sd(.dtt$y-.lfp, na.rm=TRUE)
if(.cv > qnorm(1-0.05/.n) && .sd < rss){
lfp<- .lfp
drop<- i
rss<- .sd
}
}else{
if(.cv > max(cv,qnorm(1-0.05/.n))){
lfp<- .lfp
drop<- i
cv<- .cv
}
}
rm(.n)
}
}
if(!is.na(drop)){
dat$y[drop]<- NA
predicted<- lfp
}
list(data=dat, predicted=predicted, drop=drop)
}
# fit loess per day and then select the max among middle 50%
procDayDataf<- function(pdat, dl=10, min.n=0, span=NA){
# pdat: time points by row, samples by column
# except the first column timestamp
# dl: extract day string with length of dl
# min.n: min number of observations in a day; or, no data
itv<- table(substring(pdat$time,15,16))
itv<- names(itv)[itv > mean(itv)/3]
itv<- sort(as.numeric(itv),decreasing=FALSE)
itv<- min(diff(itv))
itv<- seq(0,60-1e-8,by=itv)
hrs<- table(substring(pdat$time,12,13))
hrs<- names(hrs)[hrs > mean(hrs)/3]
hrs<- sort(as.numeric(hrs),decreasing=FALSE)
nr<- length(hrs)
tt<- c(paste(sprintf("%02d",min(hrs)),"_00",sep=""),paste(sprintf("%02d",max(hrs)),"_30",sep=""))
tm<- paste(sprintf("%02d",rep(hrs,rep(length(itv),nr))), sprintf("%02d",rep(itv,nr)), sep="_")
tm<- tm[match(tt[1],tm):match(tt[2],tm)]
day<- sapply(as.character(pdat$time), substr, 1, dl)
ud<- table(day)
ud<- names(ud)[ud > mean(ud)/3]
ud<- sort(ud, decreasing=FALSE)
dayTime<- paste(rep(ud,rep(length(tm),length(ud))), rep(tm,length(ud)), sep="_")
dt<- matrix(NA, nrow=length(dayTime), ncol=ncol(pdat)-1)
rownames(dt)<- dayTime
colnames(dt)<- colnames(pdat)[-1]
for(j in 2:ncol(pdat)){
if((j-1)%%10 + 1 == 1) cat(j-1) else cat(".")
for(i in 1:length(ud)){
#cat(".")
dat<- pdat[day==ud[i],]
if(nrow(dat) < min.n){
#cat(paste("[", i, ",",j,"]: ", "Less than ", min.n, " usable data points\n", sep=""))
next
}
tmTmp<- sapply(as.character(dat$time), substr, max(dl)+2, max(dl)+6)
dat<- dat[match(tm,tmTmp),]
datTmp<- data.frame(x=1:nrow(dat), y = dat[, j])
rng<- c(min(pdat[,-1],na.rm=TRUE), max(pdat[,-1],na.rm=TRUE))
idx1<- !is.na(datTmp$y)
# idx1[idx1]<- datTmp$y[idx1] > 0 # remove 0's?
if(sum(idx1) < min.n){
#cat(paste("[", i, ",",j,"]: ", "Less than ", min.n, " usable data points\n", sep=""))
next
}
.dat<- datTmp
.dat$y[!idx1]<- NA
while(TRUE){
if(sum(!is.na(.dat$y)) < min.n){
#cat(paste("[", i, ",",j,"]: ", "Less than ", min.n, " usable data points\n", sep=""))
break
}else{
dp1<- drop1f(.dat, min.n=min.n, span=span)
if(is.na(dp1$drop)) break
.dat<- dp1$data
}
}
# add points that are close
ytmp<- abs(datTmp$y-dp1$predict)
idx<- !(ytmp > sd(.dat$y-dp1$predict, na.rm=TRUE)*qnorm(1-0.05/2) + sqrt(.Machine$double.eps))
idx[is.na(idx)]<- FALSE
idx<- idx & is.na(.dat$y)
.dat$y[idx]<- datTmp$y[idx]
rm(ytmp, idx)
idx2<- !is.na(.dat$y)
idx<- idx1 & idx2
if(sum(idx) >= min.n){
n<- sum(!is.na(.dat$y))
# use "interpolate" to avoid unrealistic predicted values
lf<- loess(y ~ x, data=.dat, na.action=na.exclude, degree=2, span=nf(n)/n,
control=loess.control(surface="interpolate"))
lfp<- predict(lf, .dat$x)
lfp[lfp<rng[1]]<- rng[1]
lfp[lfp>rng[2]]<- rng[2]
dt[match(paste(ud[i], tm, sep="_"), rownames(dt)), j-1]<- lfp
#cat("[",j-1,",",i,"]: ", dt[704:707,134], "\n",sep="")
}else{
#cat(paste("[", i, ",",j,"]: ", "Less than ", min.n, " usable data points\n", sep=""))
next
}
#Sys.sleep(1)
}
}
cat("\n")
invisible(dt)
}
# day data & smoothing
dtf<- function(dayDat, day, ud, dl=10){
# one data point from each day
ddt<- matrix(NA,nrow=length(ud),ncol=ncol(dayDat))
rownames(ddt)<- ud
colnames(ddt)<- colnames(dayDat)
for(i in 1:length(ud)){
idx<- sapply(rownames(dayDat), substr, 1, dl) == ud[i]
for(j in 1:ncol(ddt)){
smr<- summary(dayDat[idx,colnames(ddt)[j]])
# 'median'; better than a time point, e.g. 12:00 pm?
ddt[i,j]<- smr["Median"]
}
}
# smooth ddt
adt<- ddt
for(j in 1:ncol(adt)){
dtTmp<- data.frame(x=1:nrow(ddt), y=ddt[,j])
n<- sum(!is.na(dtTmp$y))
if(n < 5){
next
}else{
lf<- loess(y ~ x, data=dtTmp, na.action=na.exclude, degree=2, span=nf(n)/n,
control=loess.control(surface="interpolate"))
}
rm(n)
adt[,j]<- predict(lf, dtTmp$x)
}
adt[adt<0]<- NA
# growth rate
gr<- apply(adt, 2, diff)
# relative growth rate
rgr<- gr/adt[-nrow(adt),]
list(ddt=ddt, adt=adt, gr=gr, rgr=rgr)
}
# quality control
qcf<- function(pdat, missing.pr=0.25, change.pr=0.25){
# missing.pr: max missing proportion
# change.pr: min change ratio
# remove days with too many missing data
cn<- colnames(pdat)
pdat<- pdat[apply(is.na(pdat),1,mean)<missing.pr,]
# remove plants with too many missing data
ii<- apply(is.na(pdat),2,sum)
ii<- (1:length(ii))[ii < nrow(pdat)/2]
length(ii)
# remove plants with little change or the control
dmr<- apply(apply(pdat[,ii],2,range,na.rm=TRUE),2,diff)
ii<- ii[dmr>IQR(apply(pdat[,ii],2,median,na.rm=TRUE))*change.pr]
pdt<- pdat[,ii]
colnames(pdt)<- cn[ii]
pdt
}
# prepare for myScan
datProcf<- function(pdat){
eid<- sapply(rownames(pdat),as.character)
uid<- unique(eid)
pdt.<- pdat[match(uid,eid),]
nC<- rep(1,length(uid))
for(id in uid){
idx<- is.element(eid,id)
if(sum(idx) < 2) next
tmp<- pdat[idx,]
#if(all(is.na(tmp))) cat(id, ' ')
pdt.[match(id,uid),]<- apply(tmp,2,mean,na.rm=TRUE)
nC[match(id,uid)]<- nrow(tmp)
}
pdt<- as.data.frame(pdt.)
colnames(pdt)<- colnames(pdat)
list(pdat=pdt, n=nC)
}
# genome scan
myScan<- function(pdat,gdat,gmap,day,cdl){
# pdat: phenotype data
# gdat: genotype data
# gmap: genetic/physical map
# day: which column of pdat
# cdl: experimental environment
####-------------------------------------------
if(missing(cdl)){
pdatTmp<- datProcf(pdat)
pdt<- pdatTmp$pdat
pdt$y<- pdt[,day]
idx<- !is.na(pdt$y)
pdt<- pdt[idx,]
n<- pdatTmp$n[idx]
rm(pdatTmp,idx)
}else{
uc<- unique(cdl)
pdt<- n<- c<- NULL
for(u in uc){
pdatTmp<- datProcf(pdat[cdl==u,])
pdtTmp<- pdatTmp$pdat
pdtTmp$y<- pdtTmp[,day]
idx<- !is.na(pdtTmp$y)
pdt<- rbind(pdt, pdtTmp[idx,])
n<- c(n, pdatTmp$n[idx])
c<- c(c, rep(u,sum(idx)))
rm(pdatTmp,pdtTmp,idx)
}
cdl<- c
rm(uc,c,u)
}
idx<- match(rownames(pdt),rownames(gdat))
pdt<- pdt[!is.na(idx),]
n<- n[!is.na(idx)]
if(!missing(cdl))
cdl<- cdl[!is.na(idx)]
idx<- idx[!is.na(idx)]
gdt<- gdat[idx,]
cat("Day ", day, " [sample size: ",nrow(pdt),"]: ", date(),"\n", sep="")
vc<- vector("list",5)
for(j in 1:5){
idx<- match(rownames(gdt),rownames(gm[[j]]$AA))
if(any(is.na(idx))) stop("something wrong...")
v<- list(
AA = gm[[j]]$AA[idx,idx],
DD = NULL,
AD = NULL,
HH = NULL,
MH = NULL,
EE = diag(1/n)
)
if(missing(cdl)){
vc[[j]]<- estVC(y=pdt$y,v=v)
}else{
vc[[j]]<- estVC(y=pdt$y,x=cdl,v=v)
}
}
#### genome scan
lrt<- est<- NULL
for(j in 1:5){
idx<- is.element(colnames(gdt),gmap$snp[gmap$chr==j])
gdtTmp<- gdt[,idx]
gdtTmp<- as.matrix(gdtTmp)
if(missing(cdl)){
sc<- scanOne(y=pdt$y, gdat=gdtTmp, vc=vc[[j]], intcovar=NULL, minorGenoFreq=0.05)
}else{
sc<- scanOne(y=pdt$y, x=cdl, gdat=gdtTmp, vc=vc[[j]], intcovar=NULL, minorGenoFreq=0.05)
}
lrt<- c(lrt,sc$p)
estTmp<- matrix(unlist(sc$par),nrow=length(sc$par),byrow=TRUE)
rownames(estTmp)<- names(sc$par)
colnames(estTmp)<- names(sc$par[[1]])
est<- rbind(est, estTmp)
}
list( lrt = lrt, est = est)
###--------------------------------
}
#########################
# process and plot data #
########################################################################################
# create folders (if unavailable) for ouput
for(f in c("graphs","images","output")){
if(!dir.exists(f)) dir.create(f)
rm(f)
}
#
# data file; one at a time
#
fn<- commandArgs(trailingOnly = TRUE)[1]
#
# this file is https://raw.githubusercontent.com/borevitzlab/GBSFilteR/master/BVZ0039-GC03L-C01~fullres-area.csv
#
pheno<- strsplit(fn, "\\.")[[1]][1] # best to specify the file name with data information
pheno<- strsplit(pheno, "\\/")[[1]]
expID<- pheno[length(pheno)-4]
fld<- paste(c(pheno[1:(length(pheno)-4)],""),collapse="/")
pheno<- pheno[length(pheno)]
#
# experiment information (best to use the same file name)
#
expInfo<- paste(fld,"_data/",expID,"-traitcapture-db-import-Plants.csv",sep="")
expInfo<- read.csv(expInfo, check.names=FALSE)
trayNum<- strf(strsplit(as.character(expInfo$Tray),"[A-Z]"))
trayNum<- as.integer(trayNum)
#
# input data
#
pdat<- read.csv(fn, na.string=c("Na", "NaN"), header=TRUE, check.names=FALSE, skip=0)
pdat<- pdat[apply(!is.na(pdat),1,sum,na.rm=TRUE)>ncol(pdat)*2/3,] # remove NA rows
pdat<- pdat[apply(pdat[,-1] > 0,1,sum,na.rm=TRUE)>ncol(pdat)*2/3,] # remove 0 rows
pid<- sapply(expInfo$PlantID[match(colnames(pdat),expInfo$"Pot")],as.character)
pid[1]<- colnames(pdat)[1]
pid[is.na(pid)]<- paste("Pot",colnames(pdat)[is.na(pid)],sep="")
colnames(pdat)<- pid
rm(pid)
#
# process data by fitting smoothing curves
#
dl<- 10
min.n<- 7
date()
dayDat<- procDayDataf(pdat, dl=dl, min.n=min.n, span=NA)
date()
#
# one data point / day
#
day<- sapply(as.character(pdat$time), substr, 1, 10)
ud<- sort(unique(day), decreasing=FALSE)
# ddt, adt, gr & rgr
ddt<- dtf(dayDat, day, ud)
cntNa<- apply(is.na(ddt$ddt), 2, cumsum)
#
# save results
#
# smoothed curv over days
write.csv(ddt$adt,file=paste("output/",pheno,"-smoothed.csv",sep=""))
# growth rate
write.csv(ddt$gr,file=paste("output/",pheno,"-growthRate.csv",sep=""))
# candidates for manual checking
rownames(ddt$adt)[1:min(nrow(ddt$adt),25)]
pdt<- qcf(ddt$adt[1:min(nrow(ddt$adt),25),], missing.pr=0.25, change.pr=1/3)
ex<- expInfo[is.element(expInfo$PlantID,colnames(ddt$adt)),]
exLst<- ex[!is.element(ex$PlantID,colnames(pdt)),]
rm(pdt,ex)
write.csv(exLst, file=paste(pheno,"_checkList.csv",sep=""), row.names=FALSE)
save.image(paste(pheno,".RData",sep=""))
##########################
# plotting data
#
library(jpeg)
if(file.exists("~/WinDoc/ANU/kangaroo.jpeg")) img<- readJPEG("~/WinDoc/ANU/kangaroo.jpeg") else
img<- NA
plotf<- function(pdat,day,dayDat,ddt,cntNa,img,type=c("pdf","jpg")){
type<- match.arg(type)
if(type=="jpg")
jpeg(paste("images/",pheno,"-Cover.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
# ---------------------------------------------
plot.new()
text(0.5, 0.80, "NOTE")
text(0.5, 0.75,"************************************************************************")
text(0.5, 0.65, paste("THIS GRAPHICALLY DISPLAYS DATA AND PREDICTION\n\n",
"FOR THE PURPOSE OF DIAGNOSIS",sep=""))
if(!is.na("img")) rasterImage(img,0.35,0.30,0.65,0.50)
text(0.5, 0.55,"************************************************************************")
text(0.5, 0.20, format(Sys.time(), "%A, %B %d, %Y"))
text(1, 0.023, "Warning: you are running the sofware without any warranty", cex=0.95, adj=1)
text(1, -0.01, "_________________", adj=1)
text(1, -0.03, paste("Copyright \uA9 R. Cheng 2015",sep=""), font=3, cex=0.75, adj=1, xpd=TRUE)
# ---------------------------------------------
if(type=="jpg") dev.off()
if(type=="jpg")
jpeg(paste("images/",pheno,"-AllScatter.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
par(mfrow=c(1,1), mar=c(3.75,3.75,3.25,1), mgp=c(2.05,1,0))
# ---------------------------------------------
dTmp<- sort(unique(day), decreasing=FALSE)
udTmp<- dTmp[(1:length(dTmp))%%3==1]
tTmp<- rownames(dayDat)
tmIdx<- match(tTmp, sapply(as.character(pdat$time),substr,1,16))
rng<- range(pdat[tmIdx,-1], na.rm=TRUE)
matplot(pdat[tmIdx,-1], ylim=rng, type="p", cex=0.25, xaxt="n",
xlab="Time", ylab="Observed", main=pheno)
axis(1, at=match(udTmp,day[tmIdx]), tck=-0.010, labels=FALSE)
text(match(udTmp,day[tmIdx]), rep(min(rng)-diff(rng)*0.07,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.75, xpd=TRUE)
# ---------------------------------------------
if(type=="jpg") dev.off()
# raw data + smooth curve per day for each plant
fmt<- (ncol(pdat)-2)%/%12+1
fmt<- (fmt-1)%/%10+1
fmt<- paste("%0",fmt,"d",sep="")
if(type=="pdf") par(mfrow=c(4,3), mar=c(3.0,3.0,2.0,1), mgp=c(2,1,0))
for(j in 2:ncol(pdat)){
if(type=="jpg"){
jpeg(paste("images/",pheno,"-",colnames(pdat)[j],".jpg",sep=""),
width = 720, height = 840, quality=100, res=100)
par(mar=c(3.0,3.0,2.0,1), mgp=c(2,1,0))
# ---------------------------------------------
}
plot(1:length(tTmp), rep(NA,length(tTmp)), ylim=rng, type="n", xaxt="n", cex=0.25,
cex.axis=0.65, xlab="Time", ylab="Observed",
main=paste("Plant ID ", colnames(pdat)[j], sep=""))
axis(1, at=match(udTmp,day[tmIdx]), tck=-0.015, labels=FALSE)
text(match(udTmp,day[tmIdx]), rep(min(rng)-diff(rng)*0.125,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.5, xpd=TRUE)
for(d in dTmp){
dIdx<- grep(d,tTmp)
tIdx<- match(tTmp[dIdx], sapply(as.character(pdat$time),substr,1,16))
points(dIdx, pdat[tIdx,][,j], ylim=rng, type="p", cex=0.25)
#yTmp<- dayDat[dIdx, match(colnames(pdat)[j], colnames(dayDat))]
# the above has trouble in case duplicate plant IDs
yTmp<- dayDat[dIdx, j-1]
idx<- dIdx[!is.na(yTmp)]
yTmp<- yTmp[!is.na(yTmp)]
lines(idx, yTmp, col=2)
}
if(type=="jpg") dev.off()
# ---------------------------------------------
}
udTmp<- ud[(1:length(ud))%%3==1]
#1) predicted
if(type=="jpg")
jpeg(paste("images/",pheno,"-Predict1.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
par(mfrow=c(1,1), mar=c(3.75,3.75,3.25,1), mgp=c(2.05,1,0))
# ---------------------------------------------
rng<- range(ddt$ddt, na.rm=TRUE)
matplot(ddt$ddt, type="l", xaxt="n", xlab="Day", ylab="Predicted",
main="Prediction for All Plants (Per Day)")
axis(1, at=match(udTmp,rownames(ddt$ddt)), tck=-0.010, labels=FALSE)
text(match(udTmp,rownames(ddt$ddt)), rep(min(rng)-diff(rng)*0.07,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.75, xpd=TRUE)
# ---------------------------------------------
if(type=="jpg") dev.off()
#2) smooth over days (one data point for each data)
if(type=="jpg")
jpeg(paste("images/",pheno,"-Predict2.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
par(mfrow=c(1,1), mar=c(3.75,3.75,3.25,1), mgp=c(2.05,1,0))
# ---------------------------------------------
rng<- range(ddt$adt, na.rm=TRUE)
matplot(ddt$adt, type="l", xaxt="n", xlab="Day", ylab="Predicted",
main="Smoothed Prediction for All Plants")
axis(1, at=match(udTmp,rownames(ddt$adt)), tck=-0.010, labels=FALSE)
text(match(udTmp,rownames(ddt$adt)), rep(min(rng)-diff(rng)*0.07,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.75, xpd=TRUE)
# ---------------------------------------------
if(type=="jpg") dev.off()
#3) remove data points with too many missing data
if(type=="jpg")
jpeg(paste("images/",pheno,"-Predict3.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
par(mfrow=c(1,1), mar=c(3.75,3.75,3.25,1), mgp=c(2.05,1,0))
# ---------------------------------------------
rng<- range(ddt$ddt, na.rm=TRUE)
idx<- apply(is.na(ddt$ddt),2,sum)
ii<- (1:length(idx))[idx < nrow(ddt$ddt)/4]
#& remove plants with little change or the control
dmr<- apply(apply(ddt$ddt[,ii],2,range,na.rm=T),2,diff)
ii<- ii[dmr>median(dmr)/10]
matplot(ddt$ddt[,ii],type="l", xaxt="n", xlab="Day", ylab="Predicted",
main="Prediction for Plants with Missing Proportion < 25%")
axis(1, at=match(udTmp,rownames(ddt$ddt)), tck=-0.010, labels=FALSE)
text(match(udTmp,rownames(ddt$ddt)), rep(min(rng)-diff(rng)*0.07,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.75, xpd=TRUE)
# ---------------------------------------------
if(type=="jpg") dev.off()
#4) accumulative number of missing data points
if(type=="jpg")
jpeg(paste("images/",pheno,"-CumulNA.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
par(mfrow=c(1,1), mar=c(3.75,3.75,3.25,1), mgp=c(2.05,1,0))
# ---------------------------------------------
rng<- range(cntNa, na.rm=TRUE)
if(rng[2]==0) rng<- c(0,1)
matplot(cntNa, ylim=rng, type="l", xaxt="n", xlab="Day", ylab="Cumulative Number of NAs",
main="Missing Data Pettern for All Plants (Per Day)")
axis(1, at=match(udTmp,rownames(cntNa)), tck=-0.010, labels=FALSE)
text(match(udTmp,rownames(cntNa)), rep(min(rng)-diff(rng)*0.07,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.75, xpd=TRUE)
tbl<- table(cntNa[nrow(cntNa),])
text(rep(nrow(cntNa)-0.5,length(tbl)), as.numeric(names(tbl)), tbl, cex=0.75, col=2, pos=4, xpd=TRUE)
# ---------------------------------------------
if(type=="jpg") dev.off()
#5) growth rate
if(type=="jpg")
jpeg(paste("images/",pheno,"-GrowthRate.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
par(mfrow=c(1,1), mar=c(3.75,3.75,3.25,1), mgp=c(2.05,1,0))
# ---------------------------------------------
rng<- range(ddt$gr, na.rm=TRUE)
matplot(ddt$gr, type="l", xaxt="n", xlab="Day", ylab="Growth Rate", main="")
axis(1, at=match(udTmp,rownames(ddt$gr)), tck=-0.010, labels=FALSE)
text(match(udTmp,rownames(ddt$gr)), rep(min(rng)-diff(rng)*0.07,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.75, xpd=TRUE)
if(type=="jpg") dev.off()
#6) relative growth rate
if(type=="jpg")
jpeg(paste("images/",pheno,"-GrowthRateRel.jpg",sep=""), width = 720, height = 840, quality=100, res=100)
par(mfrow=c(1,1), mar=c(3.75,3.75,3.25,1), mgp=c(2.05,1,0))
# ---------------------------------------------
rng<- range(ddt$rgr, na.rm=TRUE)
matplot(ddt$rgr, type="l", xaxt="n", xlab="Day", ylab="Relative Growth Rate", main="")
axis(1, at=match(udTmp,rownames(ddt$rgr)), tck=-0.010, labels=FALSE)
text(match(udTmp,rownames(ddt$rgr)), rep(min(rng)-diff(rng)*0.07,length(udTmp)),
labels=gsub("_","/",substring(udTmp,6,10)), srt=35, adj=0.75, cex=0.75, xpd=TRUE)
# ---------------------------------------------
if(type=="jpg") dev.off()
}
plotf(pdat=pdat, day=day, dayDat=dayDat, ddt=ddt, cntNa=cntNa, img=img, type="jpg")
if(FALSE){
cvt<- paste("convert -compress Zip -quality 100", " images/", pheno, "_Cover.jpg", sep="")
cvt<- paste(cvt, " images/", pheno, "_AllScatter*.jpg", sep="")
cvt<- paste(cvt, " images/", pheno, "_Idv*.jpg", sep="")
cvt<- paste(cvt, " images/", pheno, "_Prediction_*.jpg", sep="")
cvt<- paste(cvt, " images/", pheno, "_CumulNA.jpg", sep="")
cvt<- paste(cvt, " images/", pheno, "_GrowthRate*.jpg", sep="")
cvt<- paste(cvt, " ", pheno, "Tmp.pdf", sep="")
system(cvt)
}
pdf(paste("graphs/",pheno,".pdf",sep=""), title=pheno, height=9)
plotf(pdat=pdat, day=day, dayDat=dayDat, ddt=ddt, cntNa=cntNa, img=img, type="pdf")
dev.off()
q("no")
########
# GWAS #
########
library(QTLRel)
load("../../allData/geno473x250k.RData")
gmap<- phyMap; colnames(gmap)<- c("snp","chr","dist")
pdat<- qcf(ddt$adt, missing.pr=0.25, change.pr=0.25)
pdat<- t(pdat)
rownames(pdat)<- expInfo$EcotypeID[match(rownames(pdat),expInfo$PlantID)]
days<- colnames(pdat)
lrt<- matrix(NA, nrow=nrow(gmap), ncol=ncol(pdat))
colnames(lrt)<- days
lrt<- cbind(gmap, lrt)
for(j in 1:ncol(pdat)){
sc<- myScan(pdat,gdat,gmap,j)
lrt[match(names(sc$lrt),lrt$snp),days[j]]<- sc$lrt
}
rm(j,sc,pdat)
write.csv(lrt,file=paste("output/",pheno,"_LRT.csv",sep=""),row.names=FALSE)
q("no")
#################################################
# the end #
###########