pushing 1st version after restructure from gitlab

container/README.md

+# Pipeline containers
+
+To have the required containers available, they must be built locally. For distribution license reasons this repository does not contain the containers itself but the recipes to easily build locally (requires [singularity](https://sylabs.io/docs/)). Examples can be found in [build_local.sh](build_local.sh).
+For the containers found on DockerHub, it is simply:
+```
+singularity build upstream/openswath:0.1.2.simg docker://openswath/openswath:0.1.2
+singularity build upstream/pyprophet_2.1.3.simg docker://pyprophet/pyprophet:2.1.3
+```
+For a specific container that is not on DockerHub, change into the directories for the chosen container that contains a `*.sdef` file. From there, it is for example:
+```
+singularity build downstream/pridereswath-downstream.simg python-r:bioc-py3-tinytex-qc_plus_msstats.sdef
+```
+__Note:__ After containers are built, the workflow [parameter file](../inputs/configs/pridereswath_v1.1.yaml) needs to be adjusted to reflect the location of the respective containers.
+
+

container/build-local.sh

+#!/usr/bin/env bash
+
+# upstream
+singularity build upstream/openswath:0.1.2.simg docker://openswath/openswath:0.1.2
+singularity build upstream/pyprophet_2.1.3.simg docker://pyprophet/pyprophet:2.1.3
+singularity build upstream/sciex_converter_prideswath.simg container/upstream/wiffconverter/sciex_converter_prideswath.sdef
+singularity build upstream/pridereswath.simg container/upstream/pridereswath/pridereswath-upstream.sdef 
+singularity build upstream/yamato:v1.0.4_pridereswath.simg container/upstream/yamato:v1.0.4_pridereswath.sdef
+
+# downstream
+# from docker builds:
+#sudo docker build -t python-r:bioc-py3-tinytex-qc_plus_msstats .
+#sudo singularity build python-r:bioc-py3-tinytex-qc_plus_msstats.simg docker-daemon://python-r:bioc-py3-tinytex-qc_plus_msstats
+# direct builds:
+singularity build downstream/pridereswath-downstream.simg downstream/python-r:bioc-py3-tinytex-qc_plus_msstats.sdef
+
+# postprocess
+# see downstream

container/downstream/DIA_downstream_datacarpentry.R

+require(tidyverse)
+require(stringr)
+
+run_median_norm <- function(data, ...) {
+  data %>% group_by(originalRUN) %>% 
+    mutate(run_sd = sd(LogIntensities), run_mdn = median(LogIntensities), run_count = n()) %>%
+    mutate(NormLogIntensities = LogIntensities-run_mdn) %>%
+    ungroup() %>%
+    dplyr::select(-run_sd, -run_mdn, -run_count)
+}
+
+run_quantile_norm <- function(data,... ){
+  matrix <- data %>% dplyr::select(Protein, originalRUN, LogIntensities) %>%
+    tidyr::spread(originalRUN, LogIntensities) %>% 
+    column_to_rownames("Protein") %>%
+  drop_na()  # alt. 0 imputation
+
+  # substitution function (according to rank)
+  index_lookup <- function(i, m){
+    return(m[i])
+  }
+  
+  data_rank <- map_df(matrix,rank,ties.method="average")  # 1. rank
+  data_sorted <- map_df(matrix,sort)  # 2. sort
+  data_mean <- rowMeans(data_sorted)  # 3. mean
+  data_norm <- map_df(data_rank, index_lookup, m=data_mean)  # 4. substitute
+  
+  final <- data_norm %>% #bind_cols(row.names(matrix),.) %>% rename(Protein = ...1) %>% 
+    bind_cols(matrix %>% rownames_to_column("Protein") %>% dplyr::select(Protein), . ) %>%
+    tidyr::gather(key= "originalRUN", value= "NormLogIntensities", -Protein)
+  
+  return(data %>% # dplyr::select(-NormLogIntensities) %>%  # in case 
+           dplyr::mutate(mergecol = paste(Protein,originalRUN,sep="_")) %>% 
+           dplyr::inner_join(final %>% 
+                      dplyr::mutate(mergecol= paste(Protein,originalRUN, sep="_")) %>%
+                      dplyr::select(-originalRUN,-Protein), 
+                    by= "mergecol") %>%
+           dplyr::select(-mergecol)
+  )
+}
+
+run_minmax_norm <- function(data, ...) {
+  data %>% group_by(originalRUN) %>% 
+    dplyr::mutate(run_min = min(LogIntensities), run_max = max(LogIntensities), run_count = n()) %>%
+    dplyr::mutate(NormLogIntensities = (LogIntensities-run_min)/(run_max-run_min)) %>%
+    dplyr::ungroup() %>%
+    dplyr::select(-run_min, -run_max, -run_count)
+}
+
+run_zscore_norm <- function(data, ...) {
+  # normalisation on log values
+  # https://www.researchgate.net/post/log_transformation_and_standardization_which_should_come_first
+  data %>% group_by(originalRUN) %>% 
+    dplyr::mutate(run_sd = sd(LogIntensities), run_mean = mean(LogIntensities), run_count = n()) %>%
+    dplyr::mutate(NormLogIntensities = (LogIntensities-run_mean)/run_sd) %>%
+    dplyr::ungroup() %>%
+    dplyr::select(-run_sd, -run_mean, -run_count)
+}
+
+group_consistency <- function(data, ...){
+  data %>%  
+    dplyr::group_by(Protein,GROUP_ORIGINAL) %>% 
+    dplyr::mutate( sumNumMeasuredFeature = sum(NumMeasuredFeature), sumNumImputedFeature = sum(NumImputedFeature) ) %>% 
+    dplyr::mutate( feature_missingrate_per_group = (100/(sumNumMeasuredFeature+sumNumImputedFeature)) * sumNumImputedFeature ) %>%
+    dplyr::ungroup() %>%
+    dplyr::mutate(feature_missingrate_per_run = MissingPercentage * 100) %>%
+    dplyr::select(-MissingPercentage)
+}
+
+clean_protein_names <- function(data, ...) {
+  data %>%
+    dplyr::mutate(Protein = str_replace(Protein, "^\\d+/", "")) %>% 
+    dplyr::mutate(Protein = str_replace(Protein, "/$", "")) %>%
+    dplyr::filter(Protein != "")
+}
+
+process_msstats_rda <- function(rda_obj, annot_obj, groups, norm="median") {
+  if(missing(groups)) {
+    groups <- as.character((rda_obj$RunlevelData %>% dplyr::distinct(GROUP_ORIGINAL))$GROUP_ORIGINAL)
+  }
+  return(our<-goldstandard.proposed$RunlevelData %>% 
+           group_consistency(annot) %>%
+           clean_protein_names() %>% 
+           dplyr::filter(GROUP_ORIGINAL %in% groups) %>%
+           {if(norm == "median") run_median_norm(.) else .} %>%
+           {if(norm == "quantile") run_quantile_norm(.) else .} %>%
+           {if(norm == "zscore") run_zscore_norm(.) else .} %>%
+           {if(norm == "minmax") run_minmax_norm(.) else .} %>%
+           dplyr::full_join(annot, by = c("originalRUN" = "Run")) %>%
+           dplyr::select(Protein,dplyr::matches("Intensities"),originalRUN,
+                         GROUP_ORIGINAL,SUBJECT_ORIGINAL,
+                         dplyr::matches("missingrate") ) %>%
+           dplyr::ungroup())
+}
+
+
+

container/downstream/DIA_downstream_process.R

+#!/usr/bin/env Rscript
+library("optparse")
+
+option_list = list(
+  make_option(c("-t", "--tric"), type="character", default=NULL, 
+              help="The results file from the first OpenSwath analysis pipeline.", metavar="character"),
+  make_option(c("-a", "--annot"), type="character", default=NULL, 
+              help="annotation.txt file for MSstats", metavar="character"),
+	make_option(c("-o", "--out"), type="character", default="goldstandard.rda", 
+              help="output file name [default= %default]", metavar="character")
+); 
+
+opt_parser = OptionParser(option_list=option_list);
+opt = parse_args(opt_parser);
+
+if (is.null(opt$tric) || is.null(opt$annot) ){
+  print_help(opt_parser)
+  stop("Required input arguments must be supplied.", call.=FALSE)
+}
+
+#con <- file(opt$tric,"r")
+#first_line <- readLines(con,n=1)
+#close(con)
+#print(first_line)
+#con <- file(opt$annot,"r")
+#first_line <- readLines(con,n=1)
+#close(con)
+#print(first_line)
+#stop("optparse test stop.")
+
+
+source("/scripts/OpenSWATHtoMSstatsFormat.R")
+library(MSstats)
+
+
+print ("Loading tric file...")
+raw.openMS <- read.csv(opt$tric,
+                       sep= "\t",
+                       header = TRUE,
+                       stringsAsFactors = FALSE)
+
+print ("Loading annotation file...")
+annot_osw <- read.csv(opt$annot,
+                      header = TRUE,
+                      sep = "\t")
+
+print ("Parsing annotation to MSstats format...")
+input.openms <- OpenSWATHtoMSstatsFormat_new(raw.openMS,
+                                         annotation = annot_osw,
+                                         removeProtein_with1Feature=TRUE)
+
+print("Run MSstats analysis...")
+goldstandard.proposed <-dataProcess(input.openms,
+                                    normalization=FALSE,
+                                    summaryMethod="TMP",
+                                    cutoffCensored="minFeature", 
+                                    censoredInt="0",
+                                    MBimpute=TRUE,
+                                    maxQuantileforCensored=0.999)
+
+print("Save the results")
+save(goldstandard.proposed, file=opt$out)
+

container/downstream/DIA_downstream_report.rmd

+---
+date: "`r Sys.Date()`"
+output:
+  pdf_document: default
+params:
+  rda: ""
+  ann: ""
+  pxdid: "REPLACEME"
+---
+
+---
+title: `r paste("DIA PRIDEreanalysis (downstream)", params$pxdid, sep = " - ")`
+---
+
+## DIA analysis with MSstats
+
+```{r include=FALSE}
+# Load required libraries
+library('tinytex')
+require(tidyverse)
+require(stringr)
+library(MSstats)
+library(data.table)
+library(knitr)
+library(RColorBrewer)
+library(heatmap3)
+library(ggplot2)
+library(patchwork)
+source('/scripts/DIA_downstream_datacarpentry.R')
+```
+
+```{r, echo=FALSE}
+#Annotations still needed for plots and tables
+annot <- read.csv(params$ann,
+                      header = TRUE,
+                      sep = "\t")
+		      
+#msstats_result get from DIA_downstream_process.R output (rename object)
+assign('msstats_result', get(load(file=params$rda)))
+
+prepro <- process_msstats_rda(msstats_result,annot)
+
+write.table(prepro %>%
+  dplyr::mutate(Intensities = `^`(2,LogIntensities)) %>%
+  dplyr::select(Protein, originalRUN, Intensities) %>%
+  tidyr::spread(originalRUN, Intensities),
+            paste(params$pxdid,"_baseline_raw_untransformed.tsv",sep=""),
+            quote = FALSE,
+            sep = "\t",
+            row.names = FALSE)
+
+protein_matrix <- prepro %>%
+  dplyr::filter(feature_missingrate_per_group < 50) %>%
+  dplyr::mutate(NormIntensities = `^`(2,NormLogIntensities)) %>%
+  dplyr::select(Protein, originalRUN, NormIntensities) %>%
+  tidyr::spread(originalRUN, NormIntensities) 
+
+write.table(protein_matrix,
+            paste(params$pxdid,"_baseline_mediannorm_untransformed.tsv",sep=""),
+            quote = FALSE,
+            sep = "\t",
+            row.names = FALSE)
+
+```
+
+### Annotation table:
+```{r, echo=FALSE}
+
+table_legend = data.frame(
+  index = seq(length(annot$Run)),
+  file_names = unlist(tstrsplit(annot$Run, "[.]", keep=1)),
+  Condition = annot$Condition,
+  BioReplicate = annot$BioReplicate
+)
+kable(table_legend)
+
+```
+
+###  Detection numbers
+
+```{r, echo=FALSE}
+prot_tot_distinct <- prepro %>% dplyr::distinct(Protein) %>% nrow(.)
+prot_tot_l50r <- prepro %>% dplyr::filter(feature_missingrate_per_run > 50) %>% dplyr::distinct(Protein) %>% nrow(.)
+prot_tot_l50g <- prepro %>% dplyr::filter(feature_missingrate_per_group > 50) %>% dplyr::distinct(Protein) %>% nrow(.)
+pep_tot <- length(levels(msstats_result$ProcessedData$PEPTIDE))
+irt_peptides <- unique(as.character(msstats_result$ProcessedData[which(msstats_result$ProcessedData$PROTEIN=="1/iRT_protein"),"PEPTIDE"]))
+irt_peptides = tryCatch(length(unique(unlist(tstrsplit(irt_peptides, "_", keep=1)))), 
+                        error = function(e) 0)
+
+kable(data.frame(
+    Analysis = c("Total num of proteins", 
+                 "Total num of proteins with <50% missing features per group",
+                 "Total num of proteins with <50% missing feature per run",
+                 "Total num of iRT peptides",
+                 "Total num of peptides"),
+    Measure = c(prot_tot_distinct,
+                prot_tot_l50g,
+                prot_tot_l50r,
+                irt_peptides,
+                pep_tot)),
+    col.names = NA)
+
+```
+
+\newpage
+
+### Normalisation inspection
+```{r, echo=FALSE, fig.align="center"}
+
+  ru <- ggplot(prepro, 
+               aes(x = LogIntensities , y = originalRUN , fill = GROUP_ORIGINAL) ) +
+    geom_boxplot(outlier.size = 0.5) + 
+    scale_fill_discrete(name="Experimental\nCondition") +
+    theme(legend.position = "none", axis.text.y=element_text(size=rel(0.5))) + 
+    labs(title=paste("Raw LogIntensities", params$pxdid, sep = " "),x="LogIntensity", y = "Runs")
+  nu <- ggplot(prepro, 
+               aes(x = NormLogIntensities , y = originalRUN , fill = GROUP_ORIGINAL) ) +
+    geom_boxplot(outlier.size = 0.5) + 
+    scale_fill_discrete(name="Experimental\nCondition") +
+    theme(legend.position = "none", axis.text.y=element_text(size=rel(0.5))) + 
+    labs(title=paste("Median normalised LogIntensities", params$pxdid, sep = " "),x="LogIntensity", y = "Runs")
+  
+# ru / nu + plot_layout(guides = "collect") & theme(legend.position = "right") 
+ru
+nu
+```
+
+### Clustering
+```{r echo=FALSE,  fig.align="center", dpi=270 , warning=FALSE}
+protein_matrix <- protein_matrix %>% column_to_rownames(var = "Protein")
+protein_matrix[is.na(protein_matrix)]=0
+pca_analysis<-prcomp(t(protein_matrix))
+var_expl <- pca_analysis$sdev^2/sum(pca_analysis$sdev^2)
+
+par(mar=c(5.1, 4.1, 4.1, 5.1), xpd=TRUE)
+plot(pca_analysis$x, type="n",
+     main="PCA Analysis:",
+     xlab = paste0("PC1 (", as.character(round(var_expl[1]*100,1)), "%)"),
+     ylab = paste0("PC2 (", as.character(round(var_expl[2]*100,1)), "%)"))
+text(pca_analysis$x, 
+     cex = 0.7,
+     label=seq(length(annot$Run)),
+     col = as.numeric(as.factor(annot$Condition)))
+
+
+legend("topright", inset=c(-.2,0), legend =levels(as.factor(annot$Condition)),pch=16, pt.cex=1.5, cex=0.75, bty='n',
+    col = seq_along(levels(as.factor(annot$Condition))))
+```
+
+```{r echo=FALSE,  fig.align="center", dpi=270 , warning=FALSE}
+par(mar=c(5,5,2,2),xaxs = "i",yaxs = "i",cex.axis=0.4,cex.lab=10)
+my_group1 <- as.numeric(as.factor(annot$Condition))
+
+Condition <- brewer.pal(9, "Set1")[my_group1]
+colSide <-cbind(Condition)
+colMain <- colorRampPalette(brewer.pal(10, "Blues"))(25)
+cor_results <- cor(protein_matrix)
+ColSideAnn<-data.frame(Condition=my_group1,stringsAsFactors=TRUE)
+heatmap3(main="Hierarchical Cluster Analysis:",
+         cor_results, 
+         RowSideColors=colSide,
+         ColSideColors=colSide,
+         col=colMain,
+         labRow = seq(length(annot$Run)),
+         cexRow = 0.4,
+         labCol = FALSE,
+         ColSideAnn=ColSideAnn,
+         ColSideWidth = 10,
+         legendfun=function() showLegend(legend=levels(as.factor(annot$Condition)),col=brewer.pal(9, "Set1")[seq_along(levels(as.factor(annot$Condition)))], cex=.9, title="Condition"))
+```
+
+```{r echo=FALSE,  fig.align="center", dpi=270 , warning=FALSE}
+par(mar=c(5,5,2,2),xaxs = "i",yaxs = "i",cex.axis=0.4,cex.lab=10)
+my_group1 <- as.numeric(as.factor(annot$Condition))
+
+Condition <- brewer.pal(9, "Set1")[my_group1]
+colSide <-cbind(Condition)
+colMain <- colorRampPalette(brewer.pal(10, "Blues"))(25)
+cor_results <- cor(protein_matrix)
+ColSideAnn<-data.frame(Condition=my_group1,stringsAsFactors=TRUE)
+heatmap3(cor_results, 
+         RowSideColors=colSide,
+         ColSideColors=colSide,
+         col=colMain,
+         labRow = seq(length(annot$Run)),
+         cexRow = 0.4,
+         labCol = FALSE,
+         ColSideAnn=ColSideAnn,
+         ColSideWidth = 10,
+         legendfun=function() showLegend(legend=levels(as.factor(annot$Condition)),col=brewer.pal(9, "Set1")[seq_along(levels(as.factor(annot$Condition)))], cex=.9, title="Condition"))
+```

container/downstream/Dockerfile.old

+FROM bioconductor/bioconductor_docker:RELEASE_3_12
+# bioconductor/bioconductor_docker:RELEASE_3_12 is FROM rocker/rstudio:4.0.3 
+# (i.e. 2 stages before the rocker image with tex - rocker/verse) 
+
+#install some more build tools and libs
+RUN apt-get update && apt-get install -y --no-install-recommends --no-install-suggests \ 
+  software-properties-common \
+  git \
+  vim \
+  libgomp1 \
+  zlib1g-dev \
+  libstdc++6 \
+  sudo \
+  build-essential \
+  libssl-dev \
+  libcurl4-openssl-dev \
+  gnupg2 \
+  python-dev \
+  python3-dev \
+  libxml2-dev \
+  libxslt-dev &&\
+  rm -rf /var/lib/apt/lists/*
+
+#setup R configs
+RUN Rscript -e "install.packages('Rcpp')"
+
+RUN Rscript -e "install.packages('tidyverse')"
+RUN Rscript -e "install.packages('dplyr')"
+RUN Rscript -e "install.packages('tibble')"
+RUN Rscript -e "install.packages('tidyr')"
+RUN Rscript -e "install.packages('data.table')"
+
+RUN Rscript -e "install.packages('RColorBrewer')"
+RUN Rscript -e "install.packages('heatmap3')"
+RUN Rscript -e "install.packages('ggplot2')"
+RUN Rscript -e "install.packages('graphics')"
+RUN Rscript -e "install.packages('cowplot')"
+RUN Rscript -e "install.packages('ggrepel')"
+RUN Rscript -e "install.packages('lubridate')"
+RUN Rscript -e "install.packages('chron')"
+RUN Rscript -e "install.packages('scales')"
+RUN Rscript -e "install.packages('grid')"
+RUN Rscript -e "install.packages('gridExtra')"
+
+RUN Rscript -e "install.packages('devtools')"
+RUN Rscript -e "remotes::install_github('twitter/AnomalyDetection')"
+RUN Rscript -e "BiocManager::install('MSstats')"
+RUN Rscript -e "install.packages('optparse')"
+
+RUN Rscript -e "install.packages('qcc')"
+RUN Rscript -e "install.packages('ggQC')"
+RUN Rscript -e "install.packages('MSQC')"
+RUN Rscript -e "install.packages('IQCC')"
+RUN Rscript -e "install.packages('yhatr')"
+RUN Rscript -e "install.packages('pracma')"
+RUN Rscript -e "install.packages('anomalize')"
+RUN Rscript -e "install.packages('DMwR')"
+RUN Rscript -e "install.packages('outliers')"
+
+RUN Rscript -e "install.packages('plotly')"
+RUN Rscript -e "install.packages('htmlwidgets')"
+RUN Rscript -e "install.packages('tinytex')"
+
+#RUN Rscript -e "tinytex::install_tinytex(version = '2020.11')"
+RUN wget -qO- \
+    "https://github.com/yihui/tinytex/raw/master/tools/install-unx.sh" | \
+    sh -s - --admin --no-path \
+  && mv ~/.TinyTeX /opt/TinyTeX \
+  && if /opt/TinyTeX/bin/*/tex -v | grep -q 'TeX Live 2018'; then \
+      ## Patch the Perl modules in the frozen TeX Live 2018 snapshot with the newer
+      ## version available for the installer in tlnet/tlpkg/TeXLive, to include the
+      ## fix described in https://github.com/yihui/tinytex/issues/77#issuecomment-466584510
+      ## as discussed in https://www.preining.info/blog/2019/09/tex-services-at-texlive-info/#comments
+      wget -P /tmp/ ${CTAN_REPO}/install-tl-unx.tar.gz \
+      && tar -xzf /tmp/install-tl-unx.tar.gz -C /tmp/ \
+      && cp -Tr /tmp/install-tl-*/tlpkg/TeXLive /opt/TinyTeX/tlpkg/TeXLive \
+      && rm -r /tmp/install-tl-*; \
+    fi \
+  && /opt/TinyTeX/bin/*/tlmgr path add \
+  && tlmgr install ae inconsolata listings metafont mfware parskip pdfcrop tex \
+  && tlmgr path add \
+  && Rscript -e "tinytex::r_texmf()" \
+  && chown -R root:staff /opt/TinyTeX \
+  && chmod -R g+w /opt/TinyTeX \
+  && chmod -R g+wx /opt/TinyTeX/bin \
+  && echo "PATH=${PATH}" >> /usr/local/lib/R/etc/Renviron
+RUN Rscript -e "install.packages('tinytex')"
+
+RUN pip3 install pip --upgrade
+RUN pip3 install setuptools --upgrade
+RUN pip3 install --force-reinstall 'pytest<=5.0.1'
+RUN pip3 install numpy pandas mypy pronto pytest jupyter rpy2 biopython flask
+
+COPY OpenSWATHtoMSstatsFormat.R /scripts/OpenSWATHtoMSstatsFormat.R
+COPY SWATHDIA_postprocess.R /scripts/SWATHDIA_postprocess.R 
+COPY diamsstats_qc.rmd /scripts/diamsstats_qc.rmd 

container/downstream/OpenSWATHtoMSstatsFormat.R

+OpenSWATHtoMSstatsFormat_new <- function(input,
+                                     annotation = NULL,
+                                     filter_with_mscore = TRUE,
+                                     mscore_cutoff = 0.01,
+                                     useUniquePeptide = TRUE,
+                                     fewMeasurements="remove",
+                                     removeProtein_with1Feature = FALSE,
+                                     summaryforMultipleRows=max){
+    library(dplyr)
+    library(tidyr)
+    if (is.null(fewMeasurements)) {
+        stop('** Please select \'remove\' or \'keep\' for \'fewMeasurements\'.')
+    }
+    
+    if (!is.element(fewMeasurements, c('remove', 'keep'))) {
+        stop('** Please select \'remove\' or \'keep\' for \'fewMeasurements\'.')
+    }
+    
+    if (is.null(annotation)) {
+        stop('** Please prepare \'annotation\' as one of input.')
+    } else {
+        
+        ## check annotation
+        required.annotation <- c('Condition', 'BioReplicate', 'Run')
+        
+        if (!all(required.annotation %in% colnames(annotation))) {
+            
+            missedAnnotation <- which(!(required.annotation %in% colnames(annotation)))
+            
+            stop(paste("**", toString(required.annotation[missedAnnotation]), 
+                       "is not provided in Annotation. Please check the annotation file."))
+        } else {
+            annotinfo <- annotation
+        }
+    }
+  
+    ## check annotation information
+    ## get annotation
+    ## Each Run should has unique information about condition and bioreplicate
+    check.annot <- xtabs(~Run, annotinfo)
+  
+    if ( any(check.annot > 1) ) {
+        stop('** Please check annotation. Each MS run can\'t have multiple conditions or BioReplicates.' )
+    }
+
+    ## Check correct option or input
+    requiredinput.general <- c('ProteinName', 'FullPeptideName', 'Charge', 'Sequence',
+                               'decoy', 'm_score',
+                               'aggr_Fragment_Annotation', 'aggr_Peak_Area', 
+                               'filename')
+    
+    ################################
+    ## 1. check general input and use only required columns.
+    ################################
+    
+    if (!all(requiredinput.general %in% colnames(input))) {
+        
+        misssing.col <- requiredinput.general[!requiredinput.general %in% colnames(input)]
+        
+        stop(paste0("** Please check the required input. The required input needs : ", 
+                   toString(missing.col)))
+    } else {
+        input <- input[, colnames(input) %in% requiredinput.general]
+    }
+    
+    ##############################
+    ## 2. remove the decoys
+    ##############################
+    if (length(unique(input$decoy)) == 2) {
+        
+        ## decoy=1 means the decoys.
+        input <- input[input$decoy == 0, ]
+        input <- input[, -which(colnames(input) %in% 'decoy')]
+        
+        message("** Remove the decoys.")
+    }
+  
+    ##############################
+    ## 3. filter by mscore
+    ##############################
+    
+    ## mscore
+    if (filter_with_mscore) {
+    
+        if (!is.element(c('m_score'), colnames(input))) {
+      
+            stop('** m_score column is needed in order to filter out by m_scoe. Please add m_score column in the input.')
+      
+        } else {
+      
+            ## when mscore > mscore_cutoff, replace with zero for intensity
+            input <- input[!is.na(input$m_score) & input$m_score <= mscore_cutoff, ] 
+      
+            message(paste0('** Features with great than ', mscore_cutoff, ' in m_score are removed.'))
+        }
+        
+        input <- input[, -which(colnames(input) %in% 'm_score')]
+    }
+    
+    ##############################
+    ## 4. Make required long format - disaggregate : one row for each transition
+    ##############################
+    
+    ## The columns "aggr_Fragment_Annotation" : separate by ';' and "aggr_Peak_Area" : separate by ';' 
+    ## are disaggregated into the new columns "FragmentIon" and "Intensity". 
+    input <- separate_rows(input, "aggr_Fragment_Annotation", "aggr_Peak_Area", sep="[;]")