R/workflow.R
In MASHUOA/HiTMaP: A collection of tools for imaging MS data processing
Defines functions
imaging_identification_target
IMS_data_process
imaging_identification
Documented in
imaging_identification
#' imaging_identification
#'
#' This is a peptide mass fingerprint search function for maldi imaging data analysis
#' @param datafile the data files' path for the analysis, leave it as blank to enable a graphical user interface to select the data
#' @param projectfolder optional, if NULL script will extract the path from datafile(s), and use the first workdir as project folder
#' @param parallel the number of threads will be used in the PMF search, this option now only works for windows OS
#' @param threshold specify the intensities threshold (0 to 1 in percentage)to report a identified molecule
#' @param ppm the mz tolerance (in ppm) for peak integration
#' @param Digestion_site Set the enzyme digestion specificity by one or more regex expressions or the name of a enzyme
#' @param missedCleavages miss cleavage number allowed in this PMF search
#' @param Fastadatabase the fasta database used in this pmf search, the file should be placed in the same folder with data files
#' @param adducts the adducts list to be used for generating the PMF search candidates
#' @param Modifications set the modifications
#' @param Substitute_AA set the amino acid Substitutions
#' @param Decoy_search enable (default) or disable the decoy search
#' @param Decoy_mode select the decoy search mode between "isotope" (default), "element" and "adduct"
#' @param Decoy_adducts define the adduct list for decoy search. the decoy adducts could be "M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe" or"M+Rn".
#' @param mzrange define the mz range for the experiment, default is 700 to 4000 m/z.
#' @param use_previous_candidates set as TRUE to reload the previously generated candidate list.
#' @param IMS_analysis Set \code{"true"} if you want to perform data pre-processing and proteomics search, set \code{"false"} if you want to bypass it
#' @param FDR_cutoff set the protein FDR cutoff threshold, default is 5 percent
#' @param score_method specify the peptide spectrum scoring method, "SQRTP" is recommended.
#' @param peptide_ID_filter set the minimal count of peptides needed to identify a protein
#' @param plot_matching_score enable the spectrum matching overlay plot
#' @param Protein_feature_summary \code{"IMS_analysis"} follow-up process that will collect all the identified peptide information and associate them with possible proteins
#' @param Peptide_feature_summary \code{"IMS_analysis"} follow-up process that will summarize all datafiles identified peptides and generats a \code{"peptide shortlist"} in the result summary folder
#' @param Region_feature_summary \code{"IMS_analysis"} follow-up process that will summarize mz feature of all regions of all data files into the summary folder
#' @param plot_ion_image \code{"Peptide_feature_summarya"} follow-up process that will plot every connponents in the \code{"peptide shortlist"}. please use the cluster image grid to output the images.
#' @param preprocess a list of params that define the IMS data pre-processing procedure
#' @param spectra_segments_per_file optimal number of distinctive regions in the tissue section, a virtual segmentation will be applied to the image files with this value. To have a better PMF result you may set a value that in the sweet point of sensitivety and false discovery rate (FDR).
#' @param Segmentation set as "spatialKMeans" to enable a \code{"spatialKMeans"} Segmentation; set as "spatialShrunkenCentroids" to enable a \code{"spatialShrunkenCentroids"} Segmentation; If a region rank file was supplied, you can set this as "Virtual_segmentation" to perform a manual segmentation; Set it as "none" to bypass the segmentation.
#' @param Smooth_range \code{"Segmentation"} pixel smooth range
#' @param Virtual_segmentation_rankfile specify a region rank file contains region information for manualy region segmentation
#' @param Rotate_IMG specify a configuration file to further change the rotation of the images
#' @param plot_cluster_image_grid set as \code{"TRUE"} to enable the protein cluster image function.
#' @param plot_layout Set as \code{"line"} to plot cluster and component images for multiple data file or as \code{"grid"} to plot cluster images for single data file. In "grid" mode, Image's will be rendered into a grid with 5 columns.
#' @param ClusterID_colname Specify the cluster ID column in the result spreadsheet.
#' @param componentID_colname Specify the component ID column in the result spreadsheet.
#' @param Protein_desc_of_interest Specify a list of protein descriptions for cluster image plotting. Default setting will plot all reported proteins.
#' @param Protein_desc_of_exclusion Specify a list of protein descriptions to be excluded from cluster image plotting.
#' @param plot_unique_component Set as \code{"TRUE"} to plot only the unique components in the cluster image plotting.
#' @param Component_plot_coloure set as "mono" to use a pre-defined color scale to plot component images. Set as "as.cluster" to use the previously assigned mono color in the additive cluster binning process.
#' @param cluster_color_scale Set as "blackwhite" to use only black and white color in the cluster image plotting. using "blackwhite" in cluster_color_scale will overwrite the components' color setting.
#' @param export_Header_table Set as \code{"TRUE"} to plot the header in the cluster image plotting. Header table includes the basic information of cluster and components.
#' @param export_footer_table Set as \code{"TRUE"} to plot the footer in the cluster image plotting. Footer shows the protein coverage in the Proteomics mode.
#' @param attach_summary_cluster Set as \code{"TRUE"} to attach an enlarged cluster image to the bottom of the cluster image.
#' @param remove_cluster_from_grid Set as \code{"TRUE"} to remove the cluster image from the cluster image grid. it is recommended to set this same as the attach_summary_cluster.
#' @param plot_cluster_image_overwrite Set as true to generate the cluster images regardless the existance of previously file(s)
#' @param cluster_rds_path set as NULL if there is not preprocessed.rds available for a single file, script will load the raw data file which may reduce the signal intensities. For multiple samples, scripts will try to load the RDS file from each "ID" folder and merge the mz features via instrument resolution setting and output a combined RDS file to the project folder. For multiple files cluster images rendering user should set the attach_summary_cluster as False, and set remove_cluster_from_grid as true.
#'
#'
#' @return None
#'
#' @examples
#' imaging_identification(threshold=0.05, ppm=5,Digestion_site="[G]",
#' missedCleavages=0:1,Fastadatabase="murine_matrisome.fasta",
#' adducts=c("M+H","M+NH4","M+Na"),IMS_analysis=TRUE,
#' Protein_feature_summary=TRUE,plot_cluster_image=TRUE,
#' Peptide_feature_summary=TRUE,plot_ion_image=FALSE,
#' parallel=3,spectra_segments_per_file=5,Segmentation="spatialKMeans"
#' )
#'
#' @export
#'
imaging_identification<-function(
#==============Choose the imzml raw data file(s) to process make sure the fasta file in the same folder
datafile,
projectfolder=NULL,
threshold=0.001,
ppm=5,
mode=c("Proteomics","Metabolomics"),
Digestion_site="trypsin",
missedCleavages=0:1,
Fastadatabase="uniprot-bovin.fasta",
adducts=c("M+H"),
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=NULL,varmod_position=NULL),
Substitute_AA=NULL,
Decoy_search=TRUE,
Decoy_adducts=c("M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe","M+Rn"),
Decoy_mode = "isotope",
mzrange=c(700,4000),
Database_stats=F,
adjust_score = FALSE,
IMS_analysis=TRUE,
PMFsearch=IMS_analysis,
Load_candidatelist=IMS_analysis || plot_cluster_image_grid,
Bypass_generate_spectrum=FALSE,
peptide_ID_filter=2,
Protein_feature_summary=TRUE,
Peptide_feature_summary=TRUE,
plot_ion_image=FALSE,
parallel=detectCores(),
spectra_segments_per_file=4,
Segmentation=c("spatialKMeans","spatialShrunkenCentroids","Virtual_segmentation","none","def_file"),
Segmentation_def="Segmentation_def.csv",
Segmentation_ncomp="auto-detect",
Segmentation_variance_coverage=0.8,
preprocess=list(force_preprocess=FALSE,use_preprocessRDS=TRUE,smoothSignal=list(method="disable"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=ppm/2, units="ppm"),
peakFilter=list(freq.min=0.05),
normalize=list(method=c("rms","tic","reference")[1],mz=1)),
Smooth_range=1,
Virtual_segmentation_rankfile=NULL,
Rotate_IMG=NULL,
Region_feature_summary=FALSE,
Spectrum_validate=TRUE,
output_candidatelist=TRUE,
use_previous_candidates=FALSE,
score_method="SQRTP",
plot_cluster_image_grid=FALSE,
deconv_peaklist="New",
plot_cluster_image_maxretry=2,
plot_cluster_image_overwrite=F,
smooth.image="gaussian",
componentID_colname="Peptide",
ClusterID_colname="Protein",
Protein_desc_of_interest=".",
Protein_desc_of_exclusion=NULL,
plot_unique_component=TRUE,
FDR_cutoff=0.05,
use_top_rank=NULL,
plot_matching_score=F,
Component_plot_coloure="mono",
cluster_color_scale="blackwhite",
plot_layout="line",
export_Header_table=T,
export_footer_table=T,
attach_summary_cluster=T,
remove_cluster_from_grid=attach_summary_cluster,
pixel_size_um=50,
img_brightness=100,
Thread=4,
cluster_rds_path=NULL,
remove_score_outlier=F,
Plot_score_IQR_cutoff=0.75,
Plot_score_abs_cutoff=-0.1,
mzAlign_runs="TopNfeature_mean",
...
){
suppressMessages(suppressWarnings(library("pacman")))
suppressMessages(suppressWarnings(p_load(stringr,BiocParallel,data.table,Cardinal,parallel)))
if (missing(datafile)) stop("Missing data file, Choose single or multiple imzml file(s) for analysis")
# retrieve/parse the working dir info, and convert the filenames
if (is.null(projectfolder)){
workdir<-base::dirname(datafile[1])
}else{ workdir<-projectfolder }
datafile <- basename(datafile)
datafile <- gsub(".imzML$", "", datafile)
datafile_imzML <- paste0(datafile,".imzML")
setwd(paste0(workdir[1],"/"))
# Set the parallel processing parameter, multicore-fork method has been temporarily disabled due to the reduced performance in docker enviornment
if (is.null(Thread)){
parallel=try(future::availableCores()/2)
if (parallel<1 | is.null(parallel)){parallel=1}
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}else{
parallel=Thread
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}
message(paste(try(future::availableCores()), "Cores detected,",parallel, "threads will be used for computing"))
message(paste(length(datafile), "files were selected and will be used for Searching"))
message(paste(Fastadatabase, "was selected as database.", "Candidates will be generated through",mode[1] ,"mode" ))
# prepare the proteome database, use_previous_candidates=T will override the other argument and load the candidate list directly
if(Load_candidatelist){
Protein_feature_list<-Protein_feature_list_fun(workdir=workdir,
database=Fastadatabase,
Digestion_site=Digestion_site,
missedCleavages=missedCleavages,
adducts=adducts,
BPPARAM = BPPARAM,
Decoy_adducts=Decoy_adducts,
Decoy_search=Decoy_search,
Decoy_mode = Decoy_mode,
output_candidatelist=output_candidatelist,
use_previous_candidates=use_previous_candidates,
Substitute_AA=Substitute_AA,
Modifications=Modifications,
mzrange=mzrange,
Protein_desc_of_exclusion=Protein_desc_of_exclusion,
Database_stats=Database_stats)
}
#
if(IMS_analysis){
message(paste(Fastadatabase,"was selected as database","\nSpectrum intensity threshold:",percent(threshold),"\nmz tolerance:",ppm,"ppm","Segmentation method:",Segmentation[1],
"\nManual segmentation def file:",ifelse(is.null(Virtual_segmentation_rankfile),"None",Virtual_segmentation_rankfile),"\nBypass spectrum generation:",Bypass_generate_spectrum))
#select candidate list for IMS annotation
Peptide_Summary_searchlist<-unique(Protein_feature_list)
Peptide_Summary_file<-IMS_data_process(datafile=datafile, workdir=workdir,
Peptide_Summary_searchlist=Peptide_Summary_searchlist,
segmentation_num=spectra_segments_per_file,
threshold=threshold,rotate = Rotate_IMG,
ppm=ppm,mzrange=mzrange,
Segmentation=Segmentation,
Segmentation_ncomp=Segmentation_ncomp,
PMFsearch = PMFsearch,
Virtual_segmentation_rankfile = Virtual_segmentation_rankfile,
BPPARAM = BPPARAM,
Bypass_generate_spectrum=Bypass_generate_spectrum,
score_method = score_method,
Decoy_mode=Decoy_mode,
Decoy_search=Decoy_search,
adjust_score=adjust_score,
peptide_ID_filter=peptide_ID_filter,
Protein_desc_of_interest=Protein_desc_of_interest,
plot_matching_score_t=plot_matching_score,
FDR_cutoff= FDR_cutoff,
Segmentation_def=Segmentation_def,
Segmentation_variance_coverage=Segmentation_variance_coverage,
preprocess=preprocess)
}
#Summarize the protein result across the datafiles and store these summarized files into the summary folder
if(Protein_feature_summary){
message("Protein feature summary...")
Peptide_Summary_file<-NULL
Protein_peptide_Summary_file<-NULL
protein_feature_all<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
protein_feature<-NULL
for (protein_feature_file in dir()[stringr::str_detect(dir(),"Protein_segment_PMF_RESULT_")]){
protein_feature<-fread(protein_feature_file)
if(nrow(protein_feature)!=0){
protein_feature$Source<-datafilename
region_code<-str_replace(protein_feature_file,"Protein_segment_PMF_RESULT_","")
region_code<-str_replace(region_code,".csv","")
protein_feature$Region<-region_code
protein_feature_all<-rbind(protein_feature_all,protein_feature)
}
}
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-datafilename
if(nrow(Peptide_Summary_file)!=0){
Protein_peptide_Summary_file<-rbind(Protein_peptide_Summary_file,Peptide_Summary_file)
}
}
message("Protein feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(protein_feature_all,paste(workdir,"/Summary folder/Protein_Summary.csv",sep=""),row.names = F)
write.csv(Protein_peptide_Summary_file,paste(workdir,"/Summary folder/Protein_peptide_Summary.csv",sep=""),row.names = F)
}
#Summarize the protein and peptide result across the datafiles and store these summarized files into the summary folder
if(Peptide_feature_summary){
message("Peptide feature summary...")
Peptide_Summary_file<-NULL
Peptide_Summary_file_a<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-gsub(".imzML", "", datafile[i])
if(nrow(Peptide_Summary_file)!=0){
Peptide_Summary_file_a<-rbind(Peptide_Summary_file_a,Peptide_Summary_file)
}
}
Peptide_Summary_file_a<-unique(Peptide_Summary_file_a)
message("Peptide feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(Peptide_Summary_file_a,paste(workdir,"/Summary folder/Peptide_Summary.csv",sep=""),row.names = F)
#Peptide_feature_summary_all_files_new(datafile,workdir,threshold = threshold)
}
#Summarize the mz feature list
if(Region_feature_summary){
message("Region feature summary...")
Spectrum_summary<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(currentdir)
name <-gsub(base::dirname(datafile[i]),"",gsub(".imzML", "", datafile[i]))
message(paste("Region_feature_summary",gsub(".imzML", "", datafile[i])))
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
match_pattern <- "Spectrum.csv"
spectrum_file_table_sum<-NULL
for (spectrum_file in dir(recursive = T)[str_detect(dir(recursive = T), match_pattern)]){
spectrum_file_table=fread(spectrum_file)
if (nrow(spectrum_file_table)>=1){
spectrum_file_table$Region=gsub("/Spectrum.csv","",spectrum_file)
spectrum_file_table$Source<-gsub(".imzML", "", datafile[i])
spectrum_file_table_sum[[spectrum_file]] <- spectrum_file_table
}
}
spectrum_file_table_sum <- do.call(rbind,spectrum_file_table_sum)
Spectrum_summary[[datafile[i]]]=spectrum_file_table_sum
}
Spectrum_summary <- do.call(rbind,Spectrum_summary)
write.csv(Spectrum_summary,file = paste(workdir,"/Summary folder/Region_feature_summary.csv",sep=""),row.names = F)
}
#Protein cluster image rendering
if(plot_cluster_image_grid){
message("cluster image rendering...")
setwd(workdir[1])
# read protein-peptide features result
Protein_feature_list=read.csv(file=paste(workdir[1],"/Summary folder/Protein_peptide_Summary.csv",sep=""),stringsAsFactors = F)
# remove peptide score outlier from result
if (remove_score_outlier){
Protein_feature_list <- remove_pep_score_outlier(Protein_feature_list,abs_cutoff=Plot_score_abs_cutoff,IQR_LB = Plot_score_IQR_cutoff)
}
# extract the protein entries of interest
if (sum(Protein_desc_of_interest!=".")>=1){
Protein_feature_list_interest<-NULL
num_of_interest<-numeric(0)
for (interest_desc in Protein_desc_of_interest){
idx_iterest_desc<-str_detect(Protein_feature_list$desc,regex(interest_desc,ignore_case = T))
if(nrow(Protein_feature_list[idx_iterest_desc,])!=0){
Protein_feature_list_interest<-rbind(Protein_feature_list_interest,Protein_feature_list[idx_iterest_desc,])
}
num_of_interest[interest_desc]<-length(unique(Protein_feature_list[idx_iterest_desc,"Protein"]))
}
Protein_feature_list=Protein_feature_list_interest
message(paste(num_of_interest,"Protein(s) found with annotations of interest:",Protein_desc_of_interest,collapse = "\n"))
}
Protein_feature_list=as.data.frame(Protein_feature_list)
# generate combined IMS data for multiple files or use a link to load the pre-processed IMS data
if (!is.null(cluster_rds_path)){
cluster_rds_path
imdata=readRDS(paste0(workdir[1],"/",cluster_rds_path))
message("cluster imdata loaded.")
}else{
cluster_rds_path<-Load_IMS_decov_combine(datafile=datafile,workdir=workdir,import_ppm=ppm,SPECTRUM_batch="overall",mzAlign_runs=mzAlign_runs,
ppm=ppm,threshold=0,rotate=Rotate_IMG,mzrange=mzrange,
deconv_peaklist=deconv_peaklist,preprocessRDS_rotated=T,target_mzlist=sort(unique(as.numeric(Protein_feature_list$mz)),decreasing = F))
imdata=readRDS(paste0(workdir[1],"/",basename(cluster_rds_path)))
message("cluster imdata generated and loaded.")
}
# test combined imdata
if (class(imdata)[1]=="matrix"){
do.call(Cardinal::cbind,imdata)->imdata
saveRDS(imdata,paste0(workdir[1],"/combinedimdata.rds"),compress = T)
}
# Setup output folder and queue the R calls for cluster image randering
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
if (!(plot_unique_component)){
setwd(outputfolder)
Protein_feature_list_trimmed<-Protein_feature_list
}
if (plot_unique_component){
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/unique/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
setwd(outputfolder)
Protein_feature_list_unique=Protein_feature_list %>% group_by(mz) %>% dplyr::summarise(num=length(unique(Protein)))
Protein_feature_list_unique_mz<-Protein_feature_list_unique$mz[Protein_feature_list_unique$num==1]
Protein_feature_list_trimmed<-Protein_feature_list[Protein_feature_list$mz %in% Protein_feature_list_unique_mz, ]
write.csv(Protein_feature_list_trimmed,paste(workdir,"/Summary folder/Protein_feature_list_trimmed.csv",sep=""),row.names = F)
}
#list_of_protein_sequence[!(1:length(list_of_protein_sequence) %in% as.numeric(Protein_feature_list_trimmed$Protein))]<-""
#unname(as.character(list_of_protein_sequence))->str_vec
#imdata <- imdata %>% peakBin(ref=sort(unique((Protein_feature_list_trimmed$mz_align+Protein_feature_list_trimmed$mz)/2)), tolerance=ppm, units="ppm") %>% process(BPPARAM=SerialParam())
save(list=c("Protein_feature_list_trimmed",
"imdata",
"ClusterID_colname",
"componentID_colname",
"plot_layout",
"export_Header_table",
"export_footer_table",
"attach_summary_cluster",
"remove_cluster_from_grid",
"smooth.image",
"Component_plot_coloure",
"cluster_color_scale",
"list_of_protein_sequence",
"outputfolder",
"peptide_ID_filter",
"ppm",
"img_brightness",
"pixel_size_um"
),
file=paste0(workdir,"/cluster_img_grid.RData"))
for (clusterID in unique(Protein_feature_list_trimmed$Protein)){
cluster_desc<-unique(Protein_feature_list_trimmed$desc[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID])
cluster_desc<-gsub(stringr::str_extract(cluster_desc,"OS=.{1,}"),"",cluster_desc)
n_component<-nrow(unique(Protein_feature_list_trimmed[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID,c(ClusterID_colname,componentID_colname,"moleculeNames","adduct","Modification")]))
if (n_component>=peptide_ID_filter){
if ('&'(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png")),!plot_cluster_image_overwrite)){
message("Cluster image rendering Skipped file exists: No.",clusterID," ",cluster_desc)
next
}else {
fileConn<-file(paste0(workdir,"/cluster_img_scource.R"),)
writeLines(c("suppressMessages(suppressWarnings(require(HiTMaP)))",
paste0("clusterID=",clusterID),
paste0("suppressMessages(suppressWarnings(load(file =\"", workdir,"/cluster_img_grid.RData\")))"),
"suppressMessages(suppressWarnings(cluster_image_grid(clusterID = clusterID,
imdata=imdata,
SMPLIST=Protein_feature_list_trimmed,
ppm=ppm,
ClusterID_colname=ClusterID_colname,
componentID_colname=componentID_colname,
plot_layout=plot_layout,
export_Header_table=export_Header_table,
export_footer_table=export_footer_table,
attach_summary_cluster=attach_summary_cluster,
remove_cluster_from_grid=remove_cluster_from_grid,
plot_style=\"fleximaging\",
smooth.image=smooth.image,
Component_plot_coloure=Component_plot_coloure,
cluster_color_scale=cluster_color_scale,
list_of_protein_sequence=list_of_protein_sequence,
workdir=outputfolder,
pixel_size_um=pixel_size_um,
img_brightness=img_brightness,
Component_plot_threshold=peptide_ID_filter)))"),
fileConn)
close(fileConn)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
}else{
retrytime=1
repeat{
message("Cluster image rendering failed and retry ",retrytime,": No.",clusterID," ",cluster_desc)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if (file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
break
}else if(retrytime>=plot_cluster_image_maxretry){
message("Cluster image rendering reaches maximum Retry Attempts: No.",clusterID," ",cluster_desc)
break
}
retrytime=1+retrytime
}
}
}
}
}
}
message("Workflow done.")
}
IMS_data_process<-function(datafile,
workdir=NULL,
Peptide_Summary_searchlist,
segmentation_num=5,threshold=0.001,TopNFeatures=1250,
ppm,import_ppm=5,
mzrange="auto-detect",
Segmentation=c("spatialKMeans","spatialShrunkenCentroids","Virtual_segmentation","none","def_file"),
Segmentation_def="segmentation_def.csv",
Segmentation_ncomp="auto-detect",
Segmentation_variance_coverage=0.8,
Bypass_Segmentation=F,
Smooth_range=1,
colorstyle="Set1",
Virtual_segmentation_rankfile=NULL,
PMFsearch=TRUE,
rotate=NULL,
matching_validation=T,
BPPARAM=bpparam(),
Bypass_generate_spectrum=F,
scoring=T,
score_method="SQRTP",
Rank=3,
Decoy_mode="",
Decoy_search=T,
adjust_score=T,
plot_matching_score_t=F,
Protein_feature_list,
peptide_ID_filter=2,
Protein_desc_of_interest=".",
FDR_cutoff=0.1,
use_top_rank=NULL,
preprocess=list(force_preprocess=FALSE,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)),
...){
suppressMessages(suppressWarnings(require(data.table)))
suppressMessages(suppressWarnings(require(Cardinal)))
suppressMessages(suppressWarnings(require(RColorBrewer)))
suppressMessages(suppressWarnings(require(stringr)))
getPalette = colorRampPalette(brewer.pal_n(9, colorstyle))
setCardinalBPPARAM(BPPARAM)
# resolve the filename, project folder and rotation info
datafile <- paste0(workdir,"/",datafile)
workdir <- dirname(datafile)
datafile <- basename(datafile)
rotate=Parse_rotation(datafile,rotate)
datafile_imzML<-datafile
# perform the IMS analysis
for (z in 1:length(datafile)){
#file related variables
name <-basename(datafile[z])
name <-gsub(".imzML$","",name)
name <-gsub("/$","",name)
folder<-base::dirname(datafile[z])
if (!str_detect(datafile[z],".imzML$")){
datafile_imzML[z]<-paste0(datafile[z],".imzML")
}
#perform the IMS pre-processing and image segmentation
setwd(workdir[z])
segmentation_res <- Preprocessing_segmentation(datafile=datafile[z],
workdir=workdir[z],
segmentation_num=segmentation_num,
ppm=ppm,import_ppm=import_ppm,Bypass_Segmentation=Bypass_Segmentation,
mzrange=mzrange,
Segmentation=Segmentation,
Segmentation_def=Segmentation_def,
Segmentation_ncomp=Segmentation_ncomp,
Segmentation_variance_coverage=Segmentation_variance_coverage,
Smooth_range=Smooth_range,
colorstyle=colorstyle,
Virtual_segmentation_rankfile=Virtual_segmentation_rankfile,
rotate=rotate,
BPPARAM=BPPARAM,
preprocess=preprocess)
segmentation_label=segmentation_res$segmentation_label
imdata=segmentation_res$imdata
#imdata_ed=segmentation_res$imdata_ed
#imdata_org=segmentation_res$imdata_org
rm(segmentation_res)
if(PMFsearch){
if (missing(Protein_feature_list)){
Protein_feature_list=get("Protein_feature_list", envir = .GlobalEnv)
message("Got Protein_feature_list from global environment.")
}
Peptide_Summary_searchlist$Protein<-NULL
Peptide_Summary_searchlist$pro_end<-NULL
Peptide_Summary_searchlist$start<-NULL
Peptide_Summary_searchlist$end<-NULL
Peptide_Summary_searchlist<-unique(Peptide_Summary_searchlist)
Peptide_Summary_file<-Peptide_Summary_searchlist
Peptide_Summary_file$Intensity<-rep(0,nrow(Peptide_Summary_file))
setwd(workdir[z])
#remove previously genrated IMS annotation result
if (dir.exists(paste0(gsub(".imzML$","",datafile[z]) ," ID"))==FALSE){dir.create(paste0(gsub(".imzML$","",datafile[z]) ," ID"))
}else{
all_files<-dir(paste0(gsub(".imzML$","",datafile[z]) ," ID"),recursive = F)
delete_files<-all_files[str_detect(all_files,"Protein_segment_PMF_RESULT_|Peptide_|PMF spectrum match.png$")]
delete_dir<-list.dirs(path = paste0(gsub(".imzML$","",datafile[z]) ," ID"), full.names = TRUE, recursive = F)
try(suppressWarnings(file.remove(c(paste0(gsub(".imzML$","",datafile[z]) ," ID/",delete_files)))))
unlink(delete_dir, recursive = T)
}
#Start annotation for each found region
setwd(paste0(gsub(".imzML$","",datafile[z]) ," ID"))
Peptide_Summary_file_regions<-data.frame()
message(paste("PMFsearch",name))
message(paste( "region",names(segmentation_label),"Found.",sep=" ",collapse = "\n"))
for (SPECTRUM_batch in names(segmentation_label)){
message(paste("IMS_analysis",name,"region",SPECTRUM_batch))
if (dir.exists(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/"))==FALSE){dir.create(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/"))}
# if (!is.null(preprocess)){
# if ('|'(imdata@metadata[["ibd binary type"]]!="processed",preprocess$force_preprocess)){
# message(paste("Using preprocessed .rda data:",datafile[z]))
# imdata<-imdata_ed
# } else if (imdata@metadata[["ibd binary type"]]=="processed"){
# message(paste("Using preprocessed .imzml data:",datafile[z]))
# imdata<-imdata_org
# }
# }
imdata_sb <- imdata[,unlist(segmentation_label[[SPECTRUM_batch]])]
imdata_ed <- imdata_sb
if(is.null(preprocess$peakAlign$level)) preprocess$peakAlign$level<-"local"
if (preprocess$peakAlign$level=="local"){
if (preprocess$peakAlign$tolerance==0 ) {
message("preprocess$peakAlign$tolerance set as zero, step bypassed")
}else if ('&'(!is.null(preprocess$peakAlign$tolerance),!is.null(preprocess$peakAlign$units))){
message("preprocess$peakAlign$tolerance set as ", preprocess$peakAlign$tolerance)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units)
}else {
message("preprocess$peakAlign$tolerance missing, use default tolerance in ppm ", ppm/2)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=ppm/2, units="ppm")
}
imdata_ed <- imdata_ed %>% process()
}
imdata_sb <- imdata_ed
#generate spectrum for each found region
spectrum_file_table<- summarizeFeatures(imdata_sb, FUN = "mean")
spectrum_file_table<-data.frame(mz=spectrum_file_table@featureData@mz,mean=spectrum_file_table@featureData@listData[["mean"]])
peaklist<-spectrum_file_table
colnames(peaklist)<-c("m.z","intensities")
savename=paste(name,SPECTRUM_batch)
peaklist<-peaklist[peaklist$intensities>0,]
write.csv(peaklist,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Spectrum.csv"),row.names = F)
#generate filtered processed peaklist to next PMF step
deconv_peaklist<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist,ppm=ppm,threshold=0)
deconv_peaklist_thres_id<-deconv_peaklist$intensities>=max(deconv_peaklist$intensities)*threshold
deconv_peaklist_topN_id<-rank(deconv_peaklist$intensities,)>(max(rank(deconv_peaklist$intensities,))-TopNFeatures)
deconv_peaklist_PMF_id<-`|`(deconv_peaklist_thres_id,deconv_peaklist_topN_id)
peaklist_pmf<-deconv_peaklist[deconv_peaklist_PMF_id,]
message(paste(nrow(peaklist_pmf),"mz features subjected to 1st PMF search") )
#Do first round of peptide search to get putative result
mz_feature_list<-Do_PMF_search(peaklist_pmf,Peptide_Summary_searchlist,BPPARAM=BPPARAM,ppm = ppm)
mz_feature_list<-unique(mz_feature_list)
message("Summarizing peptide information...")
Peptide_Summary_searchlist<-as.data.table(Peptide_Summary_searchlist)
mz_feature_list<-as.data.table(mz_feature_list)
Peptide_Summary_searchlist$Intensity<-NULL
mz_feature_list$mz<-as.character(mz_feature_list$mz)
Peptide_Summary_searchlist$mz<-as.character(Peptide_Summary_searchlist$mz)
Peptide_Summary_searchlist<-merge(Peptide_Summary_searchlist,mz_feature_list,by.x="mz",by.y="mz",all.x=T,sort=F)
Peptide_Summary_searchlist$Intensity[is.na(Peptide_Summary_searchlist$Intensity)]<-0
Peptide_plot_list<-Peptide_Summary_searchlist[Peptide_Summary_searchlist$Intensity>0,]
Peptide_plot_list$formula<-as.character(Peptide_plot_list$formula)
if(is.null(Peptide_plot_list$moleculeNames)){Peptide_plot_list$moleculeNames=Peptide_plot_list$Peptide}
Peptide_plot_list$Region=SPECTRUM_batch
Peptide_plot_list=Peptide_plot_list[(!is.na(Peptide_plot_list$Intensity)),]
message(paste("1st run returns",nrow(Peptide_plot_list), "peptide candidates"))
write.csv(Peptide_plot_list,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_1st_ID.csv"),row.names = F)
#perform peptide-protein scoring and FDR cut off
if (nrow(Peptide_plot_list)==0){
next
}
if (scoring){
suppressMessages(suppressWarnings(require(enviPat)))
suppressMessages(suppressWarnings(require(ggplot2)))
data(isotopes)
unique_formula<-unique(Peptide_plot_list$formula)
Peptide_plot_list_Score=(bplapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=isotopes,score_method=score_method,charge = 1,ppm=ppm,BPPARAM = BPPARAM))
#Peptide_plot_list_Score=(lapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=isotopes,score_method=score_method,charge = 1,ppm=ppm))
Peptide_plot_list_Score_m=as.data.frame(do.call(rbind, Peptide_plot_list_Score))
names(Peptide_plot_list_Score_m)<-c("Score", "delta_ppm","Intensity")
formula_score<-data.frame(formula=unique_formula,Score=Peptide_plot_list_Score_m$Score,Delta_ppm=Peptide_plot_list_Score_m$delta_ppm,Intensity=Peptide_plot_list_Score_m$Intensity)
Peptide_plot_list$Score<-NULL
Peptide_plot_list$Delta_ppm<-NULL
Peptide_plot_list$Intensity<-NULL
Peptide_plot_list<-merge(Peptide_plot_list,formula_score,by="formula")
#generate decoy candidate list if running in "isotope" decoy mode
if (Decoy_search && ("isotope" %in% Decoy_mode)){
decoy_isotopes=isotopes
decoy_isotopes[decoy_isotopes$isotope=="13C",]=data.frame(element="C",isotope="11C",mass=10.99664516,aboudance=0.0107,ratioC=0,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="15N",]=data.frame(element="N",isotope="13N",mass=13.00603905,aboudance=0.00364000,ratioC=4,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="2H",]=data.frame(element="H",isotope="0H",mass=0.001548286,aboudance=0.00011500,ratioC=6,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="17O",]=data.frame(element="O",isotope="15O",mass=14.99069774,aboudance=0.00038000,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="18O",]=data.frame(element="O",isotope="14O",mass=13.99066884,aboudance=0.00205000,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="33S",]=data.frame(element="S",isotope="31S",mass=30.97268292,aboudance=0.0075,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="34S",]=data.frame(element="S",isotope="30S",mass=29.9762745,aboudance=0.0425,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="35S",]=data.frame(element="S",isotope="29S",mass=28.94414146,aboudance=0,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="36S",]=data.frame(element="S",isotope="28S",mass=27.97706058,aboudance=0.0001,ratioC=3,stringsAsFactors = F)
Peptide_plot_list_decoy<-Peptide_plot_list[Peptide_plot_list$isdecoy==0,]
Peptide_plot_list_decoy$isdecoy=1
unique_formula<-unique(Peptide_plot_list_decoy$formula)
Peptide_plot_list_Score=(bplapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=decoy_isotopes,score_method=score_method,charge = 1,ppm=ppm,BPPARAM = BPPARAM))
#Peptide_plot_list_Score=(lapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=decoy_isotopes,score_method=score_method,charge = 1,ppm=ppm))
Peptide_plot_list_Score_m=as.data.frame(do.call(rbind, Peptide_plot_list_Score))
names(Peptide_plot_list_Score_m)<-c("Score", "delta_ppm","Intensity")
formula_score<-data.frame(formula=unique_formula,Score=Peptide_plot_list_Score_m$Score,Delta_ppm=Peptide_plot_list_Score_m$delta_ppm,Intensity=Peptide_plot_list_Score_m$Intensity)
Peptide_plot_list_decoy$Score<-NULL
Peptide_plot_list_decoy$Delta_ppm<-NULL
Peptide_plot_list_decoy$Intensity<-NULL
Peptide_plot_list_decoy<-merge(Peptide_plot_list_decoy,formula_score,by="formula")
Peptide_plot_list<-rbind(Peptide_plot_list,Peptide_plot_list_decoy)
Protein_feature_list_decoy<-Protein_feature_list[Protein_feature_list$isdecoy==0,]
Protein_feature_list_decoy$isdecoy=1
Protein_feature_list=rbind(Protein_feature_list[Protein_feature_list$isdecoy==0,],Protein_feature_list_decoy)
}
Peptide_plot_list$mz<-as.numeric(as.character(Peptide_plot_list$mz))
Peptide_plot_list_rank=rank_mz_feature(Peptide_plot_list,mz_feature=deconv_peaklist,BPPARAM = BPPARAM)
Peptide_plot_list_rank<-unique(Peptide_plot_list_rank)
write.csv(Peptide_plot_list_rank,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_1st_ID_score_rank_",score_method,".csv"),row.names = F)
Peptide_plot_list_rank<-read.csv(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_1st_ID_score_rank_",score_method,".csv"),stringsAsFactors = F)
#Peptide score filtering (default=False)
if (adjust_score==F){
Score_cutoff= FDR_cutoff_plot(Peptide_plot_list_rank,FDR_cutoff=FDR_cutoff,plot_fdr=T,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch),adjust_score = adjust_score)
Peptide_plot_list_2nd=Peptide_plot_list_rank[((Peptide_plot_list_rank$Score>=Score_cutoff)&(!is.na(Peptide_plot_list_rank$Intensity))),]
}else{
Score_cutoff= FDR_cutoff_plot(Peptide_plot_list_rank,FDR_cutoff=FDR_cutoff,plot_fdr=T,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch),adjust_score = adjust_score)
Peptide_plot_list_2nd=Score_cutoff[[2]]
Score_cutoff=Score_cutoff[[1]]
Peptide_plot_list_2nd=Peptide_plot_list_2nd[((Peptide_plot_list_2nd$Score>=Score_cutoff)&(!is.na(Peptide_plot_list_2nd$Intensity))),]
}
write.csv(Peptide_plot_list_2nd,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_2nd_ID_score_rank",score_method,"_Rank_above_",Rank,".csv"),row.names = F)
#Plot peptide matching spectrum and score
if (plot_matching_score_t && nrow(Peptide_plot_list_2nd)!=0){
try(plot_matching_score(Peptide_plot_list_2nd,peaklist,charge=1,ppm,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/ppm")))
}
Peptide_plot_list_rank$mz=as.numeric(as.character(Peptide_plot_list_rank$mz))
Peptide_plot_list_2nd$mz=as.numeric(as.character(Peptide_plot_list_2nd$mz))
#Protein scoring and FDR cut-off
Protein_feature_result<-protein_scoring(Protein_feature_list,Peptide_plot_list_rank,BPPARAM = BPPARAM,scoretype="mean",peptide_ID_filter=peptide_ID_filter,use_top_rank=use_top_rank)
Protein_feature_list_rank<-Protein_feature_result[[2]]
Protein_feature_list_rank_filtered<-Protein_feature_result[[3]]
Protein_feature_list_rank_filtered_grouped<-Protein_feature_result[[4]]
Protein_feature_result<-Protein_feature_result[[1]]
if (nrow(Protein_feature_result)>=2){
Score_cutoff_protein= FDR_cutoff_plot_protein(Protein_feature_result,FDR_cutoff=FDR_cutoff,plot_fdr=T,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch),adjust_score = F)
}else{
Score_cutoff_protein=Protein_feature_result$Proscore
if (length(Score_cutoff_protein)==0) Score_cutoff_protein=0}
Protein_feature_result_cutoff=Protein_feature_result[((Protein_feature_result$Proscore>=Score_cutoff_protein)&(!is.na(Protein_feature_result$Intensity))&(Protein_feature_result$isdecoy==0)),]
Protein_feature_list_rank=Protein_feature_list_rank[Protein_feature_list_rank$Protein %in% Protein_feature_result_cutoff$Protein,]
Protein_feature_list_rank$Score=round(Protein_feature_list_rank$Score,digits = 7)
Protein_feature_list_rank$mz=round(Protein_feature_list_rank$mz,digits = 4)
Protein_feature_list_rank<-as.data.frame(Protein_feature_list_rank)
if(is.null(Protein_feature_list_rank$desc)){
if (exists("Index_of_protein_sequence", envir = .GlobalEnv)){
Index_of_protein_sequence<-get("Index_of_protein_sequence", envir = .GlobalEnv)
Protein_feature_list_rank=merge(Protein_feature_list_rank,Index_of_protein_sequence[,c("recno","desc")],by.x="Protein",by.y="recno",all.x=T)
}else{
Protein_feature_list_rank$desc<-Protein_feature_list_rank$Protein
}
}
Protein_feature_list_rank_cutoff<-Protein_feature_list_rank
Protein_feature_list_rank_cutoff<-Protein_feature_list_rank_cutoff[Protein_feature_list_rank_cutoff$isdecoy==0,]
write.csv(Protein_feature_result,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Protein_ID_score_rank_",score_method,".csv"),row.names = F)
write.csv(Protein_feature_list_rank_filtered,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Protein_ID_score_rank_filtered_",score_method,".csv"),row.names = F)
write.csv(Protein_feature_list_rank_filtered_grouped,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Protein_ID_score_rank_filtered_grouped_",score_method,".csv"),row.names = F)
write.csv(Protein_feature_list_rank_cutoff,paste0(workdir[z],"/",datafile[z] ," ID/","Peptide_segment_PMF_RESULT_",SPECTRUM_batch,".csv"),row.names = F)
#Plot Protein matching spectrum and score
png(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/unique_peptide_ranking_vs_mz_feature",".png"),width = 960,height = 480)
sp<-ggplot2::ggplot(Peptide_plot_list_rank, aes(x=mz,fill=as.factor(Rank))) + geom_bar(stat = "bin",bins = 100) +
labs(title="Matched peptide ranking vs. mz feature",x="mz", y = "Matched peptide ranking")+
theme_classic() + theme(legend.title = element_blank()) + scale_fill_manual(values =grDevices::colorRampPalette(brewer.pal(8, "RdYlBu"))(max(Peptide_plot_list_rank$Rank)))
try(print(sp))
dev.off()
message(paste("Plotting peptide matching number vs mz feature"))
Peptide_plot_list_2nd=Protein_feature_list_rank_cutoff
png(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/unique_peptide_ranking_vs_mz_feature_2nd",".png"),width = 960,height = 480)
sp<-ggplot2::ggplot(Peptide_plot_list_2nd, aes(x=mz,fill=as.factor(floor(Rank/max(Rank)*8)))) + geom_bar(stat = "bin",bins = 100) +
labs(title="Matched peptide ranking 2nd vs. mz feature",x="mz", y = "Matched peptide ranking")+
theme_classic() + theme(legend.title = element_blank()) + scale_fill_brewer()
try(print(sp))
dev.off()
write.csv(Protein_feature_result_cutoff,paste0(workdir[z],"/",datafile[z] ," ID/","Protein_segment_PMF_RESULT_",SPECTRUM_batch,".csv"),row.names = F)
}
#Plot overall matching spectrum and score
Plot_PMF_all(Peptide_plot_list_2nd,peaklist=peaklist,threshold=threshold,savename)
write.csv(peaklist,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Spectrum.csv"),row.names = F)
if (nrow(Peptide_plot_list_2nd)!=0){
Peptide_plot_list_2nd$Region<-SPECTRUM_batch
}
Peptide_Summary_file_regions<-rbind(Peptide_Summary_file_regions,Peptide_plot_list_2nd)
}
if(is.null(Peptide_Summary_file_regions$Peptide)){Peptide_Summary_file_regions$Peptide=Peptide_Summary_file_regions$moleculeNames}
if(is.null(Peptide_Summary_file_regions$moleculeNames)){Peptide_Summary_file_regions$moleculeNames=Peptide_Summary_file_regions$Peptide}
#export final result for the given data
write.csv(Peptide_Summary_file_regions,"Peptide_region_file.csv",row.names = F)
}
}
setwd(workdir[1])
}
imaging_identification_target<-function(
#==============Choose the imzml raw data file(s) to process make sure the fasta file in the same folder
datafile,
projectfolder=NULL,
threshold=0.001,
ppm=5,
mode=c("Proteomics","Metabolomics"),
Digestion_site="trypsin",
missedCleavages=0:1,
Fastadatabase="uniprot-bovin.fasta",
adducts=c("M+H"),
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=NULL,varmod_position=NULL),
Substitute_AA=NULL,
Decoy_search=TRUE,
Decoy_adducts=c("M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe","M+Rn"),
Decoy_mode = "isotope",
mzrange=c(700,4000),
Database_stats=F,
adjust_score = FALSE,
IMS_analysis=TRUE,
PMFsearch=IMS_analysis,
Load_candidatelist=IMS_analysis || plot_cluster_image_grid,
Bypass_generate_spectrum=FALSE,
peptide_ID_filter=2,
Protein_feature_summary=TRUE,
Peptide_feature_summary=TRUE,
plot_ion_image=FALSE,
parallel=detectCores(),
spectra_segments_per_file=4,
Segmentation=c("spatialKMeans","spatialShrunkenCentroids","Virtual_segmentation","none","def_file"),
Segmentation_def="Segmentation_def.csv",
Segmentation_ncomp="auto-detect",
Segmentation_variance_coverage=0.8,
preprocess=list(force_preprocess=FALSE,use_preprocessRDS=TRUE,smoothSignal=list(method="disable"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=ppm/2, units="ppm"),
peakFilter=list(freq.min=0.05),
normalize=list(method=c("rms","tic","reference")[1],mz=1)),
Smooth_range=1,
Virtual_segmentation_rankfile=NULL,
Rotate_IMG=NULL,
Region_feature_summary=FALSE,
Spectrum_validate=TRUE,
output_candidatelist=TRUE,
use_previous_candidates=FALSE,
score_method="SQRTP",
plot_cluster_image_grid=FALSE,
deconv_peaklist="New",
plot_cluster_image_maxretry=2,
plot_cluster_image_overwrite=F,
smooth.image="gaussian",
componentID_colname="Peptide",
ClusterID_colname="Protein",
Protein_desc_of_interest=".",
Protein_desc_of_exclusion=NULL,
plot_unique_component=TRUE,
FDR_cutoff=0.05,
use_top_rank=NULL,
plot_matching_score=F,
Component_plot_coloure="mono",
cluster_color_scale="blackwhite",
plot_layout="line",
export_Header_table=T,
export_footer_table=T,
attach_summary_cluster=T,
remove_cluster_from_grid=attach_summary_cluster,
pixel_size_um=50,
img_brightness=100,
Thread=4,
cluster_rds_path=NULL,
remove_score_outlier=F,
Plot_score_IQR_cutoff=0.75,
Plot_score_abs_cutoff=-0.1,
mzAlign_runs="TopNfeature_mean",
...
){
suppressMessages(suppressWarnings(library("pacman")))
suppressMessages(suppressWarnings(p_load(stringr,BiocParallel,data.table,Cardinal,parallel)))
if (missing(datafile)) stop("Missing data file, Choose single or multiple imzml file(s) for analysis")
# retrieve/parse the working dir info, and convert the filenames
if (is.null(projectfolder)){
workdir<-base::dirname(datafile[1])
}else{ workdir<-projectfolder }
datafile <- basename(datafile)
datafile <- gsub(".imzML$", "", datafile)
datafile_imzML <- paste0(datafile,".imzML")
setwd(paste0(workdir[1],"/"))
# Set the parallel processing parameter, multicore-fork method has been temporarily disabled due to the reduced performance in docker enviornment
if (is.null(Thread)){
parallel=try(detectCores()/2)
if (parallel<1 | is.null(parallel)){parallel=1}
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}else{
parallel=Thread
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}
message(paste(try(detectCores()), "Cores detected,",parallel, "threads will be used for computing"))
message(paste(length(datafile), "files were selected and will be used for Searching"))
message(paste(Fastadatabase, "was selected as database.", "Candidates will be generated through",mode[1] ,"mode" ))
# prepare the proteome database, use_previous_candidates=T will override the other argument and load the candidate list directly
if(Load_candidatelist){
Protein_feature_list<-Protein_feature_list_fun(workdir=workdir,
database=Fastadatabase,
Digestion_site=Digestion_site,
missedCleavages=missedCleavages,
adducts=adducts,
BPPARAM = BPPARAM,
Decoy_adducts=Decoy_adducts,
Decoy_search=Decoy_search,
Decoy_mode = Decoy_mode,
output_candidatelist=output_candidatelist,
use_previous_candidates=use_previous_candidates,
Substitute_AA=Substitute_AA,
Modifications=Modifications,
mzrange=mzrange,
Protein_desc_of_exclusion=Protein_desc_of_exclusion,
Database_stats=Database_stats)
}
#
if(IMS_analysis){
message(paste(Fastadatabase,"was selected as database","\nSpectrum intensity threshold:",percent(threshold),"\nmz tolerance:",ppm,"ppm","Segmentation method:",Segmentation[1],
"\nManual segmentation def file:",ifelse(is.null(Virtual_segmentation_rankfile),"None",Virtual_segmentation_rankfile),"\nBypass spectrum generation:",Bypass_generate_spectrum))
#select candidate list for IMS annotation
Peptide_Summary_searchlist<-unique(Protein_feature_list)
Peptide_Summary_file<-IMS_data_process(datafile=datafile, workdir=workdir,
Peptide_Summary_searchlist=Peptide_Summary_searchlist,
segmentation_num=spectra_segments_per_file,
threshold=threshold,rotate = Rotate_IMG,
ppm=ppm,mzrange=mzrange,
Segmentation=Segmentation,
Segmentation_ncomp=Segmentation_ncomp,
PMFsearch = PMFsearch,
Virtual_segmentation_rankfile = Virtual_segmentation_rankfile,
BPPARAM = BPPARAM,
Bypass_generate_spectrum=Bypass_generate_spectrum,
score_method = score_method,
Decoy_mode=Decoy_mode,
Decoy_search=Decoy_search,
adjust_score=adjust_score,
peptide_ID_filter=peptide_ID_filter,
Protein_desc_of_interest=Protein_desc_of_interest,
plot_matching_score_t=plot_matching_score,
FDR_cutoff= FDR_cutoff,
Segmentation_def=Segmentation_def,
Segmentation_variance_coverage=Segmentation_variance_coverage,
preprocess=preprocess)
}
#Summarize the protein result across the datafiles and store these summarized files into the summary folder
if(Protein_feature_summary){
message("Protein feature summary...")
Peptide_Summary_file<-NULL
Protein_peptide_Summary_file<-NULL
protein_feature_all<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
protein_feature<-NULL
for (protein_feature_file in dir()[stringr::str_detect(dir(),"Protein_segment_PMF_RESULT_")]){
protein_feature<-fread(protein_feature_file)
if(nrow(protein_feature)!=0){
protein_feature$Source<-datafilename
region_code<-str_replace(protein_feature_file,"Protein_segment_PMF_RESULT_","")
region_code<-str_replace(region_code,".csv","")
protein_feature$Region<-region_code
protein_feature_all<-rbind(protein_feature_all,protein_feature)
}
}
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-datafilename
if(nrow(Peptide_Summary_file)!=0){
Protein_peptide_Summary_file<-rbind(Protein_peptide_Summary_file,Peptide_Summary_file)
}
}
message("Protein feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(protein_feature_all,paste(workdir,"/Summary folder/Protein_Summary.csv",sep=""),row.names = F)
write.csv(Protein_peptide_Summary_file,paste(workdir,"/Summary folder/Protein_peptide_Summary.csv",sep=""),row.names = F)
}
#Summarize the protein and peptide result across the datafiles and store these summarized files into the summary folder
if(Peptide_feature_summary){
message("Peptide feature summary...")
Peptide_Summary_file<-NULL
Peptide_Summary_file_a<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-gsub(".imzML", "", datafile[i])
if(nrow(Peptide_Summary_file)!=0){
Peptide_Summary_file_a<-rbind(Peptide_Summary_file_a,Peptide_Summary_file)
}
}
Peptide_Summary_file_a<-unique(Peptide_Summary_file_a)
message("Peptide feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(Peptide_Summary_file_a,paste(workdir,"/Summary folder/Peptide_Summary.csv",sep=""),row.names = F)
#Peptide_feature_summary_all_files_new(datafile,workdir,threshold = threshold)
}
#Summarize the mz feature list
if(Region_feature_summary){
message("Region feature summary...")
Spectrum_summary<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(currentdir)
name <-gsub(base::dirname(datafile[i]),"",gsub(".imzML", "", datafile[i]))
message(paste("Region_feature_summary",gsub(".imzML", "", datafile[i])))
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
match_pattern <- "Spectrum.csv"
spectrum_file_table_sum<-NULL
for (spectrum_file in dir(recursive = T)[str_detect(dir(recursive = T), match_pattern)]){
spectrum_file_table=fread(spectrum_file)
if (nrow(spectrum_file_table)>=1){
spectrum_file_table$Region=gsub("/Spectrum.csv","",spectrum_file)
spectrum_file_table$Source<-gsub(".imzML", "", datafile[i])
spectrum_file_table_sum[[spectrum_file]] <- spectrum_file_table
}
}
spectrum_file_table_sum <- do.call(rbind,spectrum_file_table_sum)
Spectrum_summary[[datafile[i]]]=spectrum_file_table_sum
}
Spectrum_summary <- do.call(rbind,Spectrum_summary)
write.csv(Spectrum_summary,file = paste(workdir,"/Summary folder/Region_feature_summary.csv",sep=""),row.names = F)
}
#Protein cluster image rendering
if(plot_cluster_image_grid){
message("cluster image rendering...")
setwd(workdir[1])
# read protein-peptide features result
Protein_feature_list=read.csv(file=paste(workdir[1],"/Summary folder/Protein_peptide_Summary.csv",sep=""),stringsAsFactors = F)
# remove peptide score outlier from result
if (remove_score_outlier){
Protein_feature_list <- remove_pep_score_outlier(Protein_feature_list,abs_cutoff=Plot_score_abs_cutoff,IQR_LB = Plot_score_IQR_cutoff)
}
# extract the protein entries of interest
if (sum(Protein_desc_of_interest!=".")>=1){
Protein_feature_list_interest<-NULL
num_of_interest<-numeric(0)
for (interest_desc in Protein_desc_of_interest){
idx_iterest_desc<-str_detect(Protein_feature_list$desc,regex(interest_desc,ignore_case = T))
if(nrow(Protein_feature_list[idx_iterest_desc,])!=0){
Protein_feature_list_interest<-rbind(Protein_feature_list_interest,Protein_feature_list[idx_iterest_desc,])
}
num_of_interest[interest_desc]<-length(unique(Protein_feature_list[idx_iterest_desc,"Protein"]))
}
Protein_feature_list=Protein_feature_list_interest
message(paste(num_of_interest,"Protein(s) found with annotations of interest:",Protein_desc_of_interest,collapse = "\n"))
}
Protein_feature_list=as.data.frame(Protein_feature_list)
# generate combined IMS data for multiple files or use a link to load the pre-processed IMS data
if (!is.null(cluster_rds_path)){
cluster_rds_path
imdata=readRDS(paste0(workdir[1],"/",cluster_rds_path))
message("cluster imdata loaded.")
}else{
cluster_rds_path<-Load_IMS_decov_combine(datafile=datafile,workdir=workdir,import_ppm=ppm,SPECTRUM_batch="overall",mzAlign_runs=mzAlign_runs,
ppm=ppm,threshold=0,rotate=Rotate_IMG,mzrange=mzrange,
deconv_peaklist=deconv_peaklist,preprocessRDS_rotated=T)
imdata=readRDS(paste0(workdir[1],"/",basename(cluster_rds_path)))
message("cluster imdata generated and loaded.")
}
# test combined imdata
if (class(imdata)[1]=="matrix"){
do.call(Cardinal::cbind,imdata)->imdata
saveRDS(imdata,paste0(workdir[1],"/combinedimdata.rds"),compress = T)
}
# Setup output folder and queue the R calls for cluster image randering
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
if (!(plot_unique_component)){
setwd(outputfolder)
Protein_feature_list_trimmed<-Protein_feature_list
}
if (plot_unique_component){
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/unique/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
setwd(outputfolder)
Protein_feature_list_unique=Protein_feature_list %>% group_by(mz) %>% dplyr::summarise(num=length(unique(Protein)))
Protein_feature_list_unique_mz<-Protein_feature_list_unique$mz[Protein_feature_list_unique$num==1]
Protein_feature_list_trimmed<-Protein_feature_list[Protein_feature_list$mz %in% Protein_feature_list_unique_mz, ]
write.csv(Protein_feature_list_trimmed,paste(workdir,"/Summary folder/Protein_feature_list_trimmed.csv",sep=""),row.names = F)
}
#list_of_protein_sequence[!(1:length(list_of_protein_sequence) %in% as.numeric(Protein_feature_list_trimmed$Protein))]<-""
#unname(as.character(list_of_protein_sequence))->str_vec
#imdata <- imdata %>% peakBin(ref=sort(unique((Protein_feature_list_trimmed$mz_align+Protein_feature_list_trimmed$mz)/2)), tolerance=ppm, units="ppm") %>% process(BPPARAM=SerialParam())
save(list=c("Protein_feature_list_trimmed",
"imdata",
"ClusterID_colname",
"componentID_colname",
"plot_layout",
"export_Header_table",
"export_footer_table",
"attach_summary_cluster",
"remove_cluster_from_grid",
"smooth.image",
"Component_plot_coloure",
"cluster_color_scale",
"list_of_protein_sequence",
"outputfolder",
"peptide_ID_filter",
"ppm",
"img_brightness",
"pixel_size_um"
),
file=paste0(workdir,"/cluster_img_grid.RData"))
for (clusterID in unique(Protein_feature_list_trimmed$Protein)){
cluster_desc<-unique(Protein_feature_list_trimmed$desc[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID])
cluster_desc<-gsub(stringr::str_extract(cluster_desc,"OS=.{1,}"),"",cluster_desc)
n_component<-nrow(unique(Protein_feature_list_trimmed[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID,c(ClusterID_colname,componentID_colname,"moleculeNames","adduct","Modification")]))
if (n_component>=peptide_ID_filter){
if ('&'(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png")),!plot_cluster_image_overwrite)){
message("Cluster image rendering Skipped file exists: No.",clusterID," ",cluster_desc)
next
}else {
fileConn<-file(paste0(workdir,"/cluster_img_scource.R"),)
writeLines(c("suppressMessages(suppressWarnings(require(HiTMaP)))",
paste0("clusterID=",clusterID),
paste0("suppressMessages(suppressWarnings(load(file =\"", workdir,"/cluster_img_grid.RData\")))"),
"suppressMessages(suppressWarnings(cluster_image_grid(clusterID = clusterID,
imdata=imdata,
SMPLIST=Protein_feature_list_trimmed,
ppm=ppm,
ClusterID_colname=ClusterID_colname,
componentID_colname=componentID_colname,
plot_layout=plot_layout,
export_Header_table=export_Header_table,
export_footer_table=export_footer_table,
attach_summary_cluster=attach_summary_cluster,
remove_cluster_from_grid=remove_cluster_from_grid,
plot_style=\"fleximaging\",
smooth.image=smooth.image,
Component_plot_coloure=Component_plot_coloure,
cluster_color_scale=cluster_color_scale,
list_of_protein_sequence=list_of_protein_sequence,
workdir=outputfolder,
pixel_size_um=pixel_size_um,
img_brightness=img_brightness,
Component_plot_threshold=peptide_ID_filter)))"),
fileConn)
close(fileConn)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
}else{
retrytime=1
repeat{
message("Cluster image rendering failed and retry ",retrytime,": No.",clusterID," ",cluster_desc)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if (file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
break
}else if(retrytime>=plot_cluster_image_maxretry){
message("Cluster image rendering reaches maximum Retry Attempts: No.",clusterID," ",cluster_desc)
break
}
retrytime=1+retrytime
}
}
}
}
}
}
message("Workflow done.")
}
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 imaging_identification_target IMS_data_process imaging_identification
Documented in imaging_identification
#' imaging_identification
#'
#' This is a peptide mass fingerprint search function for maldi imaging data analysis
#' @param datafile the data files' path for the analysis, leave it as blank to enable a graphical user interface to select the data
#' @param projectfolder optional, if NULL script will extract the path from datafile(s), and use the first workdir as project folder
#' @param parallel the number of threads will be used in the PMF search, this option now only works for windows OS
#' @param threshold specify the intensities threshold (0 to 1 in percentage)to report a identified molecule
#' @param ppm the mz tolerance (in ppm) for peak integration
#' @param Digestion_site Set the enzyme digestion specificity by one or more regex expressions or the name of a enzyme
#' @param missedCleavages miss cleavage number allowed in this PMF search
#' @param Fastadatabase the fasta database used in this pmf search, the file should be placed in the same folder with data files
#' @param adducts the adducts list to be used for generating the PMF search candidates
#' @param Modifications set the modifications
#' @param Substitute_AA set the amino acid Substitutions
#' @param Decoy_search enable (default) or disable the decoy search
#' @param Decoy_mode select the decoy search mode between "isotope" (default), "element" and "adduct"
#' @param Decoy_adducts define the adduct list for decoy search. the decoy adducts could be "M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe" or"M+Rn".
#' @param mzrange define the mz range for the experiment, default is 700 to 4000 m/z.
#' @param use_previous_candidates set as TRUE to reload the previously generated candidate list.
#' @param IMS_analysis Set \code{"true"} if you want to perform data pre-processing and proteomics search, set \code{"false"} if you want to bypass it
#' @param FDR_cutoff set the protein FDR cutoff threshold, default is 5 percent
#' @param score_method specify the peptide spectrum scoring method, "SQRTP" is recommended.
#' @param peptide_ID_filter set the minimal count of peptides needed to identify a protein
#' @param plot_matching_score enable the spectrum matching overlay plot
#' @param Protein_feature_summary \code{"IMS_analysis"} follow-up process that will collect all the identified peptide information and associate them with possible proteins
#' @param Peptide_feature_summary \code{"IMS_analysis"} follow-up process that will summarize all datafiles identified peptides and generats a \code{"peptide shortlist"} in the result summary folder
#' @param Region_feature_summary \code{"IMS_analysis"} follow-up process that will summarize mz feature of all regions of all data files into the summary folder
#' @param plot_ion_image \code{"Peptide_feature_summarya"} follow-up process that will plot every connponents in the \code{"peptide shortlist"}. please use the cluster image grid to output the images.
#' @param preprocess a list of params that define the IMS data pre-processing procedure
#' @param spectra_segments_per_file optimal number of distinctive regions in the tissue section, a virtual segmentation will be applied to the image files with this value. To have a better PMF result you may set a value that in the sweet point of sensitivety and false discovery rate (FDR).
#' @param Segmentation set as "spatialKMeans" to enable a \code{"spatialKMeans"} Segmentation; set as "spatialShrunkenCentroids" to enable a \code{"spatialShrunkenCentroids"} Segmentation; If a region rank file was supplied, you can set this as "Virtual_segmentation" to perform a manual segmentation; Set it as "none" to bypass the segmentation.
#' @param Smooth_range \code{"Segmentation"} pixel smooth range
#' @param Virtual_segmentation_rankfile specify a region rank file contains region information for manualy region segmentation
#' @param Rotate_IMG specify a configuration file to further change the rotation of the images
#' @param plot_cluster_image_grid set as \code{"TRUE"} to enable the protein cluster image function.
#' @param plot_layout Set as \code{"line"} to plot cluster and component images for multiple data file or as \code{"grid"} to plot cluster images for single data file. In "grid" mode, Image's will be rendered into a grid with 5 columns.
#' @param ClusterID_colname Specify the cluster ID column in the result spreadsheet.
#' @param componentID_colname Specify the component ID column in the result spreadsheet.
#' @param Protein_desc_of_interest Specify a list of protein descriptions for cluster image plotting. Default setting will plot all reported proteins.
#' @param Protein_desc_of_exclusion Specify a list of protein descriptions to be excluded from cluster image plotting.
#' @param plot_unique_component Set as \code{"TRUE"} to plot only the unique components in the cluster image plotting.
#' @param Component_plot_coloure set as "mono" to use a pre-defined color scale to plot component images. Set as "as.cluster" to use the previously assigned mono color in the additive cluster binning process.
#' @param cluster_color_scale Set as "blackwhite" to use only black and white color in the cluster image plotting. using "blackwhite" in cluster_color_scale will overwrite the components' color setting.
#' @param export_Header_table Set as \code{"TRUE"} to plot the header in the cluster image plotting. Header table includes the basic information of cluster and components.
#' @param export_footer_table Set as \code{"TRUE"} to plot the footer in the cluster image plotting. Footer shows the protein coverage in the Proteomics mode.
#' @param attach_summary_cluster Set as \code{"TRUE"} to attach an enlarged cluster image to the bottom of the cluster image.
#' @param remove_cluster_from_grid Set as \code{"TRUE"} to remove the cluster image from the cluster image grid. it is recommended to set this same as the attach_summary_cluster.
#' @param plot_cluster_image_overwrite Set as true to generate the cluster images regardless the existance of previously file(s)
#' @param cluster_rds_path set as NULL if there is not preprocessed.rds available for a single file, script will load the raw data file which may reduce the signal intensities. For multiple samples, scripts will try to load the RDS file from each "ID" folder and merge the mz features via instrument resolution setting and output a combined RDS file to the project folder. For multiple files cluster images rendering user should set the attach_summary_cluster as False, and set remove_cluster_from_grid as true.
#'
#'
#' @return None
#'
#' @examples
#' imaging_identification(threshold=0.05, ppm=5,Digestion_site="[G]",
#' missedCleavages=0:1,Fastadatabase="murine_matrisome.fasta",
#' adducts=c("M+H","M+NH4","M+Na"),IMS_analysis=TRUE,
#' Protein_feature_summary=TRUE,plot_cluster_image=TRUE,
#' Peptide_feature_summary=TRUE,plot_ion_image=FALSE,
#' parallel=3,spectra_segments_per_file=5,Segmentation="spatialKMeans"
#' )
#'
#' @export
#'
imaging_identification<-function(
#==============Choose the imzml raw data file(s) to process make sure the fasta file in the same folder
datafile,
projectfolder=NULL,
threshold=0.001,
ppm=5,
mode=c("Proteomics","Metabolomics"),
Digestion_site="trypsin",
missedCleavages=0:1,
Fastadatabase="uniprot-bovin.fasta",
adducts=c("M+H"),
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=NULL,varmod_position=NULL),
Substitute_AA=NULL,
Decoy_search=TRUE,
Decoy_adducts=c("M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe","M+Rn"),
Decoy_mode = "isotope",
mzrange=c(700,4000),
Database_stats=F,
adjust_score = FALSE,
IMS_analysis=TRUE,
PMFsearch=IMS_analysis,
Load_candidatelist=IMS_analysis || plot_cluster_image_grid,
Bypass_generate_spectrum=FALSE,
peptide_ID_filter=2,
Protein_feature_summary=TRUE,
Peptide_feature_summary=TRUE,
plot_ion_image=FALSE,
parallel=detectCores(),
spectra_segments_per_file=4,
Segmentation=c("spatialKMeans","spatialShrunkenCentroids","Virtual_segmentation","none","def_file"),
Segmentation_def="Segmentation_def.csv",
Segmentation_ncomp="auto-detect",
Segmentation_variance_coverage=0.8,
preprocess=list(force_preprocess=FALSE,use_preprocessRDS=TRUE,smoothSignal=list(method="disable"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=ppm/2, units="ppm"),
peakFilter=list(freq.min=0.05),
normalize=list(method=c("rms","tic","reference")[1],mz=1)),
Smooth_range=1,
Virtual_segmentation_rankfile=NULL,
Rotate_IMG=NULL,
Region_feature_summary=FALSE,
Spectrum_validate=TRUE,
output_candidatelist=TRUE,
use_previous_candidates=FALSE,
score_method="SQRTP",
plot_cluster_image_grid=FALSE,
deconv_peaklist="New",
plot_cluster_image_maxretry=2,
plot_cluster_image_overwrite=F,
smooth.image="gaussian",
componentID_colname="Peptide",
ClusterID_colname="Protein",
Protein_desc_of_interest=".",
Protein_desc_of_exclusion=NULL,
plot_unique_component=TRUE,
FDR_cutoff=0.05,
use_top_rank=NULL,
plot_matching_score=F,
Component_plot_coloure="mono",
cluster_color_scale="blackwhite",
plot_layout="line",
export_Header_table=T,
export_footer_table=T,
attach_summary_cluster=T,
remove_cluster_from_grid=attach_summary_cluster,
pixel_size_um=50,
img_brightness=100,
Thread=4,
cluster_rds_path=NULL,
remove_score_outlier=F,
Plot_score_IQR_cutoff=0.75,
Plot_score_abs_cutoff=-0.1,
mzAlign_runs="TopNfeature_mean",
...
){
suppressMessages(suppressWarnings(library("pacman")))
suppressMessages(suppressWarnings(p_load(stringr,BiocParallel,data.table,Cardinal,parallel)))
if (missing(datafile)) stop("Missing data file, Choose single or multiple imzml file(s) for analysis")
# retrieve/parse the working dir info, and convert the filenames
if (is.null(projectfolder)){
workdir<-base::dirname(datafile[1])
}else{ workdir<-projectfolder }
datafile <- basename(datafile)
datafile <- gsub(".imzML$", "", datafile)
datafile_imzML <- paste0(datafile,".imzML")
setwd(paste0(workdir[1],"/"))
# Set the parallel processing parameter, multicore-fork method has been temporarily disabled due to the reduced performance in docker enviornment
if (is.null(Thread)){
parallel=try(future::availableCores()/2)
if (parallel<1 | is.null(parallel)){parallel=1}
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}else{
parallel=Thread
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}
message(paste(try(future::availableCores()), "Cores detected,",parallel, "threads will be used for computing"))
message(paste(length(datafile), "files were selected and will be used for Searching"))
message(paste(Fastadatabase, "was selected as database.", "Candidates will be generated through",mode[1] ,"mode" ))
# prepare the proteome database, use_previous_candidates=T will override the other argument and load the candidate list directly
if(Load_candidatelist){
Protein_feature_list<-Protein_feature_list_fun(workdir=workdir,
database=Fastadatabase,
Digestion_site=Digestion_site,
missedCleavages=missedCleavages,
adducts=adducts,
BPPARAM = BPPARAM,
Decoy_adducts=Decoy_adducts,
Decoy_search=Decoy_search,
Decoy_mode = Decoy_mode,
output_candidatelist=output_candidatelist,
use_previous_candidates=use_previous_candidates,
Substitute_AA=Substitute_AA,
Modifications=Modifications,
mzrange=mzrange,
Protein_desc_of_exclusion=Protein_desc_of_exclusion,
Database_stats=Database_stats)
}
#
if(IMS_analysis){
message(paste(Fastadatabase,"was selected as database","\nSpectrum intensity threshold:",percent(threshold),"\nmz tolerance:",ppm,"ppm","Segmentation method:",Segmentation[1],
"\nManual segmentation def file:",ifelse(is.null(Virtual_segmentation_rankfile),"None",Virtual_segmentation_rankfile),"\nBypass spectrum generation:",Bypass_generate_spectrum))
#select candidate list for IMS annotation
Peptide_Summary_searchlist<-unique(Protein_feature_list)
Peptide_Summary_file<-IMS_data_process(datafile=datafile, workdir=workdir,
Peptide_Summary_searchlist=Peptide_Summary_searchlist,
segmentation_num=spectra_segments_per_file,
threshold=threshold,rotate = Rotate_IMG,
ppm=ppm,mzrange=mzrange,
Segmentation=Segmentation,
Segmentation_ncomp=Segmentation_ncomp,
PMFsearch = PMFsearch,
Virtual_segmentation_rankfile = Virtual_segmentation_rankfile,
BPPARAM = BPPARAM,
Bypass_generate_spectrum=Bypass_generate_spectrum,
score_method = score_method,
Decoy_mode=Decoy_mode,
Decoy_search=Decoy_search,
adjust_score=adjust_score,
peptide_ID_filter=peptide_ID_filter,
Protein_desc_of_interest=Protein_desc_of_interest,
plot_matching_score_t=plot_matching_score,
FDR_cutoff= FDR_cutoff,
Segmentation_def=Segmentation_def,
Segmentation_variance_coverage=Segmentation_variance_coverage,
preprocess=preprocess)
}
#Summarize the protein result across the datafiles and store these summarized files into the summary folder
if(Protein_feature_summary){
message("Protein feature summary...")
Peptide_Summary_file<-NULL
Protein_peptide_Summary_file<-NULL
protein_feature_all<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
protein_feature<-NULL
for (protein_feature_file in dir()[stringr::str_detect(dir(),"Protein_segment_PMF_RESULT_")]){
protein_feature<-fread(protein_feature_file)
if(nrow(protein_feature)!=0){
protein_feature$Source<-datafilename
region_code<-str_replace(protein_feature_file,"Protein_segment_PMF_RESULT_","")
region_code<-str_replace(region_code,".csv","")
protein_feature$Region<-region_code
protein_feature_all<-rbind(protein_feature_all,protein_feature)
}
}
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-datafilename
if(nrow(Peptide_Summary_file)!=0){
Protein_peptide_Summary_file<-rbind(Protein_peptide_Summary_file,Peptide_Summary_file)
}
}
message("Protein feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(protein_feature_all,paste(workdir,"/Summary folder/Protein_Summary.csv",sep=""),row.names = F)
write.csv(Protein_peptide_Summary_file,paste(workdir,"/Summary folder/Protein_peptide_Summary.csv",sep=""),row.names = F)
}
#Summarize the protein and peptide result across the datafiles and store these summarized files into the summary folder
if(Peptide_feature_summary){
message("Peptide feature summary...")
Peptide_Summary_file<-NULL
Peptide_Summary_file_a<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-gsub(".imzML", "", datafile[i])
if(nrow(Peptide_Summary_file)!=0){
Peptide_Summary_file_a<-rbind(Peptide_Summary_file_a,Peptide_Summary_file)
}
}
Peptide_Summary_file_a<-unique(Peptide_Summary_file_a)
message("Peptide feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(Peptide_Summary_file_a,paste(workdir,"/Summary folder/Peptide_Summary.csv",sep=""),row.names = F)
#Peptide_feature_summary_all_files_new(datafile,workdir,threshold = threshold)
}
#Summarize the mz feature list
if(Region_feature_summary){
message("Region feature summary...")
Spectrum_summary<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(currentdir)
name <-gsub(base::dirname(datafile[i]),"",gsub(".imzML", "", datafile[i]))
message(paste("Region_feature_summary",gsub(".imzML", "", datafile[i])))
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
match_pattern <- "Spectrum.csv"
spectrum_file_table_sum<-NULL
for (spectrum_file in dir(recursive = T)[str_detect(dir(recursive = T), match_pattern)]){
spectrum_file_table=fread(spectrum_file)
if (nrow(spectrum_file_table)>=1){
spectrum_file_table$Region=gsub("/Spectrum.csv","",spectrum_file)
spectrum_file_table$Source<-gsub(".imzML", "", datafile[i])
spectrum_file_table_sum[[spectrum_file]] <- spectrum_file_table
}
}
spectrum_file_table_sum <- do.call(rbind,spectrum_file_table_sum)
Spectrum_summary[[datafile[i]]]=spectrum_file_table_sum
}
Spectrum_summary <- do.call(rbind,Spectrum_summary)
write.csv(Spectrum_summary,file = paste(workdir,"/Summary folder/Region_feature_summary.csv",sep=""),row.names = F)
}
#Protein cluster image rendering
if(plot_cluster_image_grid){
message("cluster image rendering...")
setwd(workdir[1])
# read protein-peptide features result
Protein_feature_list=read.csv(file=paste(workdir[1],"/Summary folder/Protein_peptide_Summary.csv",sep=""),stringsAsFactors = F)
# remove peptide score outlier from result
if (remove_score_outlier){
Protein_feature_list <- remove_pep_score_outlier(Protein_feature_list,abs_cutoff=Plot_score_abs_cutoff,IQR_LB = Plot_score_IQR_cutoff)
}
# extract the protein entries of interest
if (sum(Protein_desc_of_interest!=".")>=1){
Protein_feature_list_interest<-NULL
num_of_interest<-numeric(0)
for (interest_desc in Protein_desc_of_interest){
idx_iterest_desc<-str_detect(Protein_feature_list$desc,regex(interest_desc,ignore_case = T))
if(nrow(Protein_feature_list[idx_iterest_desc,])!=0){
Protein_feature_list_interest<-rbind(Protein_feature_list_interest,Protein_feature_list[idx_iterest_desc,])
}
num_of_interest[interest_desc]<-length(unique(Protein_feature_list[idx_iterest_desc,"Protein"]))
}
Protein_feature_list=Protein_feature_list_interest
message(paste(num_of_interest,"Protein(s) found with annotations of interest:",Protein_desc_of_interest,collapse = "\n"))
}
Protein_feature_list=as.data.frame(Protein_feature_list)
# generate combined IMS data for multiple files or use a link to load the pre-processed IMS data
if (!is.null(cluster_rds_path)){
cluster_rds_path
imdata=readRDS(paste0(workdir[1],"/",cluster_rds_path))
message("cluster imdata loaded.")
}else{
cluster_rds_path<-Load_IMS_decov_combine(datafile=datafile,workdir=workdir,import_ppm=ppm,SPECTRUM_batch="overall",mzAlign_runs=mzAlign_runs,
ppm=ppm,threshold=0,rotate=Rotate_IMG,mzrange=mzrange,
deconv_peaklist=deconv_peaklist,preprocessRDS_rotated=T,target_mzlist=sort(unique(as.numeric(Protein_feature_list$mz)),decreasing = F))
imdata=readRDS(paste0(workdir[1],"/",basename(cluster_rds_path)))
message("cluster imdata generated and loaded.")
}
# test combined imdata
if (class(imdata)[1]=="matrix"){
do.call(Cardinal::cbind,imdata)->imdata
saveRDS(imdata,paste0(workdir[1],"/combinedimdata.rds"),compress = T)
}
# Setup output folder and queue the R calls for cluster image randering
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
if (!(plot_unique_component)){
setwd(outputfolder)
Protein_feature_list_trimmed<-Protein_feature_list
}
if (plot_unique_component){
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/unique/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
setwd(outputfolder)
Protein_feature_list_unique=Protein_feature_list %>% group_by(mz) %>% dplyr::summarise(num=length(unique(Protein)))
Protein_feature_list_unique_mz<-Protein_feature_list_unique$mz[Protein_feature_list_unique$num==1]
Protein_feature_list_trimmed<-Protein_feature_list[Protein_feature_list$mz %in% Protein_feature_list_unique_mz, ]
write.csv(Protein_feature_list_trimmed,paste(workdir,"/Summary folder/Protein_feature_list_trimmed.csv",sep=""),row.names = F)
}
#list_of_protein_sequence[!(1:length(list_of_protein_sequence) %in% as.numeric(Protein_feature_list_trimmed$Protein))]<-""
#unname(as.character(list_of_protein_sequence))->str_vec
#imdata <- imdata %>% peakBin(ref=sort(unique((Protein_feature_list_trimmed$mz_align+Protein_feature_list_trimmed$mz)/2)), tolerance=ppm, units="ppm") %>% process(BPPARAM=SerialParam())
save(list=c("Protein_feature_list_trimmed",
"imdata",
"ClusterID_colname",
"componentID_colname",
"plot_layout",
"export_Header_table",
"export_footer_table",
"attach_summary_cluster",
"remove_cluster_from_grid",
"smooth.image",
"Component_plot_coloure",
"cluster_color_scale",
"list_of_protein_sequence",
"outputfolder",
"peptide_ID_filter",
"ppm",
"img_brightness",
"pixel_size_um"
),
file=paste0(workdir,"/cluster_img_grid.RData"))
for (clusterID in unique(Protein_feature_list_trimmed$Protein)){
cluster_desc<-unique(Protein_feature_list_trimmed$desc[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID])
cluster_desc<-gsub(stringr::str_extract(cluster_desc,"OS=.{1,}"),"",cluster_desc)
n_component<-nrow(unique(Protein_feature_list_trimmed[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID,c(ClusterID_colname,componentID_colname,"moleculeNames","adduct","Modification")]))
if (n_component>=peptide_ID_filter){
if ('&'(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png")),!plot_cluster_image_overwrite)){
message("Cluster image rendering Skipped file exists: No.",clusterID," ",cluster_desc)
next
}else {
fileConn<-file(paste0(workdir,"/cluster_img_scource.R"),)
writeLines(c("suppressMessages(suppressWarnings(require(HiTMaP)))",
paste0("clusterID=",clusterID),
paste0("suppressMessages(suppressWarnings(load(file =\"", workdir,"/cluster_img_grid.RData\")))"),
"suppressMessages(suppressWarnings(cluster_image_grid(clusterID = clusterID,
imdata=imdata,
SMPLIST=Protein_feature_list_trimmed,
ppm=ppm,
ClusterID_colname=ClusterID_colname,
componentID_colname=componentID_colname,
plot_layout=plot_layout,
export_Header_table=export_Header_table,
export_footer_table=export_footer_table,
attach_summary_cluster=attach_summary_cluster,
remove_cluster_from_grid=remove_cluster_from_grid,
plot_style=\"fleximaging\",
smooth.image=smooth.image,
Component_plot_coloure=Component_plot_coloure,
cluster_color_scale=cluster_color_scale,
list_of_protein_sequence=list_of_protein_sequence,
workdir=outputfolder,
pixel_size_um=pixel_size_um,
img_brightness=img_brightness,
Component_plot_threshold=peptide_ID_filter)))"),
fileConn)
close(fileConn)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
}else{
retrytime=1
repeat{
message("Cluster image rendering failed and retry ",retrytime,": No.",clusterID," ",cluster_desc)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if (file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
break
}else if(retrytime>=plot_cluster_image_maxretry){
message("Cluster image rendering reaches maximum Retry Attempts: No.",clusterID," ",cluster_desc)
break
}
retrytime=1+retrytime
}
}
}
}
}
}
message("Workflow done.")
}
IMS_data_process<-function(datafile,
workdir=NULL,
Peptide_Summary_searchlist,
segmentation_num=5,threshold=0.001,TopNFeatures=1250,
ppm,import_ppm=5,
mzrange="auto-detect",
Segmentation=c("spatialKMeans","spatialShrunkenCentroids","Virtual_segmentation","none","def_file"),
Segmentation_def="segmentation_def.csv",
Segmentation_ncomp="auto-detect",
Segmentation_variance_coverage=0.8,
Bypass_Segmentation=F,
Smooth_range=1,
colorstyle="Set1",
Virtual_segmentation_rankfile=NULL,
PMFsearch=TRUE,
rotate=NULL,
matching_validation=T,
BPPARAM=bpparam(),
Bypass_generate_spectrum=F,
scoring=T,
score_method="SQRTP",
Rank=3,
Decoy_mode="",
Decoy_search=T,
adjust_score=T,
plot_matching_score_t=F,
Protein_feature_list,
peptide_ID_filter=2,
Protein_desc_of_interest=".",
FDR_cutoff=0.1,
use_top_rank=NULL,
preprocess=list(force_preprocess=FALSE,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)),
...){
suppressMessages(suppressWarnings(require(data.table)))
suppressMessages(suppressWarnings(require(Cardinal)))
suppressMessages(suppressWarnings(require(RColorBrewer)))
suppressMessages(suppressWarnings(require(stringr)))
getPalette = colorRampPalette(brewer.pal_n(9, colorstyle))
setCardinalBPPARAM(BPPARAM)
# resolve the filename, project folder and rotation info
datafile <- paste0(workdir,"/",datafile)
workdir <- dirname(datafile)
datafile <- basename(datafile)
rotate=Parse_rotation(datafile,rotate)
datafile_imzML<-datafile
# perform the IMS analysis
for (z in 1:length(datafile)){
#file related variables
name <-basename(datafile[z])
name <-gsub(".imzML$","",name)
name <-gsub("/$","",name)
folder<-base::dirname(datafile[z])
if (!str_detect(datafile[z],".imzML$")){
datafile_imzML[z]<-paste0(datafile[z],".imzML")
}
#perform the IMS pre-processing and image segmentation
setwd(workdir[z])
segmentation_res <- Preprocessing_segmentation(datafile=datafile[z],
workdir=workdir[z],
segmentation_num=segmentation_num,
ppm=ppm,import_ppm=import_ppm,Bypass_Segmentation=Bypass_Segmentation,
mzrange=mzrange,
Segmentation=Segmentation,
Segmentation_def=Segmentation_def,
Segmentation_ncomp=Segmentation_ncomp,
Segmentation_variance_coverage=Segmentation_variance_coverage,
Smooth_range=Smooth_range,
colorstyle=colorstyle,
Virtual_segmentation_rankfile=Virtual_segmentation_rankfile,
rotate=rotate,
BPPARAM=BPPARAM,
preprocess=preprocess)
segmentation_label=segmentation_res$segmentation_label
imdata=segmentation_res$imdata
#imdata_ed=segmentation_res$imdata_ed
#imdata_org=segmentation_res$imdata_org
rm(segmentation_res)
if(PMFsearch){
if (missing(Protein_feature_list)){
Protein_feature_list=get("Protein_feature_list", envir = .GlobalEnv)
message("Got Protein_feature_list from global environment.")
}
Peptide_Summary_searchlist$Protein<-NULL
Peptide_Summary_searchlist$pro_end<-NULL
Peptide_Summary_searchlist$start<-NULL
Peptide_Summary_searchlist$end<-NULL
Peptide_Summary_searchlist<-unique(Peptide_Summary_searchlist)
Peptide_Summary_file<-Peptide_Summary_searchlist
Peptide_Summary_file$Intensity<-rep(0,nrow(Peptide_Summary_file))
setwd(workdir[z])
#remove previously genrated IMS annotation result
if (dir.exists(paste0(gsub(".imzML$","",datafile[z]) ," ID"))==FALSE){dir.create(paste0(gsub(".imzML$","",datafile[z]) ," ID"))
}else{
all_files<-dir(paste0(gsub(".imzML$","",datafile[z]) ," ID"),recursive = F)
delete_files<-all_files[str_detect(all_files,"Protein_segment_PMF_RESULT_|Peptide_|PMF spectrum match.png$")]
delete_dir<-list.dirs(path = paste0(gsub(".imzML$","",datafile[z]) ," ID"), full.names = TRUE, recursive = F)
try(suppressWarnings(file.remove(c(paste0(gsub(".imzML$","",datafile[z]) ," ID/",delete_files)))))
unlink(delete_dir, recursive = T)
}
#Start annotation for each found region
setwd(paste0(gsub(".imzML$","",datafile[z]) ," ID"))
Peptide_Summary_file_regions<-data.frame()
message(paste("PMFsearch",name))
message(paste( "region",names(segmentation_label),"Found.",sep=" ",collapse = "\n"))
for (SPECTRUM_batch in names(segmentation_label)){
message(paste("IMS_analysis",name,"region",SPECTRUM_batch))
if (dir.exists(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/"))==FALSE){dir.create(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/"))}
# if (!is.null(preprocess)){
# if ('|'(imdata@metadata[["ibd binary type"]]!="processed",preprocess$force_preprocess)){
# message(paste("Using preprocessed .rda data:",datafile[z]))
# imdata<-imdata_ed
# } else if (imdata@metadata[["ibd binary type"]]=="processed"){
# message(paste("Using preprocessed .imzml data:",datafile[z]))
# imdata<-imdata_org
# }
# }
imdata_sb <- imdata[,unlist(segmentation_label[[SPECTRUM_batch]])]
imdata_ed <- imdata_sb
if(is.null(preprocess$peakAlign$level)) preprocess$peakAlign$level<-"local"
if (preprocess$peakAlign$level=="local"){
if (preprocess$peakAlign$tolerance==0 ) {
message("preprocess$peakAlign$tolerance set as zero, step bypassed")
}else if ('&'(!is.null(preprocess$peakAlign$tolerance),!is.null(preprocess$peakAlign$units))){
message("preprocess$peakAlign$tolerance set as ", preprocess$peakAlign$tolerance)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=preprocess$peakAlign$tolerance, units=preprocess$peakAlign$units)
}else {
message("preprocess$peakAlign$tolerance missing, use default tolerance in ppm ", ppm/2)
imdata_ed<- imdata_ed %>% peakAlign(tolerance=ppm/2, units="ppm")
}
imdata_ed <- imdata_ed %>% process()
}
imdata_sb <- imdata_ed
#generate spectrum for each found region
spectrum_file_table<- summarizeFeatures(imdata_sb, FUN = "mean")
spectrum_file_table<-data.frame(mz=spectrum_file_table@featureData@mz,mean=spectrum_file_table@featureData@listData[["mean"]])
peaklist<-spectrum_file_table
colnames(peaklist)<-c("m.z","intensities")
savename=paste(name,SPECTRUM_batch)
peaklist<-peaklist[peaklist$intensities>0,]
write.csv(peaklist,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Spectrum.csv"),row.names = F)
#generate filtered processed peaklist to next PMF step
deconv_peaklist<-HiTMaP:::isopattern_ppm_filter_peaklist(peaklist,ppm=ppm,threshold=0)
deconv_peaklist_thres_id<-deconv_peaklist$intensities>=max(deconv_peaklist$intensities)*threshold
deconv_peaklist_topN_id<-rank(deconv_peaklist$intensities,)>(max(rank(deconv_peaklist$intensities,))-TopNFeatures)
deconv_peaklist_PMF_id<-`|`(deconv_peaklist_thres_id,deconv_peaklist_topN_id)
peaklist_pmf<-deconv_peaklist[deconv_peaklist_PMF_id,]
message(paste(nrow(peaklist_pmf),"mz features subjected to 1st PMF search") )
#Do first round of peptide search to get putative result
mz_feature_list<-Do_PMF_search(peaklist_pmf,Peptide_Summary_searchlist,BPPARAM=BPPARAM,ppm = ppm)
mz_feature_list<-unique(mz_feature_list)
message("Summarizing peptide information...")
Peptide_Summary_searchlist<-as.data.table(Peptide_Summary_searchlist)
mz_feature_list<-as.data.table(mz_feature_list)
Peptide_Summary_searchlist$Intensity<-NULL
mz_feature_list$mz<-as.character(mz_feature_list$mz)
Peptide_Summary_searchlist$mz<-as.character(Peptide_Summary_searchlist$mz)
Peptide_Summary_searchlist<-merge(Peptide_Summary_searchlist,mz_feature_list,by.x="mz",by.y="mz",all.x=T,sort=F)
Peptide_Summary_searchlist$Intensity[is.na(Peptide_Summary_searchlist$Intensity)]<-0
Peptide_plot_list<-Peptide_Summary_searchlist[Peptide_Summary_searchlist$Intensity>0,]
Peptide_plot_list$formula<-as.character(Peptide_plot_list$formula)
if(is.null(Peptide_plot_list$moleculeNames)){Peptide_plot_list$moleculeNames=Peptide_plot_list$Peptide}
Peptide_plot_list$Region=SPECTRUM_batch
Peptide_plot_list=Peptide_plot_list[(!is.na(Peptide_plot_list$Intensity)),]
message(paste("1st run returns",nrow(Peptide_plot_list), "peptide candidates"))
write.csv(Peptide_plot_list,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_1st_ID.csv"),row.names = F)
#perform peptide-protein scoring and FDR cut off
if (nrow(Peptide_plot_list)==0){
next
}
if (scoring){
suppressMessages(suppressWarnings(require(enviPat)))
suppressMessages(suppressWarnings(require(ggplot2)))
data(isotopes)
unique_formula<-unique(Peptide_plot_list$formula)
Peptide_plot_list_Score=(bplapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=isotopes,score_method=score_method,charge = 1,ppm=ppm,BPPARAM = BPPARAM))
#Peptide_plot_list_Score=(lapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=isotopes,score_method=score_method,charge = 1,ppm=ppm))
Peptide_plot_list_Score_m=as.data.frame(do.call(rbind, Peptide_plot_list_Score))
names(Peptide_plot_list_Score_m)<-c("Score", "delta_ppm","Intensity")
formula_score<-data.frame(formula=unique_formula,Score=Peptide_plot_list_Score_m$Score,Delta_ppm=Peptide_plot_list_Score_m$delta_ppm,Intensity=Peptide_plot_list_Score_m$Intensity)
Peptide_plot_list$Score<-NULL
Peptide_plot_list$Delta_ppm<-NULL
Peptide_plot_list$Intensity<-NULL
Peptide_plot_list<-merge(Peptide_plot_list,formula_score,by="formula")
#generate decoy candidate list if running in "isotope" decoy mode
if (Decoy_search && ("isotope" %in% Decoy_mode)){
decoy_isotopes=isotopes
decoy_isotopes[decoy_isotopes$isotope=="13C",]=data.frame(element="C",isotope="11C",mass=10.99664516,aboudance=0.0107,ratioC=0,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="15N",]=data.frame(element="N",isotope="13N",mass=13.00603905,aboudance=0.00364000,ratioC=4,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="2H",]=data.frame(element="H",isotope="0H",mass=0.001548286,aboudance=0.00011500,ratioC=6,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="17O",]=data.frame(element="O",isotope="15O",mass=14.99069774,aboudance=0.00038000,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="18O",]=data.frame(element="O",isotope="14O",mass=13.99066884,aboudance=0.00205000,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="33S",]=data.frame(element="S",isotope="31S",mass=30.97268292,aboudance=0.0075,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="34S",]=data.frame(element="S",isotope="30S",mass=29.9762745,aboudance=0.0425,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="35S",]=data.frame(element="S",isotope="29S",mass=28.94414146,aboudance=0,ratioC=3,stringsAsFactors = F)
decoy_isotopes[decoy_isotopes$isotope=="36S",]=data.frame(element="S",isotope="28S",mass=27.97706058,aboudance=0.0001,ratioC=3,stringsAsFactors = F)
Peptide_plot_list_decoy<-Peptide_plot_list[Peptide_plot_list$isdecoy==0,]
Peptide_plot_list_decoy$isdecoy=1
unique_formula<-unique(Peptide_plot_list_decoy$formula)
Peptide_plot_list_Score=(bplapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=decoy_isotopes,score_method=score_method,charge = 1,ppm=ppm,BPPARAM = BPPARAM))
#Peptide_plot_list_Score=(lapply(unique_formula,SCORE_PMF,peaklist=peaklist,isotopes=decoy_isotopes,score_method=score_method,charge = 1,ppm=ppm))
Peptide_plot_list_Score_m=as.data.frame(do.call(rbind, Peptide_plot_list_Score))
names(Peptide_plot_list_Score_m)<-c("Score", "delta_ppm","Intensity")
formula_score<-data.frame(formula=unique_formula,Score=Peptide_plot_list_Score_m$Score,Delta_ppm=Peptide_plot_list_Score_m$delta_ppm,Intensity=Peptide_plot_list_Score_m$Intensity)
Peptide_plot_list_decoy$Score<-NULL
Peptide_plot_list_decoy$Delta_ppm<-NULL
Peptide_plot_list_decoy$Intensity<-NULL
Peptide_plot_list_decoy<-merge(Peptide_plot_list_decoy,formula_score,by="formula")
Peptide_plot_list<-rbind(Peptide_plot_list,Peptide_plot_list_decoy)
Protein_feature_list_decoy<-Protein_feature_list[Protein_feature_list$isdecoy==0,]
Protein_feature_list_decoy$isdecoy=1
Protein_feature_list=rbind(Protein_feature_list[Protein_feature_list$isdecoy==0,],Protein_feature_list_decoy)
}
Peptide_plot_list$mz<-as.numeric(as.character(Peptide_plot_list$mz))
Peptide_plot_list_rank=rank_mz_feature(Peptide_plot_list,mz_feature=deconv_peaklist,BPPARAM = BPPARAM)
Peptide_plot_list_rank<-unique(Peptide_plot_list_rank)
write.csv(Peptide_plot_list_rank,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_1st_ID_score_rank_",score_method,".csv"),row.names = F)
Peptide_plot_list_rank<-read.csv(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_1st_ID_score_rank_",score_method,".csv"),stringsAsFactors = F)
#Peptide score filtering (default=False)
if (adjust_score==F){
Score_cutoff= FDR_cutoff_plot(Peptide_plot_list_rank,FDR_cutoff=FDR_cutoff,plot_fdr=T,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch),adjust_score = adjust_score)
Peptide_plot_list_2nd=Peptide_plot_list_rank[((Peptide_plot_list_rank$Score>=Score_cutoff)&(!is.na(Peptide_plot_list_rank$Intensity))),]
}else{
Score_cutoff= FDR_cutoff_plot(Peptide_plot_list_rank,FDR_cutoff=FDR_cutoff,plot_fdr=T,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch),adjust_score = adjust_score)
Peptide_plot_list_2nd=Score_cutoff[[2]]
Score_cutoff=Score_cutoff[[1]]
Peptide_plot_list_2nd=Peptide_plot_list_2nd[((Peptide_plot_list_2nd$Score>=Score_cutoff)&(!is.na(Peptide_plot_list_2nd$Intensity))),]
}
write.csv(Peptide_plot_list_2nd,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Peptide_2nd_ID_score_rank",score_method,"_Rank_above_",Rank,".csv"),row.names = F)
#Plot peptide matching spectrum and score
if (plot_matching_score_t && nrow(Peptide_plot_list_2nd)!=0){
try(plot_matching_score(Peptide_plot_list_2nd,peaklist,charge=1,ppm,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/ppm")))
}
Peptide_plot_list_rank$mz=as.numeric(as.character(Peptide_plot_list_rank$mz))
Peptide_plot_list_2nd$mz=as.numeric(as.character(Peptide_plot_list_2nd$mz))
#Protein scoring and FDR cut-off
Protein_feature_result<-protein_scoring(Protein_feature_list,Peptide_plot_list_rank,BPPARAM = BPPARAM,scoretype="mean",peptide_ID_filter=peptide_ID_filter,use_top_rank=use_top_rank)
Protein_feature_list_rank<-Protein_feature_result[[2]]
Protein_feature_list_rank_filtered<-Protein_feature_result[[3]]
Protein_feature_list_rank_filtered_grouped<-Protein_feature_result[[4]]
Protein_feature_result<-Protein_feature_result[[1]]
if (nrow(Protein_feature_result)>=2){
Score_cutoff_protein= FDR_cutoff_plot_protein(Protein_feature_result,FDR_cutoff=FDR_cutoff,plot_fdr=T,outputdir=paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch),adjust_score = F)
}else{
Score_cutoff_protein=Protein_feature_result$Proscore
if (length(Score_cutoff_protein)==0) Score_cutoff_protein=0}
Protein_feature_result_cutoff=Protein_feature_result[((Protein_feature_result$Proscore>=Score_cutoff_protein)&(!is.na(Protein_feature_result$Intensity))&(Protein_feature_result$isdecoy==0)),]
Protein_feature_list_rank=Protein_feature_list_rank[Protein_feature_list_rank$Protein %in% Protein_feature_result_cutoff$Protein,]
Protein_feature_list_rank$Score=round(Protein_feature_list_rank$Score,digits = 7)
Protein_feature_list_rank$mz=round(Protein_feature_list_rank$mz,digits = 4)
Protein_feature_list_rank<-as.data.frame(Protein_feature_list_rank)
if(is.null(Protein_feature_list_rank$desc)){
if (exists("Index_of_protein_sequence", envir = .GlobalEnv)){
Index_of_protein_sequence<-get("Index_of_protein_sequence", envir = .GlobalEnv)
Protein_feature_list_rank=merge(Protein_feature_list_rank,Index_of_protein_sequence[,c("recno","desc")],by.x="Protein",by.y="recno",all.x=T)
}else{
Protein_feature_list_rank$desc<-Protein_feature_list_rank$Protein
}
}
Protein_feature_list_rank_cutoff<-Protein_feature_list_rank
Protein_feature_list_rank_cutoff<-Protein_feature_list_rank_cutoff[Protein_feature_list_rank_cutoff$isdecoy==0,]
write.csv(Protein_feature_result,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Protein_ID_score_rank_",score_method,".csv"),row.names = F)
write.csv(Protein_feature_list_rank_filtered,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Protein_ID_score_rank_filtered_",score_method,".csv"),row.names = F)
write.csv(Protein_feature_list_rank_filtered_grouped,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Protein_ID_score_rank_filtered_grouped_",score_method,".csv"),row.names = F)
write.csv(Protein_feature_list_rank_cutoff,paste0(workdir[z],"/",datafile[z] ," ID/","Peptide_segment_PMF_RESULT_",SPECTRUM_batch,".csv"),row.names = F)
#Plot Protein matching spectrum and score
png(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/unique_peptide_ranking_vs_mz_feature",".png"),width = 960,height = 480)
sp<-ggplot2::ggplot(Peptide_plot_list_rank, aes(x=mz,fill=as.factor(Rank))) + geom_bar(stat = "bin",bins = 100) +
labs(title="Matched peptide ranking vs. mz feature",x="mz", y = "Matched peptide ranking")+
theme_classic() + theme(legend.title = element_blank()) + scale_fill_manual(values =grDevices::colorRampPalette(brewer.pal(8, "RdYlBu"))(max(Peptide_plot_list_rank$Rank)))
try(print(sp))
dev.off()
message(paste("Plotting peptide matching number vs mz feature"))
Peptide_plot_list_2nd=Protein_feature_list_rank_cutoff
png(paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/unique_peptide_ranking_vs_mz_feature_2nd",".png"),width = 960,height = 480)
sp<-ggplot2::ggplot(Peptide_plot_list_2nd, aes(x=mz,fill=as.factor(floor(Rank/max(Rank)*8)))) + geom_bar(stat = "bin",bins = 100) +
labs(title="Matched peptide ranking 2nd vs. mz feature",x="mz", y = "Matched peptide ranking")+
theme_classic() + theme(legend.title = element_blank()) + scale_fill_brewer()
try(print(sp))
dev.off()
write.csv(Protein_feature_result_cutoff,paste0(workdir[z],"/",datafile[z] ," ID/","Protein_segment_PMF_RESULT_",SPECTRUM_batch,".csv"),row.names = F)
}
#Plot overall matching spectrum and score
Plot_PMF_all(Peptide_plot_list_2nd,peaklist=peaklist,threshold=threshold,savename)
write.csv(peaklist,paste0(workdir[z],"/",datafile[z] ," ID/",SPECTRUM_batch,"/Spectrum.csv"),row.names = F)
if (nrow(Peptide_plot_list_2nd)!=0){
Peptide_plot_list_2nd$Region<-SPECTRUM_batch
}
Peptide_Summary_file_regions<-rbind(Peptide_Summary_file_regions,Peptide_plot_list_2nd)
}
if(is.null(Peptide_Summary_file_regions$Peptide)){Peptide_Summary_file_regions$Peptide=Peptide_Summary_file_regions$moleculeNames}
if(is.null(Peptide_Summary_file_regions$moleculeNames)){Peptide_Summary_file_regions$moleculeNames=Peptide_Summary_file_regions$Peptide}
#export final result for the given data
write.csv(Peptide_Summary_file_regions,"Peptide_region_file.csv",row.names = F)
}
}
setwd(workdir[1])
}
imaging_identification_target<-function(
#==============Choose the imzml raw data file(s) to process make sure the fasta file in the same folder
datafile,
projectfolder=NULL,
threshold=0.001,
ppm=5,
mode=c("Proteomics","Metabolomics"),
Digestion_site="trypsin",
missedCleavages=0:1,
Fastadatabase="uniprot-bovin.fasta",
adducts=c("M+H"),
Modifications=list(fixed=NULL,fixmod_position=NULL,variable=NULL,varmod_position=NULL),
Substitute_AA=NULL,
Decoy_search=TRUE,
Decoy_adducts=c("M+ACN+H","M+IsoProp+H","M+DMSO+H","M+Co","M+Ag","M+Cu","M+He","M+Ne","M+Ar","M+Kr","M+Xe","M+Rn"),
Decoy_mode = "isotope",
mzrange=c(700,4000),
Database_stats=F,
adjust_score = FALSE,
IMS_analysis=TRUE,
PMFsearch=IMS_analysis,
Load_candidatelist=IMS_analysis || plot_cluster_image_grid,
Bypass_generate_spectrum=FALSE,
peptide_ID_filter=2,
Protein_feature_summary=TRUE,
Peptide_feature_summary=TRUE,
plot_ion_image=FALSE,
parallel=detectCores(),
spectra_segments_per_file=4,
Segmentation=c("spatialKMeans","spatialShrunkenCentroids","Virtual_segmentation","none","def_file"),
Segmentation_def="Segmentation_def.csv",
Segmentation_ncomp="auto-detect",
Segmentation_variance_coverage=0.8,
preprocess=list(force_preprocess=FALSE,use_preprocessRDS=TRUE,smoothSignal=list(method="disable"),
reduceBaseline=list(method="locmin"),
peakPick=list(method="adaptive"),
peakAlign=list(tolerance=ppm/2, units="ppm"),
peakFilter=list(freq.min=0.05),
normalize=list(method=c("rms","tic","reference")[1],mz=1)),
Smooth_range=1,
Virtual_segmentation_rankfile=NULL,
Rotate_IMG=NULL,
Region_feature_summary=FALSE,
Spectrum_validate=TRUE,
output_candidatelist=TRUE,
use_previous_candidates=FALSE,
score_method="SQRTP",
plot_cluster_image_grid=FALSE,
deconv_peaklist="New",
plot_cluster_image_maxretry=2,
plot_cluster_image_overwrite=F,
smooth.image="gaussian",
componentID_colname="Peptide",
ClusterID_colname="Protein",
Protein_desc_of_interest=".",
Protein_desc_of_exclusion=NULL,
plot_unique_component=TRUE,
FDR_cutoff=0.05,
use_top_rank=NULL,
plot_matching_score=F,
Component_plot_coloure="mono",
cluster_color_scale="blackwhite",
plot_layout="line",
export_Header_table=T,
export_footer_table=T,
attach_summary_cluster=T,
remove_cluster_from_grid=attach_summary_cluster,
pixel_size_um=50,
img_brightness=100,
Thread=4,
cluster_rds_path=NULL,
remove_score_outlier=F,
Plot_score_IQR_cutoff=0.75,
Plot_score_abs_cutoff=-0.1,
mzAlign_runs="TopNfeature_mean",
...
){
suppressMessages(suppressWarnings(library("pacman")))
suppressMessages(suppressWarnings(p_load(stringr,BiocParallel,data.table,Cardinal,parallel)))
if (missing(datafile)) stop("Missing data file, Choose single or multiple imzml file(s) for analysis")
# retrieve/parse the working dir info, and convert the filenames
if (is.null(projectfolder)){
workdir<-base::dirname(datafile[1])
}else{ workdir<-projectfolder }
datafile <- basename(datafile)
datafile <- gsub(".imzML$", "", datafile)
datafile_imzML <- paste0(datafile,".imzML")
setwd(paste0(workdir[1],"/"))
# Set the parallel processing parameter, multicore-fork method has been temporarily disabled due to the reduced performance in docker enviornment
if (is.null(Thread)){
parallel=try(detectCores()/2)
if (parallel<1 | is.null(parallel)){parallel=1}
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}else{
parallel=Thread
BPPARAM=HiTMaP:::Parallel.OS(parallel)
setCardinalBPPARAM(BPPARAM = BPPARAM)
}
message(paste(try(detectCores()), "Cores detected,",parallel, "threads will be used for computing"))
message(paste(length(datafile), "files were selected and will be used for Searching"))
message(paste(Fastadatabase, "was selected as database.", "Candidates will be generated through",mode[1] ,"mode" ))
# prepare the proteome database, use_previous_candidates=T will override the other argument and load the candidate list directly
if(Load_candidatelist){
Protein_feature_list<-Protein_feature_list_fun(workdir=workdir,
database=Fastadatabase,
Digestion_site=Digestion_site,
missedCleavages=missedCleavages,
adducts=adducts,
BPPARAM = BPPARAM,
Decoy_adducts=Decoy_adducts,
Decoy_search=Decoy_search,
Decoy_mode = Decoy_mode,
output_candidatelist=output_candidatelist,
use_previous_candidates=use_previous_candidates,
Substitute_AA=Substitute_AA,
Modifications=Modifications,
mzrange=mzrange,
Protein_desc_of_exclusion=Protein_desc_of_exclusion,
Database_stats=Database_stats)
}
#
if(IMS_analysis){
message(paste(Fastadatabase,"was selected as database","\nSpectrum intensity threshold:",percent(threshold),"\nmz tolerance:",ppm,"ppm","Segmentation method:",Segmentation[1],
"\nManual segmentation def file:",ifelse(is.null(Virtual_segmentation_rankfile),"None",Virtual_segmentation_rankfile),"\nBypass spectrum generation:",Bypass_generate_spectrum))
#select candidate list for IMS annotation
Peptide_Summary_searchlist<-unique(Protein_feature_list)
Peptide_Summary_file<-IMS_data_process(datafile=datafile, workdir=workdir,
Peptide_Summary_searchlist=Peptide_Summary_searchlist,
segmentation_num=spectra_segments_per_file,
threshold=threshold,rotate = Rotate_IMG,
ppm=ppm,mzrange=mzrange,
Segmentation=Segmentation,
Segmentation_ncomp=Segmentation_ncomp,
PMFsearch = PMFsearch,
Virtual_segmentation_rankfile = Virtual_segmentation_rankfile,
BPPARAM = BPPARAM,
Bypass_generate_spectrum=Bypass_generate_spectrum,
score_method = score_method,
Decoy_mode=Decoy_mode,
Decoy_search=Decoy_search,
adjust_score=adjust_score,
peptide_ID_filter=peptide_ID_filter,
Protein_desc_of_interest=Protein_desc_of_interest,
plot_matching_score_t=plot_matching_score,
FDR_cutoff= FDR_cutoff,
Segmentation_def=Segmentation_def,
Segmentation_variance_coverage=Segmentation_variance_coverage,
preprocess=preprocess)
}
#Summarize the protein result across the datafiles and store these summarized files into the summary folder
if(Protein_feature_summary){
message("Protein feature summary...")
Peptide_Summary_file<-NULL
Protein_peptide_Summary_file<-NULL
protein_feature_all<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
protein_feature<-NULL
for (protein_feature_file in dir()[stringr::str_detect(dir(),"Protein_segment_PMF_RESULT_")]){
protein_feature<-fread(protein_feature_file)
if(nrow(protein_feature)!=0){
protein_feature$Source<-datafilename
region_code<-str_replace(protein_feature_file,"Protein_segment_PMF_RESULT_","")
region_code<-str_replace(region_code,".csv","")
protein_feature$Region<-region_code
protein_feature_all<-rbind(protein_feature_all,protein_feature)
}
}
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-datafilename
if(nrow(Peptide_Summary_file)!=0){
Protein_peptide_Summary_file<-rbind(Protein_peptide_Summary_file,Peptide_Summary_file)
}
}
message("Protein feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(protein_feature_all,paste(workdir,"/Summary folder/Protein_Summary.csv",sep=""),row.names = F)
write.csv(Protein_peptide_Summary_file,paste(workdir,"/Summary folder/Protein_peptide_Summary.csv",sep=""),row.names = F)
}
#Summarize the protein and peptide result across the datafiles and store these summarized files into the summary folder
if(Peptide_feature_summary){
message("Peptide feature summary...")
Peptide_Summary_file<-NULL
Peptide_Summary_file_a<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(paste(currentdir,sep=""))
Peptide_Summary_file<-fread("Peptide_region_file.csv")
Peptide_Summary_file$Source<-gsub(".imzML", "", datafile[i])
if(nrow(Peptide_Summary_file)!=0){
Peptide_Summary_file_a<-rbind(Peptide_Summary_file_a,Peptide_Summary_file)
}
}
Peptide_Summary_file_a<-unique(Peptide_Summary_file_a)
message("Peptide feature summary...Done.")
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
write.csv(Peptide_Summary_file_a,paste(workdir,"/Summary folder/Peptide_Summary.csv",sep=""),row.names = F)
#Peptide_feature_summary_all_files_new(datafile,workdir,threshold = threshold)
}
#Summarize the mz feature list
if(Region_feature_summary){
message("Region feature summary...")
Spectrum_summary<-NULL
for (i in 1:length(datafile)){
datafilename<-gsub(paste(workdir,"/",sep=""),"",gsub(".imzML", "", datafile[i]))
currentdir<-paste0(workdir,"/",datafile[i]," ID")
setwd(currentdir)
name <-gsub(base::dirname(datafile[i]),"",gsub(".imzML", "", datafile[i]))
message(paste("Region_feature_summary",gsub(".imzML", "", datafile[i])))
if (dir.exists(paste(workdir,"/Summary folder",sep=""))==FALSE){dir.create(paste(workdir,"/Summary folder",sep=""))}
match_pattern <- "Spectrum.csv"
spectrum_file_table_sum<-NULL
for (spectrum_file in dir(recursive = T)[str_detect(dir(recursive = T), match_pattern)]){
spectrum_file_table=fread(spectrum_file)
if (nrow(spectrum_file_table)>=1){
spectrum_file_table$Region=gsub("/Spectrum.csv","",spectrum_file)
spectrum_file_table$Source<-gsub(".imzML", "", datafile[i])
spectrum_file_table_sum[[spectrum_file]] <- spectrum_file_table
}
}
spectrum_file_table_sum <- do.call(rbind,spectrum_file_table_sum)
Spectrum_summary[[datafile[i]]]=spectrum_file_table_sum
}
Spectrum_summary <- do.call(rbind,Spectrum_summary)
write.csv(Spectrum_summary,file = paste(workdir,"/Summary folder/Region_feature_summary.csv",sep=""),row.names = F)
}
#Protein cluster image rendering
if(plot_cluster_image_grid){
message("cluster image rendering...")
setwd(workdir[1])
# read protein-peptide features result
Protein_feature_list=read.csv(file=paste(workdir[1],"/Summary folder/Protein_peptide_Summary.csv",sep=""),stringsAsFactors = F)
# remove peptide score outlier from result
if (remove_score_outlier){
Protein_feature_list <- remove_pep_score_outlier(Protein_feature_list,abs_cutoff=Plot_score_abs_cutoff,IQR_LB = Plot_score_IQR_cutoff)
}
# extract the protein entries of interest
if (sum(Protein_desc_of_interest!=".")>=1){
Protein_feature_list_interest<-NULL
num_of_interest<-numeric(0)
for (interest_desc in Protein_desc_of_interest){
idx_iterest_desc<-str_detect(Protein_feature_list$desc,regex(interest_desc,ignore_case = T))
if(nrow(Protein_feature_list[idx_iterest_desc,])!=0){
Protein_feature_list_interest<-rbind(Protein_feature_list_interest,Protein_feature_list[idx_iterest_desc,])
}
num_of_interest[interest_desc]<-length(unique(Protein_feature_list[idx_iterest_desc,"Protein"]))
}
Protein_feature_list=Protein_feature_list_interest
message(paste(num_of_interest,"Protein(s) found with annotations of interest:",Protein_desc_of_interest,collapse = "\n"))
}
Protein_feature_list=as.data.frame(Protein_feature_list)
# generate combined IMS data for multiple files or use a link to load the pre-processed IMS data
if (!is.null(cluster_rds_path)){
cluster_rds_path
imdata=readRDS(paste0(workdir[1],"/",cluster_rds_path))
message("cluster imdata loaded.")
}else{
cluster_rds_path<-Load_IMS_decov_combine(datafile=datafile,workdir=workdir,import_ppm=ppm,SPECTRUM_batch="overall",mzAlign_runs=mzAlign_runs,
ppm=ppm,threshold=0,rotate=Rotate_IMG,mzrange=mzrange,
deconv_peaklist=deconv_peaklist,preprocessRDS_rotated=T)
imdata=readRDS(paste0(workdir[1],"/",basename(cluster_rds_path)))
message("cluster imdata generated and loaded.")
}
# test combined imdata
if (class(imdata)[1]=="matrix"){
do.call(Cardinal::cbind,imdata)->imdata
saveRDS(imdata,paste0(workdir[1],"/combinedimdata.rds"),compress = T)
}
# Setup output folder and queue the R calls for cluster image randering
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
if (!(plot_unique_component)){
setwd(outputfolder)
Protein_feature_list_trimmed<-Protein_feature_list
}
if (plot_unique_component){
outputfolder=paste(workdir,"/Summary folder/cluster Ion images/unique/",sep="")
if (dir.exists(outputfolder)==FALSE){dir.create(outputfolder)}
setwd(outputfolder)
Protein_feature_list_unique=Protein_feature_list %>% group_by(mz) %>% dplyr::summarise(num=length(unique(Protein)))
Protein_feature_list_unique_mz<-Protein_feature_list_unique$mz[Protein_feature_list_unique$num==1]
Protein_feature_list_trimmed<-Protein_feature_list[Protein_feature_list$mz %in% Protein_feature_list_unique_mz, ]
write.csv(Protein_feature_list_trimmed,paste(workdir,"/Summary folder/Protein_feature_list_trimmed.csv",sep=""),row.names = F)
}
#list_of_protein_sequence[!(1:length(list_of_protein_sequence) %in% as.numeric(Protein_feature_list_trimmed$Protein))]<-""
#unname(as.character(list_of_protein_sequence))->str_vec
#imdata <- imdata %>% peakBin(ref=sort(unique((Protein_feature_list_trimmed$mz_align+Protein_feature_list_trimmed$mz)/2)), tolerance=ppm, units="ppm") %>% process(BPPARAM=SerialParam())
save(list=c("Protein_feature_list_trimmed",
"imdata",
"ClusterID_colname",
"componentID_colname",
"plot_layout",
"export_Header_table",
"export_footer_table",
"attach_summary_cluster",
"remove_cluster_from_grid",
"smooth.image",
"Component_plot_coloure",
"cluster_color_scale",
"list_of_protein_sequence",
"outputfolder",
"peptide_ID_filter",
"ppm",
"img_brightness",
"pixel_size_um"
),
file=paste0(workdir,"/cluster_img_grid.RData"))
for (clusterID in unique(Protein_feature_list_trimmed$Protein)){
cluster_desc<-unique(Protein_feature_list_trimmed$desc[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID])
cluster_desc<-gsub(stringr::str_extract(cluster_desc,"OS=.{1,}"),"",cluster_desc)
n_component<-nrow(unique(Protein_feature_list_trimmed[Protein_feature_list_trimmed[[ClusterID_colname]]==clusterID,c(ClusterID_colname,componentID_colname,"moleculeNames","adduct","Modification")]))
if (n_component>=peptide_ID_filter){
if ('&'(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png")),!plot_cluster_image_overwrite)){
message("Cluster image rendering Skipped file exists: No.",clusterID," ",cluster_desc)
next
}else {
fileConn<-file(paste0(workdir,"/cluster_img_scource.R"),)
writeLines(c("suppressMessages(suppressWarnings(require(HiTMaP)))",
paste0("clusterID=",clusterID),
paste0("suppressMessages(suppressWarnings(load(file =\"", workdir,"/cluster_img_grid.RData\")))"),
"suppressMessages(suppressWarnings(cluster_image_grid(clusterID = clusterID,
imdata=imdata,
SMPLIST=Protein_feature_list_trimmed,
ppm=ppm,
ClusterID_colname=ClusterID_colname,
componentID_colname=componentID_colname,
plot_layout=plot_layout,
export_Header_table=export_Header_table,
export_footer_table=export_footer_table,
attach_summary_cluster=attach_summary_cluster,
remove_cluster_from_grid=remove_cluster_from_grid,
plot_style=\"fleximaging\",
smooth.image=smooth.image,
Component_plot_coloure=Component_plot_coloure,
cluster_color_scale=cluster_color_scale,
list_of_protein_sequence=list_of_protein_sequence,
workdir=outputfolder,
pixel_size_um=pixel_size_um,
img_brightness=img_brightness,
Component_plot_threshold=peptide_ID_filter)))"),
fileConn)
close(fileConn)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if(file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
}else{
retrytime=1
repeat{
message("Cluster image rendering failed and retry ",retrytime,": No.",clusterID," ",cluster_desc)
system(paste0("Rscript \"",paste0(workdir,"/cluster_img_scource.R\"")))
if (file.exists(paste0(outputfolder,clusterID,"_cluster_imaging.png"))){
message("Cluster image rendering Done: No.",clusterID," ",cluster_desc)
break
}else if(retrytime>=plot_cluster_image_maxretry){
message("Cluster image rendering reaches maximum Retry Attempts: No.",clusterID," ",cluster_desc)
break
}
retrytime=1+retrytime
}
}
}
}
}
}
message("Workflow done.")
}
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.