R/memory_profiling.R
In MASHUOA/HiTMaP: A collection of tools for imaging MS data processing
Defines functions
memory_profile
memory_profile<-function(){
#install.packages("ggplot2")
#install.packages("pryr")
#install.packages("devtools")
#devtools::install_github("hadley/lineprof")
#install.packages("tictoc")
library(Cardinal)
library(HiTMaP)
library(pryr)
library(stringr)
library(tictoc)
tic.clearlog()
for(x in 1:10) {
tic(x)
Sys.sleep(.001)
toc(log=TRUE,quiet=TRUE)
}
log.txt<-tic.log(format=TRUE)
log.lst<-tic.log(format=FALSE)
tic.clearlog()
wd="~/expdata/"
wd->workdir
z=1
ppm=10
instrument_ppm=10
parallel=4
BPPARAM=HiTMaP:::Parallel.OS(parallel,bpexportglobals=F,bpforceGC=T, bpprogressbar_t=TRUE)
setCardinalBPPARAM(BPPARAM = BPPARAM)
set.seed(2020)
# mse <- simulateImage(preset=1, npeaks=10, nruns=2, baseline=1)
#mse
import_ppm = floor(instrument_ppm*2/5)[1]
datafile=c("MouseBrain_Trypsin_FT/Mouse_brain.imzML")
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
datafile <- paste0(workdir,"/",datafile)
workdir <- dirname(datafile)
datafile <- basename(datafile)
datafile <-str_remove(datafile,regex(".imzML$"))
datafile_imzML<-paste0(datafile,".imzML")
setwd(workdir[z])
log.txt<-NULL
imdata <- Cardinal::readMSIData(datafile_imzML[z], attach.only=F,as="MSImagingExperiment",resolution=10, units="ppm",BPPARAM=SerialParam())
imdata@centroided<-F
imdata->imdata_org
imdata[,2800:3000]->imdata
tic.clearlog()
for (resolution in c(10, 200,400,800)){
resolution(imdata) <- c(ppm=resolution)
tic(resolution)
image(imdata)
toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="gaussian"),
reduceBaseline=list(method="median"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdata_ed<-imdata
log.txt<-NULL
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10)){
imdata_ed<-imdata
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP"))
#imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
#imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_native
lapply(imdatalist_param_native,object_size)
lapply(imdatalist_param_native,mz)
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10)){
imdata_ed<-imdata
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP"))
#imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
#imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_default
size1<-lapply(imdatalist_param_native,object_size)
size2<-lapply(imdatalist_param_default,object_size)
mz1 <- lapply(imdatalist_param_native,function(x) length(mz(x)))
mz2 <- lapply(imdatalist_param_default, function(x) length(mz(x)))
size<-c(size1,size2)
sizedf<-data.frame(name=names(size1),native_size=unlist(size1),default_size=unlist(size2),native_mz=unlist(mz1),default_mz=unlist(mz2))
sizedf$resolution=str_split_fixed(sizedf$name," ",2)[,1]
sizedf$type=str_split_fixed(sizedf$name," ",2)[,2]
write.csv(sizedf,"sizedf.csv")
writeLines(unlist(log.txt),"log_subset.txt")
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="gaussian"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdata_ed<-imdata_org
log.txt<-NULL
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10,4)){
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"SS_RB_PP"))
imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method, SNR=preprocess$peakPick$SNR, window=preprocess$peakPick$window) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_native
lapply(imdatalist_param_native,object_size)
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdata_ed<-imdata_org
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10,4)){
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"SS_RB_PP"))
#imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
#imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_default
size1<-lapply(imdatalist_param_native,object_size)
size2<-lapply(imdatalist_param_default,object_size)
size<-c(size1,size2)
sizedf<-data.frame(name=names(size1),native=unlist(size1),default=unlist(size2))
sizedf$resolution=str_split_fixed(sizedf$name," ",2)[,1]
sizedf$type=str_split_fixed(sizedf$name," ",2)[,2]
writeLines(unlist(log.txt),"log_all.txt")
object_size(imdata_ed)
setCardinalBPPARAM(BPPARAM)
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec.csv"),row.names = F)
#peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
#write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
#smoothSignal(method="gaussian") %>%
#reduceBaseline(method="locmin") %>%
if (!is.null(preprocess$mz_bin_list)){
preprocess$mz_bin_list->mz_bin_list_s
sort(as.numeric(unlist(mz_bin_list_s)))->ref_mz
peaklist_deco<-data.frame(mz=ref_mz,intensities=rep(1,length(ref_mz)))
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
unlist(peaklist_deco[,1])->ref_mz
ref_mz<-ref_mz[!is.na(ref_mz)]
range(mz(imdata_ed))->ref_mz_range
ref_mz<-ref_mz[ref_mz*(1-ppm/1000000)>=ref_mz_range[1]]
ref_mz<-ref_mz[ref_mz*(1+ppm/1000000)<=ref_mz_range[2]]
ref_mz<-unique(ref_mz)
ref_mz<-ref_mz[!is.na(ref_mz)]
imdata_ed<-imdata_ed %>% mzBin(ref=ref_mz, tolerance=ppm, units="ppm") %>% process()
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
}else{
if (preprocess$smoothSignal$method=="Disable") {
}else if (!is.null(preprocess$smoothSignal$method)){
imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
}else{
imdata_ed<- imdata_ed %>% smoothSignal(method="gaussian")
}
if (preprocess$reduceBaseline$method=="Disable") {
}else if (!is.null(preprocess$reduceBaseline$method)){
imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
}else{
imdata_ed<- imdata_ed %>% reduceBaseline(method="locmin")
}
if (preprocess$peakPick$method=="Disable") {
}else if (preprocess$peakPick$method %in% c("adaptive","mad","simple")){
if (is.null(preprocess$peakPick$SNR)) preprocess$peakPick$SNR=6
if (is.null(preprocess$peakPick$window)) preprocess$peakPick$window=5
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method, SNR=preprocess$peakPick$SNR, window=preprocess$peakPick$window) %>% process()
}else if (preprocess$peakPick$method == "Default|default"){
#add an peak picking function other than the Cardinal options.
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
imdata_ed<-imdata_ed %>% peakBin(peaklist_deco$mz, tolerance=ppm, units="ppm") %>% process()
}
}
saveRDS(imdata_ed,paste0(gsub(".imzML$","",datafile[z]) ," ID/preprocessed_peakpicked_imdata.RDS"))
if (is.null(preprocess$peakAlign$level)) preprocess$peakAlign$level<-"global"
if (preprocess$peakAlign$level=="global"){
if (preprocess$peakAlign$tolerance==0 ) {
message("preprocess$peakAlign$tolerance set as zero, step bypassed")
}else if ('&'(!is.null(preprocess$peakAlign$tolerance),!is.null(preprocess$peakAlign$tolerance))){
message("preprocess$peakAlign$tolerance set as ", preprocess$peakAlign$tolerance)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units)
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec.csv"),row.names = F)
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
}else {
message("preprocess$peakAlign$tolerance missing, use default tolerance in ppm ", ppm/2)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=ppm/2, units="ppm")
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec.csv"),row.names = F)
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
}
}
imdata_ed<- imdata_ed %>% process()
gc()
if (!is.null(preprocess$normalize)){
if (preprocess$normalize$method=="Disable") {
} else if (preprocess$normalize$method %in% c("rms","tic")){
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
} else if ('&'(preprocess$normalize$method == "reference", !is.null(preprocess$normalize$mz))){
norm_feature<-which(dplyr::between(imdata_ed@featureData@mz,
preprocess$normalize$mz*(1-ppm/1000000),
preprocess$normalize$mz*(1+ppm/1000000)))
if (length(norm_feature)>=1){
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method, feature = norm_feature) %>% process(BPPARAM=SerialParam())
}
} else {
imdata_ed<- imdata_ed %>% normalize(method="rms") %>% process()
}
}
}
MASHUOA/HiTMaP documentation built on May 1, 2024, 8:26 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions memory_profile
memory_profile<-function(){
#install.packages("ggplot2")
#install.packages("pryr")
#install.packages("devtools")
#devtools::install_github("hadley/lineprof")
#install.packages("tictoc")
library(Cardinal)
library(HiTMaP)
library(pryr)
library(stringr)
library(tictoc)
tic.clearlog()
for(x in 1:10) {
tic(x)
Sys.sleep(.001)
toc(log=TRUE,quiet=TRUE)
}
log.txt<-tic.log(format=TRUE)
log.lst<-tic.log(format=FALSE)
tic.clearlog()
wd="~/expdata/"
wd->workdir
z=1
ppm=10
instrument_ppm=10
parallel=4
BPPARAM=HiTMaP:::Parallel.OS(parallel,bpexportglobals=F,bpforceGC=T, bpprogressbar_t=TRUE)
setCardinalBPPARAM(BPPARAM = BPPARAM)
set.seed(2020)
# mse <- simulateImage(preset=1, npeaks=10, nruns=2, baseline=1)
#mse
import_ppm = floor(instrument_ppm*2/5)[1]
datafile=c("MouseBrain_Trypsin_FT/Mouse_brain.imzML")
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
datafile <- paste0(workdir,"/",datafile)
workdir <- dirname(datafile)
datafile <- basename(datafile)
datafile <-str_remove(datafile,regex(".imzML$"))
datafile_imzML<-paste0(datafile,".imzML")
setwd(workdir[z])
log.txt<-NULL
imdata <- Cardinal::readMSIData(datafile_imzML[z], attach.only=F,as="MSImagingExperiment",resolution=10, units="ppm",BPPARAM=SerialParam())
imdata@centroided<-F
imdata->imdata_org
imdata[,2800:3000]->imdata
tic.clearlog()
for (resolution in c(10, 200,400,800)){
resolution(imdata) <- c(ppm=resolution)
tic(resolution)
image(imdata)
toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="gaussian"),
reduceBaseline=list(method="median"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdata_ed<-imdata
log.txt<-NULL
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10)){
imdata_ed<-imdata
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP"))
#imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
#imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_native
lapply(imdatalist_param_native,object_size)
lapply(imdatalist_param_native,mz)
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10)){
imdata_ed<-imdata
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP"))
#imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
#imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_default
size1<-lapply(imdatalist_param_native,object_size)
size2<-lapply(imdatalist_param_default,object_size)
mz1 <- lapply(imdatalist_param_native,function(x) length(mz(x)))
mz2 <- lapply(imdatalist_param_default, function(x) length(mz(x)))
size<-c(size1,size2)
sizedf<-data.frame(name=names(size1),native_size=unlist(size1),default_size=unlist(size2),native_mz=unlist(mz1),default_mz=unlist(mz2))
sizedf$resolution=str_split_fixed(sizedf$name," ",2)[,1]
sizedf$type=str_split_fixed(sizedf$name," ",2)[,2]
write.csv(sizedf,"sizedf.csv")
writeLines(unlist(log.txt),"log_subset.txt")
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="gaussian"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdata_ed<-imdata_org
log.txt<-NULL
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10,4)){
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"SS_RB_PP"))
imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method, SNR=preprocess$peakPick$SNR, window=preprocess$peakPick$window) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_native
lapply(imdatalist_param_native,object_size)
preprocess=list(force_preprocess=T,
use_preprocessRDS=TRUE,
smoothSignal=list(method="Disable"),
reduceBaseline=list(method="Disable"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=5, units="ppm"),
normalize=list(method=c("rms","tic","reference")[1],mz=1))
imdata_ed<-imdata_org
imdatalist<-NULL
tic.clearlog()
for (resolution in c(800,100,10,4)){
resolution(imdata_ed) <- c(ppm=resolution)
imdatalist[[paste(resolution,"ORG")]]<-imdata_ed
tic(paste(resolution,"SS_RB_PP"))
#imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
#imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_PA"))
imdata_ed_pa<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_PA")]]<-imdata_ed_pa
toc(log=TRUE,quiet=TRUE)
tic(paste(resolution,"SS_RB_PP_NR"))
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
imdatalist[[paste(resolution,"SS_RB_PP_NR")]]<-imdata_ed
toc(log=TRUE,quiet=TRUE)
# tic(paste(resolution,"SS_RB_PP_PA_NR"))
# #imdata_ed_pa<- imdata_ed_pa %>% normalize(method=preprocess$normalize$method) %>% process()
# imdatalist[[paste(resolution,"SS_RB_PP_PA_NR")]]<-imdata_ed_pa
# toc(log=TRUE,quiet=TRUE)
}
log.txt<-c(log.txt,tic.log(format=TRUE))
imdatalist->imdatalist_param_default
size1<-lapply(imdatalist_param_native,object_size)
size2<-lapply(imdatalist_param_default,object_size)
size<-c(size1,size2)
sizedf<-data.frame(name=names(size1),native=unlist(size1),default=unlist(size2))
sizedf$resolution=str_split_fixed(sizedf$name," ",2)[,1]
sizedf$type=str_split_fixed(sizedf$name," ",2)[,2]
writeLines(unlist(log.txt),"log_all.txt")
object_size(imdata_ed)
setCardinalBPPARAM(BPPARAM)
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec.csv"),row.names = F)
#peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
#write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
#smoothSignal(method="gaussian") %>%
#reduceBaseline(method="locmin") %>%
if (!is.null(preprocess$mz_bin_list)){
preprocess$mz_bin_list->mz_bin_list_s
sort(as.numeric(unlist(mz_bin_list_s)))->ref_mz
peaklist_deco<-data.frame(mz=ref_mz,intensities=rep(1,length(ref_mz)))
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
unlist(peaklist_deco[,1])->ref_mz
ref_mz<-ref_mz[!is.na(ref_mz)]
range(mz(imdata_ed))->ref_mz_range
ref_mz<-ref_mz[ref_mz*(1-ppm/1000000)>=ref_mz_range[1]]
ref_mz<-ref_mz[ref_mz*(1+ppm/1000000)<=ref_mz_range[2]]
ref_mz<-unique(ref_mz)
ref_mz<-ref_mz[!is.na(ref_mz)]
imdata_ed<-imdata_ed %>% mzBin(ref=ref_mz, tolerance=ppm, units="ppm") %>% process()
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
}else{
if (preprocess$smoothSignal$method=="Disable") {
}else if (!is.null(preprocess$smoothSignal$method)){
imdata_ed<- imdata_ed %>% smoothSignal(method=preprocess$smoothSignal$method)
}else{
imdata_ed<- imdata_ed %>% smoothSignal(method="gaussian")
}
if (preprocess$reduceBaseline$method=="Disable") {
}else if (!is.null(preprocess$reduceBaseline$method)){
imdata_ed<- imdata_ed %>% reduceBaseline(method=preprocess$reduceBaseline$method)
}else{
imdata_ed<- imdata_ed %>% reduceBaseline(method="locmin")
}
if (preprocess$peakPick$method=="Disable") {
}else if (preprocess$peakPick$method %in% c("adaptive","mad","simple")){
if (is.null(preprocess$peakPick$SNR)) preprocess$peakPick$SNR=6
if (is.null(preprocess$peakPick$window)) preprocess$peakPick$window=5
imdata_ed<- imdata_ed %>% peakPick(method=preprocess$peakPick$method, SNR=preprocess$peakPick$SNR, window=preprocess$peakPick$window) %>% process()
}else if (preprocess$peakPick$method == "Default|default"){
#add an peak picking function other than the Cardinal options.
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
imdata_ed<-imdata_ed %>% peakBin(peaklist_deco$mz, tolerance=ppm, units="ppm") %>% process()
}
}
saveRDS(imdata_ed,paste0(gsub(".imzML$","",datafile[z]) ," ID/preprocessed_peakpicked_imdata.RDS"))
if (is.null(preprocess$peakAlign$level)) preprocess$peakAlign$level<-"global"
if (preprocess$peakAlign$level=="global"){
if (preprocess$peakAlign$tolerance==0 ) {
message("preprocess$peakAlign$tolerance set as zero, step bypassed")
}else if ('&'(!is.null(preprocess$peakAlign$tolerance),!is.null(preprocess$peakAlign$tolerance))){
message("preprocess$peakAlign$tolerance set as ", preprocess$peakAlign$tolerance)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units)
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec.csv"),row.names = F)
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
}else {
message("preprocess$peakAlign$tolerance missing, use default tolerance in ppm ", ppm/2)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=ppm/2, units="ppm")
peaklist<-summarizeFeatures(imdata_ed,"sum", as="DataFrame")
peaklist_deco<-data.frame(mz=peaklist@mz,intensities=peaklist$sum)
peaklist_deco<-peaklist_deco[peaklist_deco$intensities>0,]
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec.csv"),row.names = F)
peaklist_deco<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist_deco,ppm=ppm,threshold=0)
write.csv(peaklist_deco,paste0(gsub(".imzML$","",datafile[z]) ," ID/Sum_spec_decov.csv"),row.names = F)
}
}
imdata_ed<- imdata_ed %>% process()
gc()
if (!is.null(preprocess$normalize)){
if (preprocess$normalize$method=="Disable") {
} else if (preprocess$normalize$method %in% c("rms","tic")){
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method) %>% process()
} else if ('&'(preprocess$normalize$method == "reference", !is.null(preprocess$normalize$mz))){
norm_feature<-which(dplyr::between(imdata_ed@featureData@mz,
preprocess$normalize$mz*(1-ppm/1000000),
preprocess$normalize$mz*(1+ppm/1000000)))
if (length(norm_feature)>=1){
imdata_ed<- imdata_ed %>% normalize(method=preprocess$normalize$method, feature = norm_feature) %>% process(BPPARAM=SerialParam())
}
} else {
imdata_ed<- imdata_ed %>% normalize(method="rms") %>% process()
}
}
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.