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#!/usr/bin/env nextflow
/*
========================================================================================
nf-core/diaproteomics
========================================================================================
nf-core/diaproteomics Analysis Pipeline.
#### Homepage / Documentation
https://github.com/nf-core/diaproteomics
----------------------------------------------------------------------------------------
*/
log.info Headers.nf_core(workflow, params.monochrome_logs)
////////////////////////////////////////////////////
/* -- PRINT HELP -- */
////////////////////////////////////////////////////+
def json_schema = "$projectDir/nextflow_schema.json"
if (params.help) {
def command = "nextflow run nf-core/diaproteomics --input sample_sheet.tsv --input_spectral_library library_sheet.tsv --irts irt_sheet.tsv"
log.info NfcoreSchema.params_help(workflow, params, json_schema, command)
exit 0
}
////////////////////////////////////////////////////
/* -- VALIDATE PARAMETERS -- */
////////////////////////////////////////////////////+
if (params.validate_params) {
NfcoreSchema.validateParameters(params, json_schema, log)
}
////////////////////////////////////////////////////
/* -- Collect configuration parameters -- */
////////////////////////////////////////////////////
// Check AWS batch settings
if (workflow.profile.contains('awsbatch')) {
// AWSBatch sanity checking
if (!params.awsqueue || !params.awsregion) exit 1, 'Specify correct --awsqueue and --awsregion parameters on AWSBatch!'
// Check outdir paths to be S3 buckets if running on AWSBatch
// related: https://github.com/nextflow-io/nextflow/issues/813
if (!params.outdir.startsWith('s3:')) exit 1, 'Outdir not on S3 - specify S3 Bucket to run on AWSBatch!'
// Prevent trace files to be stored on S3 since S3 does not support rolling files.
if (params.tracedir.startsWith('s3:')) exit 1, 'Specify a local tracedir or run without trace! S3 cannot be used for tracefiles.'
}
// Stage config files
projectDir=workflow.projectDir
ch_output_docs = file("$projectDir/docs/output.md", checkIfExists: true)
ch_output_docs_images = file("$projectDir/docs/images/", checkIfExists: true)
sample_sheet = file(params.input)
Channel
.from( sample_sheet )
.into { input_exp_design; input_exp_design_mztab}
params.outdir = params.outdir ?: { log.warn "No output directory provided. Will put the results into './results'"; return "./results" }()
// DIA MS input
Channel
.from( sample_sheet )
.splitCsv(header: true, sep:'\t')
.map { col -> tuple("${col.Sample}", "${col.BatchID}", "${col.MSstats_Condition}", file("${col.Spectra_Filepath}", checkifExists: true))}
.flatMap{it -> [tuple(it[0],it[1].toString(),it[2],it[3])]}
.into { input_branch; check_dia }
// Check file extension
def hasExtension(it, extension) {
it.toString().toLowerCase().endsWith(extension.toLowerCase())
}
input_branch.branch {
raw: hasExtension(it[3], 'raw')
mzml: hasExtension(it[3], 'mzml')
mzxml: hasExtension(it[3], 'mzxml')
other: true
}.set{input_dia_ms_files}
input_dia_ms_files.other.subscribe { row -> log.warn("unknown format for entry " + row[3] + " in provided sample sheet. ignoring line."); exit 1 }
check_dia_n = check_dia.map{it[1]}.unique().toList().size().val
// Validate inputs
sample_sheet = file(params.input)
if( params.irts_from_outer_quantiles){
quant_flag = '--quantiles True'
} else {
quant_flag = ''
}
if( params.align_libraries) {
align_flag = '--align True'
} else {
align_flag = ''
}
/*
* Create a channel for input spectral library
*/
if( params.generate_spectral_library) {
// Spectral library input
dda_sheet = file(params.input_sheet_dda)
Channel.from( dda_sheet )
.splitCsv(header: true, sep:'\t')
.map { col -> tuple("${col.Sample}", "${col.BatchID}", file("${col.Spectra_Filepath}", checkifExists: true), file("${col.Id_Filepath}", checkifExists: true))}
.flatMap{it -> [tuple(it[0],it[1],it[2],it[3])]}
.into {input_dda;input_check;input_check_samples}
check_n = input_check.toList().size().val
check_n_sample = input_check_samples.map{it[1]}.unique().toList().size().val
if ((check_n > 1) & (check_n != check_n_sample) & (!params.merge_libraries)) {
print('You specified multiple DDA files to generate spectral libraries, but library merging is not set \n')
print('Set --merge_libraries and possibly --align_libraries to align them in the same RT space \n')
exit 1
}
if ((check_n_sample != check_dia_n)) {
print('The number of batches in the sample input does not match the number of batches of the spectral library input \n')
exit 1
}
input_dda.branch {
raw: hasExtension(it[2], 'raw')
mzml: hasExtension(it[2], 'mzML')
mzxml: hasExtension(it[2], 'mzXML')
other: true
}.set{input_dda_ms_files}
Channel
.fromPath( params.unimod )
.ifEmpty { exit 1, "params.unimod was empty - no unimod.xml supplied" }
.set { input_unimod }
input_lib = Channel.empty()
input_lib_1 = Channel.empty()
input_lib_nd = Channel.empty()
input_lib_nd_1 = Channel.empty()
params.input_spectral_library = "generate spectral library from DDA data"
} else if( params.skip_decoy_generation) {
// Spectral library input
library_sheet = file(params.input_spectral_library)
Channel.from( library_sheet )
.splitCsv(header: true, sep:'\t')
.map { col -> tuple("${col.Sample}", "${col.BatchID}", file("${col.Library_Filepath}", checkifExists: true))}
.flatMap{ it -> [tuple(it[0],it[1],it[2])] }
.into { input_lib_nd; check_decoy; check_decoy_2 }
check_decoy_n2 = check_decoy_2.toList().size().val
check_decoy_n = check_decoy.map{it[1]}.unique().toList().size().val
if ((check_decoy_n > 1) & (check_decoy_n2 != check_decoy_n)) {
print('You specified multiple spectral libraries including decoys for one batch \n')
print('This is not possible yet, merging and aligning would be possible without decoys\n')
exit 1
}
if ((check_decoy_n != check_dia_n)) {
print('The number of batches in the sample input do not match the number of batches of the spectral library input \n')
exit 1
}
input_lib = Channel.empty()
input_lib_1 = Channel.empty()
input_dda = Channel.empty()
input_dda.branch {
raw: hasExtension(it[2], 'raw')
mzml: hasExtension(it[2], 'mzML')
mzxml: hasExtension(it[2], 'mzXML')
other: true
}.set { input_dda_ms_files }
input_unimod = Channel.empty()
} else {
// Spectral library input
library_sheet = file(params.input_spectral_library)
Channel.from( library_sheet )
.splitCsv(header: true, sep:'\t')
.map { col -> tuple("${col.Sample}", "${col.BatchID}", file("${col.Library_Filepath}", checkifExists: true))}
.flatMap{it -> [tuple(it[0],it[1],it[2])]}
.into {input_lib; input_lib_1; input_lib_n; input_lib_n2 }
check_lib_n2 = input_lib_n2.toList().size().val
check_lib_n = input_lib_n.map{it[1]}.unique().toList().size().val
if ((check_lib_n > 1) & (check_lib_n2 != check_lib_n) & (!params.merge_libraries)) {
print('You specified multiple spectral libraries for one batch \n')
print('Set --merge_libraries and possibly --align_libraries to align them in the same RT space \n')
exit 1
}
if ((check_lib_n != check_dia_n)) {
print('The number of batches in the sample input does not match the number of batches of the spectral library input \n')
exit 1
}
input_lib_nd = Channel.empty()
input_lib_nd_1 = Channel.empty()
input_dda = Channel.empty()
input_dda.branch {
raw: hasExtension(it[2], 'raw')
mzml: hasExtension(it[2], 'mzML')
mzxml: hasExtension(it[2], 'mzXML')
other: true
}.set { input_dda_ms_files }
input_unimod = Channel.empty()
}
if( !params.generate_pseudo_irts){
// iRT library input
irt_sheet = file(params.irts)
Channel.from( irt_sheet )
.splitCsv(header: true, sep:'\t')
.map { col -> tuple("${col.BatchID}", file("${col.irt_Filepath}", checkifExists: true))}
.flatMap{it -> [tuple(it[0],it[1])]}
.into {input_irts; input_irts_check; input_irts_check_2}
check_irts_n = input_irts_check.toList().size().val
check_irts_n2 = input_irts_check_2.map{it[0]}.unique().toList().size().val
if ((check_irts_n > 1) & (check_irts_n != check_irts_n2)) {
print('You specified multiple DDA files to generate spectral libraries, but library merging is not set \n')
print('Set --merge_libraries and possibly --align_libraries to align them in the same RT space \n')
exit 1
}
if ((check_irts_n2 != check_dia_n)) {
print('The number of batches in the sample input does not match the number of batches of the irt input \n')
exit 1
}
} else {
input_irts = Channel.empty()
}
// MS1 option
if (params.use_ms1){
ms1_option = '-use_ms1_traces'
ms1_scoring = '-Scoring:Scores:use_ms1_mi'
ms1_mi = '-Scoring:Scores:use_mi_score'
} else {
ms1_option = ''
ms1_scoring = ''
ms1_mi = ''
}
// Force option
if (params.force_option){
force_option = '-force'
} else {
force_option = ''
}
// DIAlignR multithreading
if (params.dialignr_parallelization){
dialignr_parallel='parallel'
} else {
dialignr_parallel=''
}
////////////////////////////////////////////////////
/* -- PRINT PARAMETER SUMMARY -- */
////////////////////////////////////////////////////
log.info NfcoreSchema.params_summary_log(workflow, params, json_schema)
// Header log info
def summary = [:]
if (workflow.revision) summary['Pipeline Release'] = workflow.revision
summary['Spectral Library'] = params.input_spectral_library
summary['Run Name'] = workflow.runName
summary['Input'] = params.input
summary['Max Resources'] = "$params.max_memory memory, $params.max_cpus cpus, $params.max_time time per job"
if (workflow.containerEngine) summary['Container'] = "$workflow.containerEngine - $workflow.container"
summary['Output dir'] = params.outdir
summary['Launch dir'] = workflow.launchDir
summary['Working dir'] = workflow.workDir
summary['Script dir'] = workflow.projectDir
summary['User'] = workflow.userName
if (workflow.profile.contains('awsbatch')) {
summary['AWS Region'] = params.awsregion
summary['AWS Queue'] = params.awsqueue
summary['AWS CLI'] = params.awscli
}
summary['Config Profile'] = workflow.profile
if (params.config_profile_description) summary['Config Profile Description'] = params.config_profile_description
if (params.config_profile_contact) summary['Config Profile Contact'] = params.config_profile_contact
if (params.config_profile_url) summary['Config Profile URL'] = params.config_profile_url
summary['Config Files'] = workflow.configFiles.join(', ')
if (params.email || params.email_on_fail) {
summary['E-mail Address'] = params.email
summary['E-mail on failure'] = params.email_on_fail
}
// Check the hostnames against configured profiles
checkHostname()
Channel.from(summary.collect{ [it.key, it.value] })
.map { k,v -> "<dt>$k</dt><dd><samp>${v ?: '<span style=\"color:#999999;\">N/A</a>'}</samp></dd>" }
.reduce { a, b -> return [a, b].join("\n ") }
.map { x -> """
id: 'nf-core-diaproteomics-summary'
description: " - this information is collected when the pipeline is started."
section_name: 'nf-core/diaproteomics Workflow Summary'
section_href: 'https://github.com/nf-core/diaproteomics'
plot_type: 'html'
data: |
<dl class=\"dl-horizontal\">
$x
</dl>
""".stripIndent() }
.set { ch_workflow_summary }
/*
* Parse software version numbers
*/
process get_software_versions {
publishDir "${params.outdir}/pipeline_info", mode: params.publish_dir_mode,
saveAs: { filename ->
if (filename.indexOf('.csv') > 0) filename
else null
}
output:
file 'software_versions_mqc.yaml' into ch_software_versions_yaml
file 'software_versions.csv'
script:
"""
echo $workflow.manifest.version > v_pipeline.txt
echo $workflow.nextflow.version > v_nextflow.txt
FileInfo --help &> v_openms.txt
pyprophet --version &> v_pyprophet.txt
scrape_software_versions.py &> software_versions_mqc.yaml
"""
}
/*
* STEP 0 - Raw File Conversion
*/
process dda_raw_file_conversion {
input:
set val(id), val(sample), file(raw_file), file(dda_id_file) from input_dda_ms_files.raw
output:
set val(id), val(sample), file("${raw_file.baseName}.mzML"), file(dda_id_file) into converted_dda_input_mzmls
when:
params.generate_spectral_library
script:
"""
ThermoRawFileParser.sh -i=${raw_file} -f=2 -b=${raw_file.baseName}.mzML
"""
}
/*
* STEP 1 - Convert IDs for Spectral Library Generation using EasyPQP
*/
process dda_id_format_conversion {
input:
set val(id), val(sample), file(dda_mzml), file(dda_id_file) from input_dda_ms_files.mzml.mix(input_dda_ms_files.mzxml).mix(converted_dda_input_mzmls)
output:
set val(id), val(sample), file(dda_mzml), file("${id}_${sample}_peptide_ids.idXML") into input_dda_converted
when:
params.generate_spectral_library
script:
"""
IDFileConverter -in ${dda_id_file} -out ${id}_${sample}_peptide_ids.idXML -threads ${task.cpus}
"""
}
/*
* STEP 2 - Spectral Library Generation using EasyPQP
*/
process dda_library_generation {
input:
set val(id), val(sample), file(dda_mzml_file), file(idxml_file) from input_dda_converted
file unimod_file from input_unimod.first()
output:
set val(id), val(sample), file("${id}_${sample}_library.tsv") into input_lib_dda_nd
when:
params.generate_spectral_library
script:
"""
easypqp convert \\
--unimod ${unimod_file} \\
--pepxml ${idxml_file} \\
--spectra ${dda_mzml_file}
easypqp library \\
--out ${dda_mzml_file.baseName}_run_peaks.tsv \\
--rt_psm_fdr_threshold ${params.library_rt_fdr} \\
--nofdr \\
${dda_mzml_file.baseName}.psmpkl \\
${dda_mzml_file.baseName}.peakpkl
mv ${dda_mzml_file.baseName}_run_peaks.tsv ${id}_${sample}_library.tsv
"""
}
/*
* STEP 3 - Assay Generation for Spectral Library
*/
process assay_generation {
input:
set val(id), val(sample), file(lib_file_na) from input_lib.mix(input_lib_dda_nd)
output:
set val(id), val(sample), file("${id}_${sample}_assay.tsv") into (input_lib_assay, input_lib_assay_for_irt, input_lib_assay_for_merging)
when:
!params.skip_decoy_generation
script:
"""
TargetedFileConverter \\
-in ${lib_file_na} \\
-out ${lib_file_na.baseName}.tsv \\
-threads ${task.cpus}
OpenSwathAssayGenerator \\
-in ${lib_file_na.baseName}.tsv \\
-min_transitions ${params.min_transitions} \\
-max_transitions ${params.max_transitions} \\
-out ${id}_${sample}_assay.tsv \\
-threads ${task.cpus}
"""
}
if(params.merge_libraries) {
input_lib_assay = Channel.empty()
input_lib_assay_for_irt = Channel.empty()
}
/*
* STEP 4 - Merge and align spectral Libraries
*/
process library_merging_and_alignment {
publishDir "${params.outdir}/spectral_library_files"
input:
set val(id), val(sample), file(lib_files_for_merging) from input_lib_assay_for_merging.groupTuple(by:1)
output:
set val(id), val(sample), file("${sample}_library_merged.tsv") into (input_lib_assay_merged, input_lib_assay_merged_for_irt)
set val(id), val(sample), file("*.png") optional true
when:
params.merge_libraries
script:
"""
merge_and_align_libraries_from_easypqp.py \\
--input_libraries ${lib_files_for_merging} \\
--min_overlap ${params.min_overlap_for_merging} \\
--rsq_threshold 0.75 \\
--output ${sample}_library_merged.tsv \\
${align_flag}
"""
}
/*
* STEP 5 - Pseudo iRT Library Generation
*/
process pseudo_irt_generation {
publishDir "${params.outdir}/spectral_library_files"
input:
set val(id), val(sample), file(lib_file_assay_irt) from input_lib_assay_for_irt.mix(input_lib_assay_merged_for_irt)
output:
set val(sample), file("${lib_file_assay_irt.baseName}_pseudo_irts.pqp") into input_lib_assay_irt_2
when:
params.generate_pseudo_irts
script:
"""
select_pseudo_irts_from_lib.py \\
--input_libraries ${lib_file_assay_irt} \\
--min_rt 0 \\
--n_irts ${params.n_irts} \\
--max_rt 100 \\
--output ${lib_file_assay_irt.baseName}_pseudo_irts.tsv \\
${quant_flag}
TargetedFileConverter \\
-in ${lib_file_assay_irt.baseName}_pseudo_irts.tsv \\
-out ${lib_file_assay_irt.baseName}_pseudo_irts.pqp \\
-threads ${task.cpus}
"""
}
/*
* STEP 6 - Decoy Generation for Spectral Library
*/
process decoy_generation {
publishDir "${params.outdir}/spectral_library_files"
input:
set val(id), val(sample), file(lib_file_nd) from input_lib_assay.mix(input_lib_assay_merged)
output:
set val(id), val(sample), file("${lib_file_nd.baseName}_decoy.pqp") into input_lib_decoy
when:
!params.skip_decoy_generation
script:
"""
TargetedFileConverter \\
-in ${lib_file_nd} \\
-out ${lib_file_nd.baseName}.pqp \\
-threads ${task.cpus}
OpenSwathDecoyGenerator \\
-in ${lib_file_nd.baseName}.pqp \\
-method ${params.decoy_method} \\
-out ${lib_file_nd.baseName}_decoy.pqp \\
-threads ${task.cpus}
"""
}
/*
* STEP 7 - DIA Raw File Conversion
*/
process dia_raw_file_conversion {
input:
set val(id), val(sample), val(condition), file(raw_file) from input_dia_ms_files.raw
output:
set val(id), val(sample), val(condition), file("${raw_file.baseName}.mzML") into converted_dia_input_mzmls
when:
!params.skip_dia_processing
script:
"""
ThermoRawFileParser.sh -i=${raw_file} -f=2 -b=${raw_file.baseName}.mzML
"""
}
/*
* STEP 8 - DIA library search with OpenSwathWorkFlow
*/
process dia_spectral_library_search {
publishDir "${params.outdir}/openswathworkflow_output"
label 'process_medium'
input:
set val(sample), val(id), val(condition), file(mzml_file), val(dummy_id), file(lib_file), file(irt_file) from converted_dia_input_mzmls.mix(input_dia_ms_files.mzml.mix(input_dia_ms_files.mzxml)).combine(input_lib_decoy.mix(input_lib_nd), by:1).combine(input_irts.mix(input_lib_assay_irt_2), by:0)
output:
set val(id), val(sample), val(condition), file("${mzml_file.baseName}_chrom.mzML") into chromatogram_files
set val(id), val(sample), val(condition), file("${mzml_file.baseName}.osw") into osw_files
set val(id), val(sample), file("${lib_file.baseName}.pqp") into (input_lib_used, input_lib_used_I, input_lib_used_I_mztab)
when:
!params.skip_dia_processing
script:
"""
mkdir tmp
TargetedFileConverter \\
-in ${lib_file} \\
-out ${lib_file.baseName}.pqp \\
-threads ${task.cpus}
TargetedFileConverter \\
-in ${irt_file} \\
-out ${irt_file.baseName}.pqp \\
-threads ${task.cpus}
OpenSwathWorkflow \\
-in ${mzml_file} \\
-tr ${lib_file.baseName}.pqp \\
-sort_swath_maps \\
-tr_irt ${irt_file.baseName}.pqp \\
-min_rsq ${params.irt_min_rsq} \\
-out_osw ${mzml_file.baseName}.osw \\
-out_chrom ${mzml_file.baseName}_chrom.mzML \\
-mz_extraction_window ${params.mz_extraction_window} \\
-mz_extraction_window_ms1 ${params.mz_extraction_window_ms1} \\
-mz_extraction_window_unit ${params.mz_extraction_window_unit} \\
-mz_extraction_window_ms1_unit ${params.mz_extraction_window_ms1_unit} \\
-rt_extraction_window ${params.rt_extraction_window} \\
-min_upper_edge_dist ${params.min_upper_edge_dist} \\
-RTNormalization:alignmentMethod ${params.irt_alignment_method} \\
-RTNormalization:estimateBestPeptides \\
-RTNormalization:outlierMethod none \\
-RTNormalization:NrRTBins ${params.irt_n_bins} \\
-RTNormalization:MinBinsFilled ${params.irt_min_bins_covered} \\
-mz_correction_function quadratic_regression_delta_ppm \\
-Scoring:stop_report_after_feature 5 \\
-Scoring:TransitionGroupPicker:compute_peak_quality false \\
-Scoring:TransitionGroupPicker:peak_integration 'original' \\
-Scoring:TransitionGroupPicker:background_subtraction 'none' \\
-Scoring:TransitionGroupPicker:PeakPickerMRM:sgolay_frame_length 11 \\
-Scoring:TransitionGroupPicker:PeakPickerMRM:sgolay_polynomial_order 3 \\
-Scoring:TransitionGroupPicker:PeakPickerMRM:gauss_width 30 \\
-Scoring:TransitionGroupPicker:PeakPickerMRM:use_gauss 'false' \\
-Scoring:TransitionGroupPicker:PeakIntegrator:integration_type 'intensity_sum' \\
-Scoring:TransitionGroupPicker:PeakIntegrator:baseline_type 'base_to_base' \\
-Scoring:TransitionGroupPicker:PeakIntegrator:fit_EMG 'false' \\
-batchSize 1000 \\
-readOptions ${params.cache_option} \\
-tempDirectory tmp \\
-Scoring:DIAScoring:dia_nr_isotopes 3 \\
-enable_uis_scoring \\
-Scoring:uis_threshold_sn -1 \\
-threads ${task.cpus} \\
${force_option} ${ms1_option} ${ms1_scoring} ${ms1_mi}
"""
}
/*
* STEP 9 - Pyprophet merging of OpenSwath results
*/
process dia_search_output_merging {
input:
set val(sample), val(id), val(condition), file(all_osws), val(dummy_id), file(lib_file_template) from osw_files.groupTuple(by:1).join(input_lib_used, by:1)
output:
set val(id), val(sample), val(condition), file("${sample}_osw_file_merged.osw") into merged_osw_file_for_global
when:
!params.skip_dia_processing
script:
"""
pyprophet merge \\
--template=${lib_file_template} \\
--out=${sample}_osw_file_merged.osw \\
--no-same_run \\
${all_osws}
"""
}
/*
* STEP 10 - Pyprophet global FDR Scoring
*/
process global_false_discovery_rate_estimation {
publishDir "${params.outdir}/pyprophet_output"
label 'process_high_mem'
input:
set val(id), val(sample), val(condition), file(scored_osw) from merged_osw_file_for_global
output:
set val(id), val(sample), val(condition), file("${scored_osw.baseName}_global_merged.osw") into merged_osw_scored_global_for_pyprophet
set val(id), val(sample), val(condition), file("*.pdf") into target_decoy_global_score_plots
when:
!params.skip_dia_processing
script:
if (params.pyprophet_classifier=='LDA'){
"""
pyprophet score \\
--in=${scored_osw} \\
--level=${params.pyprophet_fdr_ms_level} \\
--out=${scored_osw.baseName}_scored.osw \\
--classifier=${params.pyprophet_classifier} \\
--pi0_lambda ${params.pyprophet_pi0_start} ${params.pyprophet_pi0_end} ${params.pyprophet_pi0_steps} \\
--threads=${task.cpus}
pyprophet peptide \\
--in=${scored_osw.baseName}_scored.osw \\
--out=${scored_osw.baseName}_global_merged.osw \\
--context=run-specific
pyprophet peptide --in=${scored_osw.baseName}_global_merged.osw --context=experiment-wide
pyprophet peptide --in=${scored_osw.baseName}_global_merged.osw --context=global
pyprophet ${params.pyprophet_global_fdr_level} --in=${scored_osw.baseName}_global_merged.osw --context=run-specific
pyprophet ${params.pyprophet_global_fdr_level} --in=${scored_osw.baseName}_global_merged.osw --context=experiment-wide
pyprophet ${params.pyprophet_global_fdr_level} --in=${scored_osw.baseName}_global_merged.osw --context=global
"""
} else {
"""
pyprophet score \\
--in=${scored_osw} \\
--level=${params.pyprophet_fdr_ms_level} \\
--out=${scored_osw.baseName}_scored.osw \\
--classifier=${params.pyprophet_classifier} \\
--threads=${task.cpus}
pyprophet peptide \\
--in=${scored_osw.baseName}_scored.osw \\
--out=${scored_osw.baseName}_global_merged.osw \\
--context=run-specific
pyprophet peptide --in=${scored_osw.baseName}_global_merged.osw --context=experiment-wide
pyprophet peptide --in=${scored_osw.baseName}_global_merged.osw --context=global
pyprophet ${params.pyprophet_global_fdr_level} --in=${scored_osw.baseName}_global_merged.osw --context=run-specific
pyprophet ${params.pyprophet_global_fdr_level} --in=${scored_osw.baseName}_global_merged.osw --context=experiment-wide
pyprophet ${params.pyprophet_global_fdr_level} --in=${scored_osw.baseName}_global_merged.osw --context=global
"""
}
}
/*
* STEP 11 - Pyprophet Export
*/
process export_of_scoring_results {
publishDir "${params.outdir}/pyprophet_output"
input:
set val(id), val(sample), val(condition), file(global_osw) from merged_osw_scored_global_for_pyprophet
output:
set val(id), val(sample), val(condition), file("*.tsv") into pyprophet_results
set val(id), val(sample), val(condition), file(global_osw) into osw_for_dialignr
when:
!params.skip_dia_processing
script:
"""
pyprophet export \\
--in=${global_osw} \\
--max_rs_peakgroup_qvalue=${params.pyprophet_peakgroup_fdr} \\
--max_global_peptide_qvalue=${params.pyprophet_peptide_fdr} \\
--max_global_protein_qvalue=${params.pyprophet_protein_fdr} \\
--out=legacy.tsv
"""
}
/*
* STEP 12 - Index Chromatogram mzMLs
*/
process chromatogram_indexing {
label 'process_high'
input:
set val(id), val(sample), val(condition), file(chrom_file_noindex) from chromatogram_files
output:
set val(id), val(sample), val(condition), file("${chrom_file_noindex.baseName.split('_chrom')[0]}.chrom.sqMass") into chromatogram_files_indexed
when:
!params.skip_dia_processing
script:
"""
FileConverter \\
-in ${chrom_file_noindex} \\
-process_lowmemory \\
-out ${chrom_file_noindex.baseName.split('_chrom')[0]}.chrom.mzML
OpenSwathMzMLFileCacher \\
-in ${chrom_file_noindex.baseName.split('_chrom')[0]}.chrom.mzML \\
-lossy_compression false \\
-process_lowmemory \\
-lowmem_batchsize 50000 \\
-out ${chrom_file_noindex.baseName.split('_chrom')[0]}.chrom.sqMass
"""
}
// Combine channels of osw files and osw chromatograms
osw_for_dialignr
.transpose()
.join(chromatogram_files_indexed, by:1)
.groupTuple(by:0)
// Channel contains now the following elements:
// ([id, [samples], [conditions], [osw_files], id_2, condition_2, [chromatogram_files]])
.flatMap{it -> [tuple(it[0],it[1].unique()[0],it[2].unique()[0],it[3].unique()[0],it[4],it[5],it[6])]}
.set{osw_and_chromatograms_combined_by_condition}
/*
* STEP 13 - Align DIA Chromatograms using DIAlignR
*/
process chromatogram_alignment {
publishDir "${params.outdir}/"
label 'process_high_mem'
input:
set val(sample), val(id), val(condition), file(pyresults), val(id_dummy), val(condition_dummy), file(chrom_files_index) from osw_and_chromatograms_combined_by_condition
output:
set val(id), val(sample), val(condition), file("${sample}_peptide_quantities.csv") into (DIALignR_result, DIALignR_result_I, DIALignR_result_mztab)
when:
!params.skip_dia_processing
script:
"""
mkdir osw
mv ${pyresults} osw/
mkdir xics
mv *.chrom.sqMass xics/
DIAlignR.R \\
${params.dialignr_global_align_fdr} \\
${params.dialignr_analyte_fdr} \\
${params.dialignr_unalign_fdr} \\
${params.dialignr_align_fdr} \\
${params.dialignr_query_fdr} \\
${params.pyprophet_global_fdr_level} \\
${params.dialignr_xicfilter} \\
${dialignr_parallel} \\
${task.cpus}
mv DIAlignR.tsv ${sample}_peptide_quantities.csv
"""
}
/*
* STEP 14 - Reformat output for MSstats: Combine with experimental design and missing columns from input library
*/
process reformatting {
publishDir "${params.outdir}/"
input:
set val(id), val(sample), val(condition), file(dialignr_file) from DIALignR_result
file exp_design from input_exp_design.first()
set val(id), val(sample_lib), file(lib_file) from input_lib_used_I.first()
output:
set val(id), val(sample), val(condition), file("${sample}_${condition}.csv") into msstats_file
when:
params.run_msstats
script:
if (params.pyprophet_global_fdr_level==''){
"""
TargetedFileConverter \\
-in ${lib_file} \\
-out ${lib_file.baseName}.tsv
reformat_output_for_msstats.py \\
--input ${dialignr_file} \\
--exp_design ${exp_design} \\
--library ${lib_file.baseName}.tsv \\
--fdr_level "none" \\
--output "${sample}_${condition}.csv"
"""
} else {
"""
TargetedFileConverter -in ${lib_file} -out ${lib_file.baseName}.tsv
reformat_output_for_msstats.py \\
--input ${dialignr_file} \\
--exp_design ${exp_design} \\
--library ${lib_file.baseName}.tsv \\
--fdr_level ${params.pyprophet_global_fdr_level} \\
--output "${sample}_${condition}.csv"
"""
}
}
/*
* STEP 14.5 - export_mztab
*/
process mztab_export {
publishDir "${params.outdir}/"
input:
set val(id), val(sample), val(condition), file(dialignr_file) from DIALignR_result_mztab
file exp_design from input_exp_design_mztab.first()
set val(id), val(sample_lib), file(lib_file) from input_lib_used_I_mztab.first()
output:
set val(id), val(sample), val(condition), file("${sample}_${condition}.mzTab") into mztab_file
when:
params.mztab_export
script:
"""
TargetedFileConverter -in ${lib_file} -out ${lib_file.baseName}.tsv
mztab_output.py \\
--input ${dialignr_file} \\
--exp_design ${exp_design} \\
--library ${lib_file.baseName}.tsv \\
--fdr_level ${params.pyprophet_global_fdr_level} \\
--fdr_threshold_pep ${params.pyprophet_peptide_fdr} \\
--fdr_threshold_prot ${params.pyprophet_protein_fdr} \\
--ms1_scoring ${params.use_ms1} \\
--rt_extraction_window ${params.rt_extraction_window} \\
--mz_extraction_window ${params.mz_extraction_window} \\
--mz_extraction_window_ms1 ${params.mz_extraction_window_ms1} \\
--mz_extraction_unit ${params.mz_extraction_window_unit} \\
--mz_extraction_unit_ms1 ${params.mz_extraction_window_ms1_unit} \\
--dialignr_global_align_fdr ${params.dialignr_global_align_fdr} \\
--dialignr_analyte_fdr ${params.dialignr_analyte_fdr} \\
--dialignr_unalign_fdr ${params.dialignr_unalign_fdr} \\
--dialignr_align_fdr ${params.dialignr_align_fdr} \\
--dialignr_query_fdr ${params.dialignr_query_fdr} \\
--workflow_version $workflow.manifest.version \\
--output "${sample}_${condition}.mzTab"
"""
}
/*
* STEP 15 - Run MSstats
*/
process statistical_post_processing {
publishDir "${params.outdir}/"
label 'process_low'
input:
set val(id), val(sample), val(condition), file(csv) from msstats_file.groupTuple(by:1)
output:
file "*.pdf" optional true // Output plots: 1) Comparative plots across pairwise conditions, 2) VolcanoPlot
file "*.csv" // Csv of normalized differential protein abundancies calculated by msstats
file "*.log" // logfile of msstats run
when:
params.run_msstats
script:
"""
msstats.R > msstats.log || echo "Optional MSstats step failed. Please check logs and re-run or do a manual statistical analysis."
"""
}
/*
* STEP 16 - Generate plots describing output:
* 1) BarChartProtein/Peptide Counts
* 2) Pie Chart: Peptide Charge distribution
* 3) Density Scatter: Library vs run RT deviations for all identifications
* 4) Heatmap: Peptide quantities across MS runs
* 5) Pyprophet score plots
*/
process output_visualization {
publishDir "${params.outdir}/"
label 'process_high'
input:
set val(sample), val(id), val(condition), file(quantity_csv_file), val(dummy_id), val(dummy_condition), file(pyprophet_tsv_file) from DIALignR_result_I.transpose().join(pyprophet_results, by:1)
output:
file "*.pdf" into output_plots
when:
params.generate_plots
script:
"""
plot_quantities_and_counts.R ${sample}