# loadpage server functions
getEvidence <- function(input) {
evidence = input$evidence
if(is.null(input$evidence)) {
return(NULL)
}
evidence = try(read.table(evidence$datapath, sep="\t", header=TRUE), silent=TRUE)
if (is(evidence,"try-error")) {
evidence = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in evidence\n")
return(evidence)
}
getEvidence2 <- function(input) {
evidence2 = input$evidence2
if(is.null(input$evidence2)) {
return(NULL)
}
evidence2 = try(read.delim(evidence2$datapath), silent=TRUE)
if (is(evidence2,"try-error")) {
evidence2 = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in evidence\n")
return(evidence2)
}
getGlobal <- function(input) {
unmod = input$unmod
if(is.null(input$unmod)) {
return(NULL)
}
unmod = try(read.csv(unmod$datapath, sep=",", header=TRUE,
stringsAsFactors=FALSE), silent=TRUE)
if (is(unmod,"try-error")) {
unmod = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in unmod\n")
return(unmod)
}
getProteinGroups <- function(input) {
pGroup = input$pGroup
if(is.null(input$pGroup)) {
return(NULL)
}
pGroup = try(read.table(pGroup$datapath, sep="\t", header=TRUE), silent=TRUE)
if (is(pGroup,"try-error")) {
pGroup = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in proteins_group\n")
return(pGroup)
}
getProteinGroups2 <- function(input) {
pGroup2 = input$pGroup2
if(is.null(input$pGroup2)) {
return(NULL)
}
pGroup2 = try(read.delim(pGroup2$datapath), silent=TRUE)
if (is(pGroup2,"try-error")) {
pGroup2 = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in proteins_group\n")
return(pGroup2)
}
getFragSummary <- function(input) {
fragSummary = input$fragSummary
if(is.null(input$fragSummary)) {
return(NULL)
}
fragSummary = try(read.table(fragSummary$datapath, sep="\t", header=TRUE),
silent=TRUE)
if (is(fragSummary,"try-error")) {
fragSummary = "File load error. Please ensure file is in excel format."
}
return(fragSummary)
}
getPeptideSummary <- function(input) {
peptideSummary = input$peptideSummary
if(is.null(input$peptideSummary)) {
return(NULL)
}
peptideSummary = try(read.table(peptideSummary$datapath, sep="\t",
header=TRUE), silent=TRUE)
if (is(peptideSummary,"try-error")) {
peptideSummary = "File load error. Please ensure file is in csv format."
}
return(peptideSummary)
}
getProtSummary <- function(input) {
protSummary = input$protSummary
if(is.null(input$protSummary)) {
return(NULL)
}
protSummary = try(read.table(protSummary$datapath, sep="\t", header=TRUE),
silent=TRUE)
if (is(protSummary,"try-error")) {
protSummary = "File load error. Please ensure file is in csv format."
}
return(protSummary)
}
getMaxqPtmSites <- function(input) {
maxq_ptm_sites = input$maxq_ptm_sites
if(is.null(input$maxq_ptm_sites)) {
return(NULL)
}
maxq_ptm_sites = try(read.delim(maxq_ptm_sites$datapath), silent=TRUE)
if (is(maxq_ptm_sites,"try-error")) {
maxq_ptm_sites = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in maxq_ptm_sites\n")
return(maxq_ptm_sites)
}
getAnnot3 <- function(input) {
annot3 = input$annot3
if(is.null(input$annot3)) {
return(NULL)
}
annot3 = try(read.delim(annot3$datapath, sep=","), silent=TRUE)
if (is(annot3,"try-error")) {
annot3 = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in ump annot\n")
return(annot3)
}
getAnnot2 <- function(input) {
annot1 = input$annot2
if(is.null(input$annot2)) {
return(NULL)
}
annot2=try(read.csv(annot1$datapath, header = TRUE), silent=TRUE)
if (is(annot2,"try-error")) {
annot2 = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in ump annot\n")
return(annot2)
}
getAnnot <- function(input) {
print("Inside Annot");
annot = input$annot
if(is.null(annot)) {
return(NULL)
}
else if (input$DDA_DIA == "TMT" && input$filetype == "sample")
{
return(annotation.pd)
}
print("before reading annot file")
annot_file = try(read.csv(annot$datapath), silent=TRUE)
print("after reading annot file")
if(is(annot_file,"try-error")) {
annot_file = "File load error. Please ensure file is in csv format."
}
return(annot_file)
}
getAnnot1 <- function(input) {
annot1 = input$annot1
if(is.null(input$annot1)) {
return(NULL)
}
annot1=try(read.csv(annot1$datapath, header = TRUE), silent=TRUE)
if (is(annot1,"try-error")) {
annot1 = "File load error. Please ensure file is in csv format."
}
cat(file=stderr(), "Reached in maxq annot\n")
return(annot1)
}
getData <- function(input) {
print("+++++++++ In getData +++++++++")
show_modal_spinner()
ev_maxq = getEvidence(input)
pg_maxq = getProteinGroups(input)
# ev_maxq2 = getEvidence2(input)
# pg_maxq2 = getProteinGroups2(input)
an_maxq = getAnnot1(input)
raw.frag = getFragSummary(input)
raw.pep = getPeptideSummary(input)
raw.pro = getProtSummary(input)
annot2 = getAnnot2(input)
annot3 = getAnnot3(input)
unmod = getGlobal(input)
# ptm_sites_data = getMaxqPtmSites(input)
cat(file=stderr(), "Reached in get_data\n")
cat(file=stderr(), paste("File type is",input$filetype,"\n"))
if(is.null(input$filetype)) {
return(NULL)
}
if(input$filetype == 'sample') {
if(input$BIO != "PTM" && input$DDA_DIA =='LType' && input$LabelFreeType == "SRM_PRM") {
mydata = MSstats::DDARawData
}
else if(input$BIO != "PTM" && input$DDA_DIA == 'LType' && input$LabelFreeType == "DDA") {
mydata = MSstats::DDARawData
}
else if(input$BIO != "PTM" && input$DDA_DIA =='LType' && input$LabelFreeType == "DIA"){
mydata = read.csv(system.file("extdata/dataset.csv",
package = "MSstatsShiny"),
header = TRUE, sep = ";")
}
else if(input$BIO != "PTM" && input$DDA_DIA == "TMT"){
mydata = PDtoMSstatsTMTFormat(input = MSstatsTMT::raw.pd,
annotation = MSstatsTMT::annotation.pd,
which.proteinid = input$which.proteinid,
use_log_file = FALSE
)
}
else if (input$BIO == "PTM"){
if (input$DDA_DIA != "TMT"){
mydata = MSstatsPTM::raw.input
} else {
mydata = MSstatsPTM::raw.input.tmt
}
}
}
else if (input$BIO == 'PTM' || (input$BIO == 'PTM' && input$DDA_DIA == 'TMT')){
if (input$filetype == 'maxq') {
mydata = read.csv(input$ptm_input$datapath,sep="\t") # it needs default tab sep file
print(input$globaldata$datapath)
mydata_protein = try(read.csv(input$ptm_protein_input$datapath,sep="\t"),silent=TRUE)
if (typeof(mydata_protein)=="character"){
mydata_protein=NULL
use_unmod_peptides=TRUE
} else {
use_unmod_peptides=FALSE
}
pg_maxq_ptm = try(read.csv(input$ptm_pgroup$datapath, sep="\t"),silent=TRUE)
annotation = try(read.csv(input$ptm_annot$datapath),silent=TRUE)
if (input$BIO == "PTM" && input$DDA_DIA == "TMT"){
label = "TMT"
} else {
label = "LF"
}
mydata = MaxQtoMSstatsPTMFormat(evidence=mydata,
annotation=annotation,
input$fasta$datapath,
mod_id=input$mod_id_maxq,
evidence_prot=mydata_protein,
proteinGroups=pg_maxq_ptm,
annotation_protein=annotation,
use_unmod_peptides=use_unmod_peptides,
labeling_type=label)
mydata$PROTEIN = as.data.frame(mydata$PROTEIN)
print(mydata)
} else if(input$filetype=='phil'){
mydata = read.csv(input$ptmdata$datapath)
mydata_protein = try(read.csv(input$globaldata$datapath),silent=TRUE)
annotation = read.csv(input$annotation$datapath)
annotation_protein = try(read.csv(input$globalannotation$datapath),silent=TRUE)
mydata = FragPipetoMSstatsPTMFormat(mydata,
annotation,
mydata_protein,
annotation_protein,
mod_id_col = input$mod_id_col,
localization_cutoff = as.numeric(input$localization_cutoff),
remove_unlocalized_peptides=input$remove_unlocalized_peptides)
mydata$PTM$Condition = as.character(mydata$PTM$Condition)
mydata$PTM[mydata$PTM$Condition == "NORM", "Condition"] = "Norm"
mydata$PROTEIN$Condition = as.character(mydata$PROTEIN$Condition)
mydata$PROTEIN[mydata$PROTEIN$Condition == "NORM", "Condition"] = "Norm"
} else if (input$filetype=='PD'){
mydata = read.csv(input$ptm_input$datapath, sep = "\t")
mydata_protein = try(
read.csv(input$ptm_protein_input$datapath, sep = "\t"),
silent=TRUE)
annotation = read.csv(input$ptm_annot$datapath)
if (typeof(mydata_protein)=="character"){
mydata_protein=NULL
use_unmod_peptides=TRUE
} else {
use_unmod_peptides=FALSE
}
if (input$BIO == "PTM" && input$DDA_DIA == "TMT"){
label = "TMT"
} else {
label = "LF"
}
mydata = PDtoMSstatsPTMFormat(mydata,
annotation,
input$fasta$datapath,
protein_input=mydata_protein,
annotation_protein=annotation,
labeling_type = label,
mod_id=input$mod_id_pd,
use_unmod_peptides=use_unmod_peptides,
which_proteinid = "Master.Protein.Accessions")
} else if (input$filetype=='spec'){
mydata = read.csv(input$ptm_input$datapath)
mydata_protein = try(read.csv(input$ptm_protein_input$datapath),silent=TRUE)
annotation = read.csv(input$ptm_annot$datapath)
if (typeof(mydata_protein)=="character"){
mydata_protein=NULL
use_unmod_peptides=TRUE
} else {
use_unmod_peptides=FALSE
}
mydata = SpectronauttoMSstatsPTMFormat(mydata,
annotation = annotation,
fasta_path = input$fasta$datapath,
protein_input = mydata_protein,
annotation_protein = annotation,
use_unmod_peptides=use_unmod_peptides,
intensity = "PeakArea",
mod_id=input$mod_id_spec)
} else if (input$filetype=='sky') {
mydata = read_excel(input$ptm_input$datapath)
mydata_protein = try(read_excel(input$ptm_protein_input$datapath),silent=TRUE)
annotation = try(read.csv(input$ptm_annot$datapath),silent=TRUE)
if (typeof(mydata_protein)=="character"){
mydata_protein=NULL
use_unmod_peptides=TRUE
} else {
use_unmod_peptides=FALSE
}
if (typeof(annotation)=="character"){
annotation = NULL
}
mydata = SkylinetoMSstatsPTMFormat(mydata,
input$fasta$datapath,
annotation=annotation,
input_protein=mydata_protein,
annotation_protein=annotation,
use_unmod_peptides=use_unmod_peptides)
}else {
data = read.csv(input$msstatsptmdata$datapath, header = TRUE,sep = input$sep_msstatsptmdata,
stringsAsFactors=FALSE)
mydata = list("PTM" = data, "PROTEIN" = unmod)
}
}
else if (input$filetype == "msstats"){
mydata = read.csv(input$msstatsdata$datapath, header = TRUE,sep = input$sep_msstatsdata,
stringsAsFactors=FALSE)
}
else {
if(input$filetype=='spec' || input$filetype=='spmin'){
infile = input$data1
}
else if(input$filetype=='phil' & input$BIO != "PTM"){
mydata = read.csv(input$data$datapath, sep=input$sep_data)
}
else{
infile = input$data
}
# TODO: This code stops processing if a file is not uploaded correctly.
# ATM no error messages show and the load circle spins forever
# if(input$filetype=='maxq'){
# if(is.null(ev_maxq) || is.null(pg_maxq) || is.null(an_maxq) ) {
# return(NULL)
# }
# }
# else if(input$filetype=='ump'){
# if(is.null(raw.frag) || is.null(raw.pep) || is.null(raw.pro) || is.null(annot2)) {
# return(NULL)
# }
#
#
# } else {
# if(is.null(infile)) {
# return(NULL)
# }
# }
if(input$filetype == '10col') {
mydata = read.csv(infile$datapath, header = TRUE, sep = input$sep_data)
}
else if(input$filetype == 'sky') {
cat(file=stderr(), "Reached here in skyline\n")
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep=input$sep,
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(input$skylinedata$datapath, header = TRUE, sep = input$sep_skylinedata,
stringsAsFactors=FALSE)
# }
mydata = SkylinetoMSstatsFormat(data,
annotation = getAnnot(input),
filter_with_Qvalue = TRUE,
qvalue_cutoff = 0.01,
fewMeasurements="remove",
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)
}
else if(input$filetype == 'maxq') {
cat(file=stderr(), "Reached in maxq\n")
if(input$DDA_DIA=="TMT"){
mydata = MaxQtoMSstatsTMTFormat(evidence=ev_maxq,
annotation=an_maxq,
proteinGroups=pg_maxq,
use_log_file = FALSE)
}
else{
mydata = MaxQtoMSstatsFormat(evidence= ev_maxq, annotation= an_maxq,
proteinGroups= pg_maxq,
useUniquePeptide = TRUE,
summaryforMultipleRows = max,
removeProtein_with1Peptide=input$remove,
use_log_file = FALSE)
}
}
else if(input$filetype == 'prog') {
cat(file=stderr(), "Reached in prog\n")
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep=input$sep,
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(infile$datapath, header = TRUE, sep = input$sep_data,
stringsAsFactors=FALSE)
# }
mydata = ProgenesistoMSstatsFormat(data, annotation = getAnnot(input),
removeProtein_with1Peptide = TRUE,
use_log_file = FALSE)
colnames(mydata)[colnames(mydata) == 'PeptideModifiedSequence'] = 'PeptideSequence'
}
else if(input$filetype == 'PD') {
if(input$DDA_DIA=="TMT"){
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep=input$sep,
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(infile$datapath, header = TRUE, sep = input$sep_data,
stringsAsFactors=FALSE)
# }
mydata = PDtoMSstatsTMTFormat(input = data,
annotation = getAnnot(input),
which.proteinid = input$which.proteinid, ## same as default
use_log_file = FALSE
)
}
else{
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep=input$sep,
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(infile$datapath, header = TRUE, sep = input$sep_data,
stringsAsFactors=FALSE)
# }
mydata = PDtoMSstatsFormat(data, annotation = getAnnot(input),
removeProtein_with1Peptide = input$remove,
use_log_file = FALSE)
colnames(mydata)[colnames(mydata) == 'PeptideModifiedSequence'] = 'PeptideSequence'
}
}
else if(input$filetype == 'spec') {
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep="\t",
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(input$specdata$datapath, sep=input$sep_specdata)
# }
mydata = SpectronauttoMSstatsFormat(data,
annotation = getAnnot(input),
filter_with_Qvalue = TRUE, ## same as default
qvalue_cutoff = 0.01, ## same as default
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)
}
else if(input$filetype == 'diann') {
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep="\t",
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(input$dianndata$datapath, sep=input$sep_dianndata)
# }
print(input$dianndata$datapath)
mydata = DIANNtoMSstatsFormat(data,
annotation = getAnnot(input),
qvalue_cutoff = 0.01, ## same as default
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)
print("Mydata from mstats")
print(mydata)
}
else if(input$filetype == 'open') {
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep = input$sep,
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(infile$datapath, header = TRUE, sep = input$sep_data)
# }
mydata =OpenSWATHtoMSstatsFormat(data,
annotation = getAnnot(input),
filter_with_mscore = TRUE, ## same as default
mscore_cutoff = 0.01, ## same as default
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)
cat(file=stderr(), "Reached in openSwath\n")
}
else if(input$filetype == 'openms') {
if(input$DDA_DIA=="TMT"){
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep = input$sep,
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(infile$datapath, header = TRUE, sep = input$sep_data)
# }
mydata = OpenMStoMSstatsTMTFormat(data, use_log_file = FALSE)
}
else{
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep = input$sep,
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(infile$datapath, header = TRUE, sep = input$sep_data)
# }
unique(data[, c('Run', 'BioReplicate', 'Condition')])
mydata =OpenMStoMSstatsFormat(data,
removeProtein_with1Feature=TRUE,
use_log_file = FALSE)
}
}
else if(input$filetype == 'ump') {
mydata = DIAUmpiretoMSstatsFormat(raw.frag, raw.pep, raw.pro,
annot2,
useSelectedFrag = TRUE,
useSelectedPep = FALSE,
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)
}
else if(input$filetype == 'spmin') {
# if (input$subset){
# data = read.csv.sql(infile$datapath, sep = "\t",
# sql = "select * from file order by random() limit 100000")
# } else {
data = read.csv(infile$datapath, sep=input$sep_data)
# }
mydata = SpectroMinetoMSstatsTMTFormat(data, getAnnot(input),
use_log_file = FALSE)
}
else if(input$filetype == 'phil') {
if (input$DDA_DIA=="TMT"){
mydata = PhilosophertoMSstatsTMTFormat(input = mydata,
annotation = getAnnot(input),
use_log_file = FALSE)
mydata$Condition = as.character(mydata$Condition)
mydata[mydata$Condition == "NORM", "Condition"] = "Norm"
mydata$Condition = as.factor(mydata$Condition)
} else {
mydata = FragPipetoMSstatsFormat(input = mydata,
annotation = NULL,
use_log_file = FALSE)
}
}
}
if (input$BIO == "Peptide"){
mydata$ProteinName = mydata$PeptideSequence
}
remove_modal_spinner()
return(mydata)
}
getDataCode <- function(input) {
codes = ""
codes = paste(codes, "\n# Load Packages
library(MSstats)
library(MSstatsTMT)
library(MSstatsPTM)\n", sep = "")
codes = paste(codes, "\n# Package versions\n# MSstats version ", packageVersion("MSstats"),
"\n# MSstatsTMT version ", packageVersion("MSstatsTMT"),
"\n# MSstatsPTM version ", packageVersion("MSstatsPTM"), sep = "")
codes = paste(codes, "\n\n# Read data\n", sep = "")
if(input$filetype == 'sample') {
if(input$BIO != "PTM" && input$DDA_DIA =='LType' && input$LabelFreeType == "SRM_PRM") {
codes = paste(codes, "data = SRM_yeast\n", sep = "")
}
else if(input$BIO != "PTM" && input$DDA_DIA =='LType' && input$LabelFreeType == "DDA") {
codes = paste(codes, "data = DDARawData\n", sep = "")
}
else if(input$BIO != "PTM" && input$DDA_DIA =='LType' && input$LabelFreeType == "DIA"){
codes = paste(codes, "data = read.csv(\"dataset.csv\", header = TRUE, sep = \";\")\n", sep = "")
}
else if(input$BIO != "PTM" && input$DDA_DIA == "TMT"){
codes = paste(codes, "data = PDtoMSstatsTMTFormat(input = MSstatsTMT::raw.pd,
annotation = MSstatsTMT::annotation.pd,
which.proteinid =\'", input$which.proteinid,"\',\n\t\t\t\t ",
"use_log_file = FALSE)\n", sep = "")
} else if (input$BIO == "PTM") {
if (input$BIO == "PTM" && input$DDA_DIA == "TMT"){
codes = paste(codes, "data = MSstatsPTM::raw.input.tmt\n", sep = "")
} else if (input$BIO == "PTM" && input$DDA_DIA != "TMT"){
codes = paste(codes, "data = MSstatsPTM::raw.input\n", sep = "")
}
}
} else if (input$filetype == "msstats") {
if (input$BIO == "PTM") {
codes = paste(codes, "\nptm_data = read.csv(\"Enter PTM data file path here\", sep =",input$sep_msstatsptmdata,")\nglobal_data = read.csv(\'Enter unmod data file path here\')\ndata = list(PTM = ptm_data, PROTEIN = unmod)\n")
} else {
codes = paste(codes, "data = read.csv(\"Enter MSstats formatted data file path here\", sep=",input$sep_msstatsdata,")\n")
}
} else {
if(input$filetype == '10col') {
codes = paste(codes, "data = read.csv(\"insert your quantification dataset filepath\", header = TRUE, sep = ",input$sep_data,")\n", sep = "")
}
else if(input$filetype == 'sky') {
cat(file=stderr(), "Reached here in skyline\n")
codes = paste(codes, "data = read.csv(\"insert your MSstats report from Skyline filepath\", header = TRUE, sep = ",inputsep_skylinedata,", stringsAsFactors=F)\n", sep = "")
codes = paste(codes, "annot_file = read.csv(\"insert your annotation filepath\")\n"
, sep = "")
codes = paste(codes, "data = SkylinetoMSstatsFormat(data,
annotation = annot_file,
filter_with_Qvalue = TRUE,
qvalue_cutoff = 0.01,
fewMeasurements=\"remove\",
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)\n", sep = "")
}
else if(input$filetype == 'maxq') {
cat(file=stderr(), "Reached in maxq\n")
codes = paste(codes, "an_maxq = read.csv(\"insert your annotation filepath\")\n ev_maxq = read.table(\"insert your evidence.txt filepath\",sep=\"\t\", header=TRUE)\n pg_maxq = read.table(\"insert your proteinGroups.txt filepath\",sep=\"\t\", header=TRUE)\n"
, sep = "")
if(input$DDA_DIA=="TMT"){
codes = paste(codes, "data = MaxQtoMSstatsTMTFormat(evidence=ev_maxq,
annotation=an_maxq,
proteinGroups=\'", input$which.proteinid,"\',\n\t\t\t\t ",
"use_log_file = FALSE)\n", sep = "")
} else if (input$BIO=="PTM"){
codes = paste(codes, "sites.data = read.csv(\"insert your PTM site data filepath\")\n data = MaxQtoMSstatsPTMFormat(sites.data, an_maxq, ev_maxq, pg_maxq, an_maxq)\n",
sep="")
} else {
codes = paste(codes, "data = MaxQtoMSstatsFormat(evidence=ev_maxq,
annotation=an_maxq,
proteinGroups= pg_maxq,
useUniquePeptide = TRUE,
removeProtein_with1Peptide=", input$remove,",\n\t\t\t\t ",
"use_log_file = FALSE)\n", sep = "")
}
}
else if(input$filetype == 'prog') {
cat(file=stderr(), "Reached in prog\n")
codes = paste(codes, "data = read.csv(\"insert your quantification dataset filepath\", header = TRUE, sep = ",input$sep_data,")
annot_file = read.csv(\"insert your annotation filepath\")\n"
, sep = "")
codes = paste(codes, "data = ProgenesistoMSstatsFormat(input = data,
annotation = annot_file,
removeProtein_with1Peptide = TRUE,
use_log_file = FALSE)\n", sep = "")
codes = paste(codes, "colnames(data)[colnames(data) == \'PeptideModifiedSequence\'] = \'PeptideSequence\'\n", sep = "")
}
else if(input$filetype == 'PD') {
if(input$DDA_DIA=="TMT"){
codes = paste(codes, "data = read.delim(\"insert your quantification dataset filepath\",sep=",input$sep_data,")
annot_file = read.csv(\"insert your annotation filepath\")\n"
, sep = "")
codes = paste(codes, "data = PDtoMSstatsTMTFormat(input = data,
annotation = annot_file,
which.proteinid =\'", input$which.proteinid,"\',\n\t\t\t\t ",
"use_log_file = FALSE)\n", sep = "")
}
else{
codes = paste(codes, "data = read.delim(\"insert your quantification dataset filepath\",sep=",input$sep_data,")\nannot_file = read.csv(\"insert your annotation filepath\")\n"
, sep = "")
codes = paste(codes, "data = PDtoMSstatsFormat(data,
annotation = annot_file,
removeProtein_with1Peptide = ", input$remove,",\n\t\t\t\t ",
"use_log_file = FALSE)\n", sep = "")
codes = paste(codes, "colnames(data)[colnames(data) == \'PeptideModifiedSequence\'] = \'PeptideSequence\'\n", sep = "")
}
}
else if(input$filetype == 'spec') {
codes = paste(codes, "data = read.csv(\"insert your MSstats scheme output from Spectronaut filepath\", header = TRUE, sep = ",input$sep_specdata,")\nannot_file = read.csv(\"insert your annotation filepath\", sep='\t')#Optional\n"
, sep = "")
codes = paste(codes, "data = SpectronauttoMSstatsFormat(data,
annotation = annot_file #Optional,
filter_with_Qvalue = TRUE, ## same as default
qvalue_cutoff = 0.01, ## same as default
fewMeasurements=\"remove\",
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)\n", sep = "")
}
else if(input$filetype == 'diann') {
codes = paste(codes, "data = read.csv(\"insert your MSstats scheme output from DIANN filepath\", header = TRUE, sep = ",input$sep_dianndata,")\nannot_file = read.csv(\"insert your annotation filepath\", sep='\t')#Optional\n"
, sep = "")
codes = paste(codes, "data = DIANNtoMSstatsFormat(data,
annotation = annot_file #Optional,
qvalue_cutoff = 0.01, ## same as default
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)\n", sep = "")
}
else if(input$filetype == 'open') {
codes = paste(codes, "data = read.csv(\"insert your quantification dataset filepath\", header = TRUE, sep = ",input$sep_data,")\nannot_file = read.csv(\"insert your annotation filepath\")\n"
, sep = "")
codes = paste(codes, "data = OpenSWATHtoMSstatsFormat(data,
annotation = annot_file,
filter_with_Qvalue = TRUE, ## same as default
mscore_cutoff = 0.01, ## same as default
fewMeasurements=\"remove\",
removeProtein_with1Feature = TRUE,
use_log_file = FALSE)\n", sep = "")
}
else if(input$filetype == 'openms') {
if(input$DDA_DIA=="TMT"){
codes = paste(codes, "data = read.csv(\"insert your quantification dataset filepath\", header = TRUE, sep = ",input$sep_data,")\ndata = OpenMStoMSstatsTMTFormat(data, use_log_file = FALSE)\n"
, sep = "")
}
else{
codes = paste(codes, "data = read.csv(\"insert your quantification dataset filepath\", header = TRUE, sep = ",input$sep_data,")\nunique(data[, c('Run', 'BioReplicate', 'Condition')])\ndata = OpenMStoMSstatsFormat(data, removeProtein_with1Feature=TRUE, use_log_file = FALSE)\n"
, sep = "")
}
}
else if(input$filetype == 'spmin') {
codes = paste(codes, "data = read.csv(\"insert your quantification dataset filepath\", header = TRUE, sep = ",input$sep_data,")\nannot_file = read.csv(\"insert your annotation filepath\")\ndata = SpectroMinetoMSstatsTMTFormat(data, annot_file,
use_log_file = FALSE)"
, sep = "")
}
else if(input$filetype == 'phil' & input$DDA_DIA == "TMT") {
codes = paste(codes,"data = read.csv(\"insert your msstats filepath\",sep=",input$sep_data,")\n"
, sep = "")
codes = paste(codes,"annot_file = read.csv(\"insert your annotation filepath\")\n"
, sep = "")
codes = paste(codes, "data = PhilosophertoMSstatsTMTFormat(data,
annotation = annot_file)\n", sep = "")
}else if (input$filetype == 'phil' & input$BIO == "PTM"){
codes = paste(codes,"data = read.csv(\"insert your msstats filepath\")\n"
, sep = "")
codes = paste(codes,"annot_file = read.csv(\"insert your annotation filepath\")\n"
, sep = "")
codes = paste(codes,"data_protein = read.csv(\"insert your global profiling msstats filepath\")\n"
, sep = "")
codes = paste(codes,"annot_protein_file = read.csv(\"insert your global profiling annotation filepath\")\n"
, sep = "")
codes = paste(codes, paste0("data = PhilosophertoMSstatsPTMFormat(data,
annot_file,
data_protein,
annot_protein_file,
mod_id_col = ", input$mod_id_col, ",
localization_cutoff = ",input$localization_cutoff, ",
remove_unlocalized_peptides = ", as.character(input$remove_unlocalized_peptides),
")\n"), sep = "")
} else if (input$filetype == 'phil'){
codes = paste(codes,
"data = read.csv(\"insert your msstats.csv filepath\")\n"
, sep = "")
codes = paste(codes, paste0("data = FragPipetoMSstatsFormat(data)\n"),
sep = "")
}
}
if (input$BIO != "PTM"){
codes = paste(codes,"data = unique(as.data.frame(data))\n", sep = "")
}
return(codes)
}
getSummary1 <- function(input, df,annot_df) {
# df = getData(input)
print("+++++++++ In getSummary1 +++++++++")
# annot_df = getAnnot(input)
if (input$BIO != "PTM"){
df = as.data.frame(df)
df = df %>% filter(!Condition %in% c("Norm", "Empty"))
nf = ifelse("Fraction" %in% colnames(df),n_distinct(df$Fraction),1)
}
if(input$BIO != "PTM" && input$DDA_DIA=="TMT"){
if(is.null(annot_df)){
df1 = df %>% summarise("Number of Conditions" = n_distinct(Condition),
"Number of Biological Replicates" = n_distinct(BioReplicate),
"Number of Mixtures" = n_distinct(Mixture),
"Number of Fractions" = nf,
"Number of MS runs" = n_distinct(Run),
"Number of Technical Replicates" = n_distinct(TechRepMixture))
} else {
annot_df = annot_df %>% filter(!Condition %in% c("Norm", "Empty"))
df1 = annot_df %>% summarise("Number of Conditions" = n_distinct(Condition),
"Number of Biological Replicates" = n_distinct(BioReplicate),
"Number of Mixtures" = n_distinct(Mixture),
"Number of Fractions" = n_distinct(Fraction),
"Number of MS runs" = n_distinct(Run),
"Number of Technical Replicates" = n_distinct(TechRepMixture))
}
} else if (input$BIO == "PTM"){
ptm_df = as.data.frame(df$PTM)
unmod_df = as.data.frame(df$PROTEIN)
if ((input$BIO == "PTM" & input$DDA_DIA == "TMT") | input$filetype=='phil'){
ptm_df1 = ptm_df %>% summarise("Number of Conditions" = n_distinct(Condition),
"Number of PTM Mixtures" = n_distinct(Mixture),
"Number of PTM Biological Replicates" = n_distinct(BioReplicate),
"Number of PTM MS runs" = n_distinct(Run),
"Number of PTM Technical Replicates" = n_distinct(TechRepMixture))
unmod_df1 = unmod_df %>% summarise(
"Number of Unmod Mixtures" = n_distinct(Mixture),
"Number of Unmod Biological Replicates" = n_distinct(BioReplicate),
"Number of Unmod MS runs" = n_distinct(Run),
"Number of Unmod Technical Replicates" = n_distinct(TechRepMixture))
df = cbind(ptm_df1, unmod_df1)
} else {
ptm_df = df$PTM
unmod_df = df$PROTEIN
ptm_df1 = ptm_df %>% summarise("Number of Conditions" = n_distinct(Condition),
"Number of PTM Biological Replicates" = n_distinct(BioReplicate),
"Number of PTM MS runs" = n_distinct(Run))
unmod_df1 = unmod_df %>% summarise("Number of Unmod Conditions" = n_distinct(Condition),
"Number of Unmod Biological Replicates" = n_distinct(BioReplicate),
"Number of Unmod MS runs" = n_distinct(Run))
df = cbind(ptm_df1, unmod_df1)
}
} else {
df1 = df %>% summarise("Number of Conditions" = n_distinct(Condition),
"Number of Biological Replicates" = n_distinct(BioReplicate),
"Number of Fractions" = nf,
"Number of MS runs" = n_distinct(Run)
)
}
if (input$BIO != "PTM"){
df2 = df %>% group_by(Condition, Run) %>% summarise("Condition_Run" = n()) %>% ungroup() %>%
select("Condition_Run")
df3 = df %>% group_by(Run, BioReplicate) %>% summarise("BioReplicate_Run" = n()) %>% ungroup() %>%
select("BioReplicate_Run")
df1 = head(df1,1)
df2 = head(df2,1)
df3 = head(df3,1)
if(input$DDA_DIA !="TMT"){
df1 = cbind(df1,df2,df3) %>%
mutate("Number of Technical Replicates" = Condition_Run/(BioReplicate_Run*`Number of Fractions`) ) %>%
select(-Condition_Run,-BioReplicate_Run)
df = df1[,c(1,2,5,3,4)]
}
else{
df = df1[,c(1,2,3,6,4,5)]
}
}
t_df = as.data.frame(t(df))
rownames(t_df) = colnames(df)
t_df = cbind(rownames(t_df), t_df)
colnames(t_df) = c("", "value")
t_df$value = sub("\\.\\d+$", "", t_df$value)
colnames(t_df) = c("", "")
return(t_df)
}
getSummary2 <- function(input,df) {
# df = getData(input)
print("+++++++++ In getSummary2 +++++++++")
#print(input$PTMTMT)
if(input$BIO != "PTM" && input$DDA_DIA=="TMT"){
df = as.data.frame(df)
df = df %>% mutate("FEATURES" = paste(ProteinName, PeptideSequence, Charge,
sep = '_'))
} else if (input$BIO == "PTM" & ((input$BIO == "PTM" & input$DDA_DIA == "TMT" )| input$filetype=='phil')){
df_ptm = as.data.frame(df$PTM) %>% mutate("FEATURES" = paste(ProteinName, PeptideSequence,
Charge, sep = '_'))
df_prot = as.data.frame(df$PROTEIN) %>% mutate("FEATURES" = paste(ProteinName,
PeptideSequence,
Charge, sep = '_'))
} else if (input$BIO == "PTM" & (input$BIO == "PTM" & input$DDA_DIA != "TMT" )){
df_ptm = as.data.frame(df$PTM) %>% mutate("FEATURES" = paste(PeptideSequence,
PrecursorCharge,
FragmentIon,
ProductCharge, sep = '_'))
df_prot = as.data.frame(df$PROTEIN) %>% mutate("FEATURES" = paste(PeptideSequence,
PrecursorCharge,
FragmentIon,
ProductCharge,
sep = '_'))
} else {
df = as.data.frame(df)
df = df %>% mutate("FEATURES" = paste(PeptideSequence, PrecursorCharge,
FragmentIon, ProductCharge,
sep = '_'))
}
if (input$BIO != "PTM"){
df1 = df %>% summarise("Number of Proteins" = n_distinct(ProteinName),
"Number of Peptides" = n_distinct(PeptideSequence),
"Number of Features" = n_distinct(FEATURES),
"Min_Intensity" = ifelse(!is.finite(min(Intensity, na.rm=TRUE)),0,round(min(Intensity, na.rm=TRUE),0)),
"Max_Intensity" = ifelse(!is.finite(max(Intensity, na.rm=TRUE)),0,
round(max(Intensity, na.rm=TRUE),0))) %>%
unite("Intensity Range", Min_Intensity:Max_Intensity, sep = " - ")
Peptides_Proteins = df %>% group_by(ProteinName) %>%
summarise(npep = n_distinct(PeptideSequence)) %>% summarise(Peptides_Proteins_min=min(npep),
Peptides_Proteins_max=max(npep))
Features_Peptides = df %>% group_by(PeptideSequence) %>%
summarise(nfea = n_distinct(FEATURES)) %>% summarise(Features_Peptides_min=min(nfea),
Features_Peptides_max=max(nfea))
df1 = cbind(df1,Features_Peptides,Peptides_Proteins) %>%
unite("Number of Features/Peptide",Features_Peptides_min:Features_Peptides_max,sep = " - ") %>%
unite("Number of Peptides/Protein",Peptides_Proteins_min:Peptides_Proteins_max, sep = " - ")
df1 = df1[,c(1,2,3,6,5,4)]
} else {
df_ptm1 = as.data.frame(df_ptm) %>% summarise("Number of PTMs" = n_distinct(ProteinName),
"Number of PTM Features" = n_distinct(FEATURES),
"Number of Features/PTM" = as.numeric(n_distinct(FEATURES) / n_distinct(PeptideSequence)),
"Min_Intensity" = ifelse(!is.finite(
min(Intensity, na.rm=TRUE)), 0,
round(min(Intensity, na.rm=TRUE),0)),
"Max_Intensity" = ifelse(!is.finite(
max(Intensity, na.rm=TRUE)), 0,
round(max(Intensity, na.rm=TRUE),0))) %>%
unite("PTM Intensity Range", Min_Intensity:Max_Intensity, sep = " - ")
# df_ptm1 = df_ptm1 %>% select(!Min_Intensity, !Max_Intensity)
df_prot1 = as.data.frame(df_prot) %>% summarise("Number of Unmod Proteins" = n_distinct(ProteinName),
"Number of Protein Peptides" = n_distinct(PeptideSequence),
"Number of Protein Features" = n_distinct(FEATURES),
"Number of Features/Peptide" = as.numeric(n_distinct(FEATURES) / n_distinct(PeptideSequence)),
"Number of Peptides/Protein" = as.numeric(n_distinct(PeptideSequence) / n_distinct(ProteinName)),
"Min_Intensity" = ifelse(!is.finite(
min(Intensity, na.rm=TRUE)), 0,
round(min(Intensity, na.rm=TRUE),0)),
"Max_Intensity" = ifelse(!is.finite(
max(Intensity, na.rm=TRUE)), 0,
round(max(Intensity, na.rm=TRUE),0))) %>%
unite("Protein Intensity Range", Min_Intensity:Max_Intensity, sep = " - ")
df1 = cbind(df_ptm1, df_prot1)
}
t_df = as.data.frame(t(df1))
rownames(t_df) = colnames(df1)
t_df = cbind(rownames(t_df), t_df)
colnames(t_df) = c("", "value")
colnames(t_df) = c("", "")
return(t_df)
}
# qc server functions
preprocessData <- function(qc_input,loadpage_input,input_data ) {
print("+++++++++ In preprocessData +++++++++")
# validate(need(getData(loadpage_input),
# message = "PLEASE UPLOAD DATASET OR SELECT SAMPLE"))
#
# ## Preprocess input for loop
#
# input_data = getData(loadpage_input)
validate(need(input_data,
message = "PLEASE UPLOAD DATASET OR SELECT SAMPLE"))
## Preprocess input for loop
preprocess_list = list()
MSstatsLogsSettings(FALSE)
## Here we run the underlying functions for MSstats and MSstatsTMT
## summarization. Done so we can loop over proteins and create a progress bar
if(loadpage_input$BIO == "PTM" & ((loadpage_input$BIO == "PTM" & loadpage_input$DDA_DIA == "TMT" ) | loadpage_input$filetype=='phil')){
preprocessed_ptm = MSstatsShiny::tmt_summarization_loop(input_data$PTM, qc_input,loadpage_input)
preprocessed_unmod = MSstatsShiny::tmt_summarization_loop(input_data$PROTEIN, qc_input,loadpage_input)
preprocessed = list(PTM = preprocessed_ptm, PROTEIN = preprocessed_unmod)
} else if (loadpage_input$BIO == "PTM" & (loadpage_input$BIO == "PTM" & loadpage_input$DDA_DIA != "TMT" )){
preprocessed_ptm = MSstatsShiny::lf_summarization_loop(input_data$PTM, qc_input, loadpage_input)
preprocessed_unmod = MSstatsShiny::lf_summarization_loop(input_data$PROTEIN, qc_input, loadpage_input)
preprocessed = list(PTM = preprocessed_ptm, PROTEIN = preprocessed_unmod)
} else if(loadpage_input$DDA_DIA == "TMT"){
## Run MSstatsTMT summarization
preprocessed = MSstatsShiny::tmt_summarization_loop(input_data, qc_input,loadpage_input)
} else {
## Run LF MSstats summarization
preprocessed = MSstatsShiny::lf_summarization_loop(input_data, qc_input, loadpage_input)
}
return(preprocessed)
}
preprocessDataCode <- function(qc_input,loadpage_input) {
codes = getDataCode(loadpage_input)
if(loadpage_input$DDA_DIA == "TMT"){
codes = paste(codes, "\n# use MSstats for protein summarization\n", sep = "")
codes = paste(codes, "summarized = MSstatsTMT::proteinSummarization(data,
method = '",qc_input$summarization,"\',\t\t\t\t
global_norm = ", qc_input$global_norm,",\t\t\t\t
reference_norm = ", qc_input$reference_norm,",\t\t\t\t
remove_norm_channel = ", qc_input$remove_norm_channel,",\t\t\t\t
remove_empty_channel = TRUE, \t\t\t\t
MBimpute = FALSE, \t\t\t\t
maxQuantileforCensored = ", qc_input$maxQC1,")\n", sep = "")
codes = paste(codes, "\n# use to create data summarization plots\n", sep = "")
codes = paste(codes, "dataProcessPlotsTMT(summarized,
type= \"Enter ProfilePlot or QCPlot Here\",
ylimUp = FALSE,
ylimDown = FALSE,
which.Protein = \"Enter Protein to Plot Here\",
originalPlot = TRUE,
summaryPlot =", qc_input$summ,",\t\t\t\t
address = FALSE)\n", sep="")
} else if (loadpage_input$BIO == "PTM"){
if ((loadpage_input$BIO == "PTM" & loadpage_input$DDA_DIA == "TMT" ) | loadpage_input$filetype=='phil'){
codes = paste(codes, "\n# use MSstats for protein summarization\n", sep = "")
codes = paste(codes, "summarized = MSstatsPTM::dataSummarizationPTM_TMT(data,
method = '",qc_input$summarization,"\',\t\t\t\t
global_norm.PTM = ", qc_input$global_norm,",\t\t\t\t
reference_norm.PTM = ", qc_input$reference_norm,",\t\t\t\t
remove_norm_channel = ", qc_input$remove_norm_channel,",\t\t\t\t
remove_empty_channel = TRUE, \t\t\t\t
MBimpute.PTM = FALSE, \t\t\t\t
maxQuantileforCensored = ", qc_input$maxQC1,")\n", sep = "")
} else{
codes = paste(codes, "\n# use MSstats for protein summarization\n", sep = "")
codes = paste(codes, "summarized = MSstatsPTM::dataSummarizationPTM(data,
normalization.PTM = \'", qc_input$norm,"\',\t\t\t\t
logTrans = ", as.numeric(qc_input$log),",\t\t\t\t
nameStandards = ", paste0("c('", paste(qc_input$names, collapse = "', '"), "')"), ",\t\t\t\t
featureSubset = \'", qc_input$features_used, "\',\t\t\t\t
n_top_feature = ", code_n_feat, ",\t\t\t\t
summaryMethod=\"TMP\",
censoredInt=\'", qc_input$censInt, "\',\t\t\t\t
MBimpute.PTM=", qc_input$MBi, ",\t\t\t\t
remove50missing=", qc_input$remove50, ",\t\t\t\t
maxQuantileforCensored=", qc_input$maxQC, ")\n", sep = "")
}
codes = paste(codes, "\n# use to create data summarization plots\n", sep = "")
codes = paste(codes, "dataProcessPlotsPTM(summarized,
type= \"Enter ProfilePlot or QCPlot Here\",
ylimUp = FALSE,
ylimDown = FALSE,
which.PTM = \"Enter PTM to Plot Here\",
originalPlot = TRUE,
summaryPlot =", qc_input$summ,",\t\t\t\t
address = FALSE)\n", sep="")
}
else{
if (qc_input$features_used == "all"){
code_n_feat = 'NULL'
} else if (qc_input$features_used == "topN") {
code_n_feat = qc_input$n_feat
} else {
code_n_feat = 'NULL'
}
codes = paste(codes, "\n# use MSstats for protein summarization\n", sep = "")
codes = paste(codes, "summarized = MSstats::dataProcess(data,
normalization = \'", qc_input$norm,"\',\t\t\t\t
logTrans = ", as.numeric(qc_input$log),",\t\t\t\t
nameStandards = ", paste0("c('", paste(qc_input$names, collapse = "', '"), "')"), ",\t\t\t\t
featureSubset = \'", qc_input$features_used, "\',\t\t\t\t
n_top_feature = ", code_n_feat, ",\t\t\t\t
summaryMethod=\"TMP\",
censoredInt=\'", qc_input$censInt, "\',\t\t\t\t
MBimpute=", qc_input$MBi, ",\t\t\t\t
remove50missing=", qc_input$remove50, ",\t\t\t\t
maxQuantileforCensored=", qc_input$maxQC, ")\n", sep = "")
codes = paste(codes, "dataProcessPlots(data=summarized,
type=\"Enter ProfilePlot or QCPlot Here\",
ylimUp = FALSE,
ylimDown = FALSE,
which.Protein = \"Enter Protein to Plot Here\",
summaryPlot = TRUE,
address = FALSE)\n", sep="")
}
return(codes)
}
# statmodel server functions
dataComparison <- function(statmodel_input,qc_input,loadpage_input,matrix,input_data) {
print("+++++++++ In Data Comparison +++++++++")
# input_data = preprocessData(qc_input,loadpage_input,get_data())
contrast.matrix = matrix
if (loadpage_input$BIO == "PTM" & ((loadpage_input$BIO == "PTM" & loadpage_input$DDA_DIA == "TMT" ) | loadpage_input$filetype=='phil')){
model_ptm = MSstatsShiny::tmt_model(input_data$PTM, statmodel_input, contrast.matrix)
model_protein = MSstatsShiny::tmt_model(input_data$PROTEIN, statmodel_input, contrast.matrix)
model_adj = MSstatsShiny::apply_adj(model_ptm$ComparisonResult,
model_protein$ComparisonResult)
model = list('PTM.Model' = model_ptm$ComparisonResult,
'PROTEIN.Model' = model_protein$ComparisonResult,
'ADJUSTED.Model' = model_adj)
} else if(loadpage_input$BIO == "PTM" & (loadpage_input$BIO == "PTM" & loadpage_input$DDA_DIA != "TMT" )){
model_ptm = MSstatsShiny::lf_model(input_data$PTM, contrast.matrix)
model_protein = MSstatsShiny::lf_model(input_data$PROTEIN, contrast.matrix)
model_adj = MSstatsShiny::apply_adj(model_ptm$ComparisonResult,
model_protein$ComparisonResult)
model = list('PTM.Model' = model_ptm$ComparisonResult,
'PROTEIN.Model' = model_protein$ComparisonResult,
'ADJUSTED.Model' = model_adj)
} else if(loadpage_input$DDA_DIA=="TMT"){
model = MSstatsShiny::tmt_model(input_data, statmodel_input, contrast.matrix)
}
else{
model = MSstatsShiny::lf_model(input_data, contrast.matrix)
}
return(model)
}
