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Merge pull request #36 from UMCUGenetics/release/v2.2.0
Release/v2.2.0 (DIMS)
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#!/usr/bin/Rscript | ||
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.libPaths(new = "/hpc/local/CentOS7/dbg_mz/R_libs/3.2.2") | ||
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# load required packages | ||
# none | ||
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# define parameters | ||
cmd_args <- commandArgs(trailingOnly = TRUE) | ||
for (arg in cmd_args) cat(" ", arg, "\n") | ||
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outdir <- cmd_args[1] | ||
scanmode <- cmd_args[2] | ||
normalization <- cmd_args[3] | ||
scripts <- cmd_args[4] | ||
z_score <- as.numeric(cmd_args[5]) | ||
ppm <- as.numeric(cmd_args[6]) | ||
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#outdir <- "/Users/nunen/Documents/Metab/processed/test_old" | ||
#scanmode <- "negative" | ||
#normalization <- "disabled" | ||
#scripts <- "/Users/nunen/Documents/Metab/DIMS/scripts" | ||
#db <- "/Users/nunen/Documents/Metab/DIMS/db/HMDB_add_iso_corrNaCl_withIS_withC5OH.RData" | ||
#z_score <- 0 | ||
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object.files = list.files(paste(outdir, "9-samplePeaksFilled", sep="/"), full.names=TRUE, pattern=scanmode) | ||
outlist.tot=NULL | ||
for (i in 1:length(object.files)) { | ||
load(object.files[i]) | ||
print(print(object.files[i])) | ||
outlist.tot = rbind(outlist.tot, final.outlist.idpat3) | ||
} | ||
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source(paste(scripts, "AddOnFunctions/sourceDir.R", sep="/")) | ||
sourceDir(paste(scripts, "AddOnFunctions", sep="/")) | ||
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# remove duplicates | ||
outlist.tot = mergeDuplicatedRows(outlist.tot) | ||
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# sort on mass | ||
outlist.tot = outlist.tot[order(outlist.tot[,"mzmed.pgrp"]),] | ||
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# normalization | ||
load(paste0(outdir, "/repl.pattern.",scanmode,".RData")) | ||
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if (normalization != "disabled") { | ||
outlist.tot = normalization_2.1(outlist.tot, fileName, names(repl.pattern.filtered), on=normalization, assi_label="assi_HMDB") | ||
} | ||
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if (z_score == 1) { | ||
outlist.stats = statistics_z(outlist.tot, sortCol=NULL, adducts=FALSE) | ||
nr.removed.samples=length(which(repl.pattern.filtered[]=="character(0)")) | ||
order.index.int=order(colnames(outlist.stats)[8:(length(repl.pattern.filtered)-nr.removed.samples+7)]) | ||
outlist.stats.more = cbind(outlist.stats[,1:7], | ||
outlist.stats[,(length(repl.pattern.filtered)-nr.removed.samples+8):(length(repl.pattern.filtered)-nr.removed.samples+8+6)], | ||
outlist.stats[,8:(length(repl.pattern.filtered)-nr.removed.samples+7)][order.index.int], | ||
outlist.stats[,(length(repl.pattern.filtered)-nr.removed.samples+5+10):ncol(outlist.stats)]) | ||
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tmp.index=grep("_Zscore", colnames(outlist.stats.more), fixed = TRUE) | ||
tmp.index.order=order(colnames(outlist.stats.more[,tmp.index])) | ||
tmp = outlist.stats.more[,tmp.index[tmp.index.order]] | ||
outlist.stats.more=outlist.stats.more[,-tmp.index] | ||
outlist.stats.more=cbind(outlist.stats.more,tmp) | ||
outlist.tot = outlist.stats.more | ||
} | ||
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# filter identified compounds | ||
index.1=which((outlist.tot[,"assi_HMDB"]!="") & (!is.na(outlist.tot[,"assi_HMDB"]))) | ||
index.2=which((outlist.tot[,"iso_HMDB"]!="") & (!is.na(outlist.tot[,"iso_HMDB"]))) | ||
index=union(index.1,index.2) | ||
outlist.ident = outlist.tot[index,] | ||
outlist.not.ident = outlist.tot[-index,] | ||
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if (z_score == 1) { | ||
outlist.ident$ppmdev=as.numeric(outlist.ident$ppmdev) | ||
outlist.ident <- outlist.ident[which(outlist.ident["ppmdev"] >= -ppm & outlist.ident["ppmdev"] <= ppm),] | ||
} | ||
# NAs in theormz_noise <======================================================================= uitzoeken!!! | ||
outlist.ident$theormz_noise[which(is.na(outlist.ident$theormz_noise))] = 0 | ||
outlist.ident$theormz_noise=as.numeric(outlist.ident$theormz_noise) | ||
outlist.ident$theormz_noise[which(is.na(outlist.ident$theormz_noise))] = 0 | ||
outlist.ident$theormz_noise=as.numeric(outlist.ident$theormz_noise) | ||
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save(outlist.not.ident, outlist.ident, file=paste(outdir, "/outlist_identified_", scanmode, ".RData", sep="")) | ||
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# Extra output in Excel-readable format: | ||
remove_columns <- c("fq.best", "fq.worst", "mzmin.pgrp", "mzmax.pgrp") | ||
remove_colindex <- which(colnames(outlist.ident) %in% remove_columns) | ||
outlist.ident <- outlist.ident[ , -remove_colindex] | ||
write.table(outlist.ident, file=paste0(outdir, "/outlist_identified_", scanmode, ".txt"), sep="\t", row.names = FALSE) | ||
remove_colindex <- which(colnames(outlist.not.ident) %in% remove_columns) | ||
outlist.not.ident <- outlist.not.ident[ , -remove_colindex] | ||
write.table(outlist.not.ident, file=paste0(outdir, "/outlist_not_identified_", scanmode, ".txt"), sep="\t", row.names = FALSE) | ||
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add_lab_id_and_onderzoeksnummer <- function(df_metabs_helix) { | ||
# Split patient number into labnummer and Onderzoeksnummer | ||
for (row in 1:nrow(df_metabs_helix)) { | ||
df_metabs_helix[row,"labnummer"] <- gsub("^P|\\.[0-9]*", "", df_metabs_helix[row,"Patient"]) | ||
labnummer_split <- strsplit(as.character(df_metabs_helix[row, "labnummer"]), "M")[[1]] | ||
df_metabs_helix[row, "Onderzoeksnummer"] <- paste0("MB", labnummer_split[1], "/", labnummer_split[2]) | ||
} | ||
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return(df_metabs_helix) | ||
} |
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checkOverlap <- function(range1,range2){ | ||
if (length(intersect(range1,range2))==2) { | ||
# Overlap | ||
# message("Overlap, smaller range is used") | ||
if (length(range1) >= length(range2)){ | ||
range1=range1[-length(range1)] | ||
} else { | ||
range2=range2[-1] | ||
} | ||
} else if (length(intersect(range1,range2))==3){ | ||
# message("Overlap, smaller range is used") | ||
range1=range1[-length(range1)] | ||
range2=range2[-1] | ||
} | ||
return(list("range1"=range1,"range2"=range2)) | ||
} |
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# function to test whether intensity and Z-score columns match | ||
check_same_samplename <- function(int_col_name, zscore_col_name) { | ||
paste0(int_col_name, "_Zscore") == zscore_col_name | ||
} |
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create_violin_plots <- function(pdf_dir, pt_name, metab_perpage, top_metab_pt=NULL) { | ||
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# set parameters for plots | ||
plot_height <- 9.6 | ||
plot_width <- 6 | ||
fontsize <- 1 | ||
nr_plots_perpage <- 20 | ||
circlesize <- 0.8 | ||
colors_4plot <- c("#22E4AC", "#00B0F0", "#504FFF","#A704FD","#F36265","#DA0641") | ||
# green blue blue/purple purple orange red | ||
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# patient plots, create the PDF device | ||
pt_name_sub <- pt_name | ||
suffix <- "" | ||
if (grepl("Diagnostics", pdf_dir) & is_diagnostic_patient(pt_name)) { | ||
prefix <- "MB" | ||
suffix <- "_DIMS_PL_DIAG" | ||
# substitute P and M in P2020M00001 into right format for Helix | ||
pt_name_sub <- gsub("[PM]", "", pt_name) | ||
pt_name_sub <- gsub("\\..*", "", pt_name_sub) | ||
} else if (grepl("Diagnostics", pdf_dir)) { | ||
prefix <- "Dx_" | ||
} else if (grepl("IEM", pdf_dir)) { | ||
prefix <- "IEM_" | ||
} else { | ||
prefix <- "R_" | ||
} | ||
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pdf(paste0(pdf_dir, "/", prefix, pt_name_sub, suffix, ".pdf"), | ||
onefile = TRUE, | ||
width = plot_width, | ||
height = plot_height) | ||
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# page headers: | ||
page_headers <- names(metab_perpage) | ||
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# put table into PDF file, if not empty | ||
if (!is.null(dim(top_metab_pt))) { | ||
plot.new() | ||
# get the names and numbers in the table aligned | ||
table_theme <- ttheme_default(core = list(fg_params = list(hjust=0, x=0.05, fontsize=6)), | ||
colhead = list(fg_params = list(fontsize=8, fontface="bold"))) | ||
grid.table(top_metab_pt, theme = table_theme, rows = NULL) | ||
# g <- tableGrob(top_metab_pt) | ||
# grid.draw(g) | ||
text(x=0.45, y=1.02, paste0("Top deviating metabolites for patient: ", pt_name), font=1, cex=1) | ||
} | ||
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# violin plots | ||
for (page_index in 1:length(metab_perpage)) { | ||
# extract list of metabolites to plot on a page | ||
metab_list_2plot <- metab_perpage[[page_index]] | ||
# extract original data for patient of interest (pt_name) before cut-offs | ||
pt_list_2plot_orig <- metab_list_2plot[which(metab_list_2plot$variable == pt_name), ] | ||
# cut off Z-scores higher than 20 or lower than -5 (for nicer plots) | ||
metab_list_2plot$value[metab_list_2plot$value > 20] <- 20 | ||
metab_list_2plot$value[metab_list_2plot$value < -5] <- -5 | ||
# extract data for patient of interest (pt_name) | ||
pt_list_2plot <- metab_list_2plot[which(metab_list_2plot$variable == pt_name), ] | ||
# restore original Z-score before cut-off, for showing Z-scores in PDF | ||
pt_list_2plot$value_orig <- pt_list_2plot_orig$value | ||
# remove patient of interest (pt_name) from list; violins will be made up of controls and other patients | ||
metab_list_2plot <- metab_list_2plot[-which(metab_list_2plot$variable == pt_name), ] | ||
# subtitle per page | ||
sub_perpage <- gsub("_", " ", page_headers[page_index]) | ||
# for IEM plots, put subtitle on two lines | ||
sub_perpage <- gsub("probability", "\nprobability", sub_perpage) | ||
# add size parameter for showing Z-score of patient per metabolite | ||
Z_size <- rep(3, nrow(pt_list_2plot)) | ||
# set size to 0 if row is empty | ||
Z_size[is.na(pt_list_2plot$value)] <- 0 | ||
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# draw violin plot. shape=22 gives square for patient of interest | ||
ggplot_object <- ggplot(metab_list_2plot, aes(x=value, y=HMDB_name)) + | ||
theme(axis.text.y=element_text(size=rel(fontsize)), plot.caption = element_text(size=rel(fontsize))) + | ||
xlim(-5, 20) + | ||
geom_violin(scale="width") + | ||
geom_point(data = pt_list_2plot, aes(color=value), size = 3.5*circlesize, shape=22, fill="white") + | ||
scale_fill_gradientn(colors = colors_4plot, values = NULL, space = "Lab", na.value = "grey50", guide = "colourbar", aesthetics = "colour") + | ||
# add Z-score value for patient of interest at x=16 | ||
geom_text(data = pt_list_2plot, aes(16, label = paste0("Z=", round(value_orig, 2))), hjust = "left", vjust = +0.2, size = Z_size) + | ||
# add labels. Use font Courier to get all the plots in the same location. | ||
labs(x = "Z-scores", y = "Metabolites", subtitle = sub_perpage, color = "z-score") + | ||
theme(axis.text.y = element_text(family = "Courier", size=6)) + | ||
# do not show legend | ||
theme(legend.position="none") + | ||
# add title | ||
ggtitle(label = paste0("Results for patient ", pt_name)) + | ||
# labs(x = "Z-scores", y = "Metabolites", title = paste0("Results for patient ", pt_name), subtitle = sub_perpage, color = "z-score") + | ||
# add vertical lines | ||
geom_vline(xintercept = 2, col = "grey", lwd = 0.5, lty=2) + | ||
geom_vline(xintercept = -2, col = "grey", lwd = 0.5, lty=2) | ||
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suppressWarnings(print(ggplot_object)) | ||
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} | ||
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# add explanation of violin plots, version number etc. | ||
# plot.new() | ||
plot(NA, xlim=c(0,5), ylim=c(0,5), bty='n', xaxt='n', yaxt='n', xlab='', ylab='') | ||
if (length(explanation) > 0) { | ||
text(0.2, 5, explanation[1], pos=4, cex=0.8) | ||
for (line_index in 2:length(explanation)) { | ||
text_y_position <- 5 - (line_index*0.2) | ||
text(-0.2, text_y_position, explanation[line_index], pos=4, cex=0.5) | ||
} | ||
# full_explanation <- paste(explanation[2:length(explanation)], sep=" \n") | ||
# text(0.2, 4, full_explanation, pos=4, cex=0.6) | ||
#explanation_grob=textGrob(apply(full_explanation, 2, paste, collapse="\n")) | ||
#grid.arrange(explanation_grob) | ||
} | ||
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# close the PDF file | ||
dev.off() | ||
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} |
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elementInfo <- function(name, elements = NULL) { # from Rdisop function .getElement | ||
if (!is.list(elements) || length(elements)==0 ) { | ||
elements <- initializePSE() } | ||
if (name=="CH3OH+H"){rex<-"^CH3OH\\+H$"}else{rex <- paste ("^",name,"$", sep="")} | ||
elements [[grep (rex, sapply (elements, function(x) {x$name}))]] | ||
} |
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export <- function(peaklist, plotdir, adducts, control_label, case_label, patients, sub, fileName){ | ||
# peaklist = outlist.adducts | ||
# adducts=TRUE | ||
# control_label="C" | ||
# case_label="P" | ||
# patients = getPatients(outlist.adducts) | ||
# sub=3000 | ||
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# peaklist = statistics_z_4export(as.data.frame(peaklist), plotdir, patients, adducts, control_label, case_label) | ||
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# generateExcelFile(peaklist, file.path(plotdir), imageNum=2, fileName, subName=c("","_box"), sub, adducts) | ||
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} |
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### fit Gaussian estimate mean and integrate to obtain intensity | ||
findPeaks.Gauss.HPC <- function(plist, breaks.fwhm, int.factor, scale, resol, outdir, scanmode, plot, thresh, width, height) { | ||
sampname <- colnames(plist)[1] | ||
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range <- as.vector(plist) | ||
names(range) <- rownames(plist) | ||
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values <- list("mean"=NULL, "area"=NULL, "nr"=NULL, "min"=NULL, "max"=NULL, "qual"=NULL, "spikes"=0) | ||
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values <- searchMZRange(range, values, int.factor, scale, resol, outdir, sampname, scanmode, plot, width, height, thresh) | ||
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outlist.persample <- NULL | ||
outlist.persample <- cbind("samplenr"=values$nr, "mzmed.pkt"=values$mean, "fq"=values$qual, "mzmin.pkt"=values$min, "mzmax.pkt"=values$max, "height.pkt"=values$area) | ||
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index <- which(outlist.persample[ ,"height.pkt"]==0) | ||
if (length(index) > 0) { | ||
outlist.persample <- outlist.persample[-index,] | ||
} | ||
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# save(outlist.persample, file=paste(outdir, paste(sampname, "_", scanmode, ".RData", sep=""), sep="/")) | ||
save(outlist.persample, file=paste("./", sampname, "_", scanmode, ".RData", sep="")) | ||
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cat(paste("There were", values$spikes, "spikes!")) | ||
} |
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