Fix remaining linting warnings

CHANGELOG.md

@@ -15,6 +15,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ### Chore
 
+- Clear all `nextflow lint` warnings (use `channel` factory API, explicit closure parameters, unused-parameter prefixes). ([#542](https://github.com/nf-core/scrnaseq/pull/542))
 - Migrate local subworkflows to directory-based layout with `main.nf`, matching the new nf-core standard structure for modules and subworkflows ([#553](https://github.com/nf-core/scrnaseq/pull/553))
 - Template update for nf-core/tools v3.5.1 ([#509](https://github.com/nf-core/scrnaseq/pull/509))
 - Template update for nf-core/tools v4.0.2 ([#541](https://github.com/nf-core/scrnaseq/pull/541))

conf/modules.config

@@ -159,7 +159,7 @@ process {
         publishDir = [
             path: { "${params.outdir}/${params.aligner}/${meta.id}" },
             mode: params.publish_dir_mode,
-            saveAs: { (!it.endsWith('.bam') || params.save_align_intermeds) ? it : null }
+            saveAs: { filename -> (!filename.endsWith('.bam') || params.save_align_intermeds) ? filename : null }
         ]
     }
 

subworkflows/local/align_cellranger/main.nf

@@ -41,17 +41,17 @@ workflow CELLRANGER_ALIGN {
         ch_matrices_raw =
         CELLRANGER_COUNT.out.outs.map { meta, mtx_files ->
             def desired_files = []
-            mtx_files.each{
-                if ( it.toString().contains("raw_feature_bc_matrix") ) { desired_files.add( it ) }
+            mtx_files.each{ path ->
+                if ( path.toString().contains("raw_feature_bc_matrix") ) { desired_files.add( path ) }
             }
             [ meta + [input_type: 'raw'], desired_files ]
         }
 
         ch_matrices_filtered =
         CELLRANGER_COUNT.out.outs.map { meta, mtx_files ->
             def desired_files = []
-            mtx_files.each{
-                if ( it.toString().contains("filtered_feature_bc_matrix") ) { desired_files.add( it ) }
+            mtx_files.each{ path ->
+                if ( path.toString().contains("filtered_feature_bc_matrix") ) { desired_files.add( path ) }
             }
             [ meta + [input_type: 'filtered'], desired_files ]
         }

subworkflows/local/align_cellrangerarc/main.nf

@@ -67,7 +67,7 @@ def parse_demultiplexed_output_channels(in_ch, pattern) {
         meta_clone.input_type = pattern.contains('raw_') ? 'raw' : 'filtered'
         // Iterate over the matrix files and add the ones matching the pattern to the desired files list
         def desired_files = []
-        mtx_files.each{ if ( it.toString().contains("${pattern}") ) { desired_files.add( it ) } }
+        mtx_files.each{ path -> if ( path.toString().contains("${pattern}") ) { desired_files.add( path ) } }
         [ meta_clone, desired_files ]
     }
 

subworkflows/local/align_cellrangermulti/main.nf

@@ -29,7 +29,7 @@ workflow CELLRANGER_MULTI_ALIGN {
         .flatten()
         .map{ meta ->
             def meta_clone = meta.clone()
-            def data_dict  = meta_clone.find{ it.key == "${meta_clone.feature_type}" }
+            def data_dict  = meta_clone.find{ entry -> entry.key == "${meta_clone.feature_type}" }
             def fastqs = data_dict?.value
             meta_clone.remove( data_dict?.key )
             [ meta_clone, fastqs ]
@@ -90,26 +90,26 @@ workflow CELLRANGER_MULTI_ALIGN {
 
             // CMO
             ch_grouped_fastq.gex
-            .map{ [it[0].id] }
-            .concat( PARSE_CELLRANGERMULTI_SAMPLESHEET.out.cmo.flatten().map { [ "${it.baseName}" - "_cmo", it ] } )
+            .map{ pair -> [pair[0].id] }
+            .concat( PARSE_CELLRANGERMULTI_SAMPLESHEET.out.cmo.flatten().map { csv -> [ "${csv.baseName}" - "_cmo", csv ] } )
             .groupTuple()
-            .map { if ( it.size() == 2 ) { it[1] } else { [] } } // a correct tuple from snippet will have: [ sample, cmo.csv ]
+            .map { grp -> if ( grp.size() == 2 ) { grp[1] } else { [] } } // a correct tuple from snippet will have: [ sample, cmo.csv ]
             .set { ch_cmo_barcode_csv }
 
             // OCM
             ch_grouped_fastq.gex
-            .map{ [it[0].id] }
-            .concat( PARSE_CELLRANGERMULTI_SAMPLESHEET.out.ocm.flatten().map { [ "${it.baseName}" - "_ocm", it ] } )
+            .map{ pair -> [pair[0].id] }
+            .concat( PARSE_CELLRANGERMULTI_SAMPLESHEET.out.ocm.flatten().map { csv -> [ "${csv.baseName}" - "_ocm", csv ] } )
             .groupTuple()
-            .map { if ( it.size() == 2 ) { it[1] } else { [] } } // a correct tuple from snippet will have: [ sample, ocm.csv ]
+            .map { grp -> if ( grp.size() == 2 ) { grp[1] } else { [] } } // a correct tuple from snippet will have: [ sample, ocm.csv ]
             .set { ch_ocm_barcode_csv }
 
             // FRNA
             ch_grouped_fastq.gex
-            .map{ [it[0].id] }
-            .concat( PARSE_CELLRANGERMULTI_SAMPLESHEET.out.frna.flatten().map { [ "${it.baseName}" - "_frna", it ] } )
+            .map{ pair -> [pair[0].id] }
+            .concat( PARSE_CELLRANGERMULTI_SAMPLESHEET.out.frna.flatten().map { csv -> [ "${csv.baseName}" - "_frna", csv ] } )
             .groupTuple()
-            .map { if ( it.size() == 2 ) { it[1] } else { [] } } // a correct tuple from snippet will have: [ sample, frna.csv ]
+            .map { grp -> if ( grp.size() == 2 ) { grp[1] } else { [] } } // a correct tuple from snippet will have: [ sample, frna.csv ]
             .set { ch_frna_sample_csv }
 
         } else {
@@ -197,7 +197,7 @@ workflow CELLRANGER_MULTI_ALIGN {
         // MODULE: cellranger multi
         //
         CELLRANGER_MULTI(
-            ch_grouped_fastq.gex.map{ it[0] },
+            ch_grouped_fastq.gex.map{ pair -> pair[0] },
             ch_grouped_fastq.gex,
             ch_grouped_fastq.vdj,
             ch_grouped_fastq.ab,
@@ -244,7 +244,7 @@ def parse_demultiplexed_output_channels(in_ch, pattern) {
     def out_ch = in_ch
         .map { meta, mtx_files ->
             def desired_files = []
-            mtx_files.each{ if ( it.toString().contains("${pattern}") ) { desired_files.add( it ) } }
+            mtx_files.each{ path -> if ( path.toString().contains("${pattern}") ) { desired_files.add( path ) } }
             [ meta, desired_files ]
         }                    // separate only desired files
         .transpose()         // transpose for handling one meta/file pair at a time
@@ -260,7 +260,7 @@ def parse_demultiplexed_output_channels(in_ch, pattern) {
             }
             [ meta_clone, mtx_files ]
         }                    // check if output is from demultiplexed sample, if yes, correct meta.id for proper conversion naming
-        .filter{ it != null } // remove nulls from previous step
+        .filter{ item -> item != null } // remove nulls from previous step
         .groupTuple( by: 0 ) // group it back as one file collection per sample
 
     return out_ch

subworkflows/local/fastqc/main.nf

@@ -26,7 +26,7 @@ workflow FASTQC_CHECK {
         .map { it -> [ it[1] ] }
         .set { fastqc_html_only }
 
-    fastqc_multiqc = Channel.empty()
+    fastqc_multiqc = channel.empty()
     fastqc_multiqc = fastqc_multiqc.mix( fastqc_zip_only, fastqc_html_only )
 
     emit:

subworkflows/local/kallisto_bustools/main.nf

@@ -17,7 +17,7 @@ workflow KALLISTO_BUSTOOLS {
     ch_fastq
 
     main:
-    ch_versions = Channel.empty()
+    ch_versions = channel.empty()
 
     assert (txp2gene && kallisto_index) || (genome_fasta && gtf):
         "Must provide a genome fasta file ('--fasta') and a gtf file ('--gtf') if no index is given!"

subworkflows/local/simpleaf/main.nf

@@ -20,7 +20,7 @@ workflow SIMPLEAF {
     map_dir
 
     main:
-    ch_versions = Channel.empty()
+    ch_versions = channel.empty()
 
     /*
     * Build simpleaf index if needed
@@ -60,20 +60,20 @@ workflow SIMPLEAF {
             if (!txp2gene) {
                 txp2gene = SIMPLEAF_INDEX.out.t2g.collect().map { _meta, it -> it }
             } else {
-                txp2gene = Channel.of( txp2gene )
+                txp2gene = channel.of( txp2gene )
             }
         } else {
             // we have a map dir, so we do not need to build the index
-            simpleaf_index = Channel.of( [ [:], [] ] )
+            simpleaf_index = channel.of( [ [:], [] ] )
         }
     } else {
         // we have a simpleaf index, we use it directly
         // ensure simpleaf index and txp2gene are Channels
-        simpleaf_index = Channel.of( [ [ id: simpleaf_index.getName() ], simpleaf_index ] )
+        simpleaf_index = channel.of( [ [ id: simpleaf_index.getName() ], simpleaf_index ] )
 
         // channel or null
         if (txp2gene) {
-            txp2gene = Channel.of( txp2gene )
+            txp2gene = channel.of( txp2gene )
         }
     }
 
@@ -119,7 +119,7 @@ workflow SIMPLEAF {
     /*
     * Run qcatch QC (optional)
     */
-    ch_qcatch_report = Channel.empty()
+    ch_qcatch_report = channel.empty()
     if ( !skip_qcatch ) {
         // Map quant channel to include chemistry for qcatch: tuple(meta, chemistry, quant_dir)
         ch_qcatch_input = ch_af_quant.map { meta, quant_dir -> [meta, qcatch_chemistry, quant_dir] }

subworkflows/local/starsolo/main.nf

@@ -69,5 +69,5 @@ workflow STARSOLO {
     star_counts     = STAR_ALIGN.out.counts
     raw_counts      = raw_counts
     filtered_counts = filtered_counts
-    for_multiqc     = STAR_ALIGN.out.log_final.map{ meta, it -> it }
+    for_multiqc     = STAR_ALIGN.out.log_final.map{ _meta, logFinal -> logFinal }
 }

subworkflows/local/utils_nfcore_scrnaseq_pipeline/main.nf

@@ -31,7 +31,7 @@ workflow PIPELINE_INITIALISATION {
     monochrome_logs   // boolean: Do not use coloured log outputs
     nextflow_cli_args //   array: List of positional nextflow CLI args
     outdir            //  string: The output directory where the results will be saved
-    input             //  string: Path to input samplesheet
+    _input            //  string: Path to input samplesheet
     help              // boolean: Display help message and exit
     help_full         // boolean: Show the full help message
     show_hidden       // boolean: Show hidden parameters in the help message
@@ -107,7 +107,7 @@ workflow PIPELINE_INITIALISATION {
     // Create channel from input file provided through params.input
     //
     if (params.aligner == 'cellrangermulti') { // the cellrangermulti sub-workflow logic needs that channels have reads separated by feature_type. Cannot merge all.
-        Channel
+        channel
             .fromList(samplesheetToList(params.input, "${projectDir}/assets/schema_input.json"))
             .map {
                 meta, fastq_1, fastq_2 ->
@@ -118,17 +118,17 @@ workflow PIPELINE_INITIALISATION {
                     }
             }
             .groupTuple( by: [0,1] )
-            .map{ id, type, meta, reads -> [ id, meta, reads ] }
-            .map {
-                validateInputSamplesheet(it)
+            .map{ id, _type, meta, reads -> [ id, meta, reads ] }
+            .map { sheet_row ->
+                validateInputSamplesheet(sheet_row)
             }
             .map {
                 meta, fastqs ->
                     return [ meta, fastqs.flatten() ]
             }
             .set { ch_samplesheet }
     } else if (params.aligner == 'cellrangerarc') { // the cellrangerarc sub-workflow logic needs that channels have a meta, type, subsample, fastqs structure.
-        Channel
+        channel
             .fromList(samplesheetToList(params.input, "${projectDir}/assets/schema_input.json"))
             .map { meta, fastq_1, fastq_2 ->
                 if (!fastq_2 || (meta.sample_type == "atac" && !meta.fastq_barcode)) {
@@ -141,12 +141,12 @@ workflow PIPELINE_INITIALISATION {
                 }
             }
             .groupTuple()
-            .map {
-                cellrangerarcStructure(it)
+            .map { structure_input ->
+                cellrangerarcStructure(structure_input)
             }
             .set { ch_samplesheet }
     } else {
-        Channel
+        channel
             .fromList(samplesheetToList(params.input, "${projectDir}/assets/schema_input.json"))
             .map {
                 meta, fastq_1, fastq_2 ->
@@ -157,8 +157,8 @@ workflow PIPELINE_INITIALISATION {
                     }
             }
             .groupTuple()
-            .map {
-                validateInputSamplesheet(it)
+            .map { sheet_row ->
+                validateInputSamplesheet(sheet_row)
             }
             .map {
                 meta, fastqs ->
@@ -241,7 +241,7 @@ def validateCellrangerMultiBarcodes() {
     def cellranger_multi_barcodes = file(params.cellranger_multi_barcodes).splitCsv(header: true)
 
     // Get unique samples from input samplesheet for cross-validation
-    def inputSamples = file(params.input).splitCsv(header: true).collect { it.sample }.toSet()
+    def inputSamples = file(params.input).splitCsv(header: true).collect { row -> row.sample }.toSet()
 
     // Check that at least one barcode column is provided for each row
     // and that each sample uses only one type of barcode
@@ -268,14 +268,14 @@ def validateCellrangerMultiBarcodes() {
 
     // Validate that at least one barcode identifier is populated in each row
     if (rowsWithoutBarcodes) {
-        def errorDetails = rowsWithoutBarcodes.collect { "row ${it.row} (${it.multiplexed_sample_id})" }.join(', ')
+        def errorDetails = rowsWithoutBarcodes.collect { missing -> "row ${missing.row} (${missing.multiplexed_sample_id})" }.join(', ')
         error("Please check cellranger_multi_barcodes samplesheet -> " +
               "The following rows have no barcode identifiers: ${errorDetails}. " +
               "Each row must have exactly one of: 'probe_barcode_ids', 'cmo_ids', or 'ocm_ids'.")
     }
 
     // Validate that no more than one barcode identifier is populated in each row
-    def samplesWithMixedBarcodes = sampleBarcodeTypes.findAll { multiplexed_sample_id, info -> info.types.size() > 1 }
+    def samplesWithMixedBarcodes = sampleBarcodeTypes.findAll { _multiplexed_sample_id, info -> info.types.size() > 1 }
     if (samplesWithMixedBarcodes) {
         def errorMsg = samplesWithMixedBarcodes.collect { multiplexed_sample_id, info ->
             "'${multiplexed_sample_id}' (row ${info.row}) uses multiple barcode types: ${info.types.join(', ')}"
@@ -321,7 +321,7 @@ def cellrangerarcStructure(input) {
 
     // Validate that the property "sample_type" is present and has valid values
     def valid_sample_types = ["gex", "atac"]
-    def sample_type_ok = metas.collect { meta -> meta.sample_type }.unique().every { it in valid_sample_types }
+    def sample_type_ok = metas.collect { meta -> meta.sample_type }.unique().every { st -> st in valid_sample_types }
     if (!sample_type_ok) {
         error("Please check input samplesheet -> The property 'sample_type' is required and can only be 'gex' or 'atac'.")
     }

workflows/scrnaseq.nf

@@ -109,7 +109,7 @@ workflow SCRNASEQ {
     //
     if (fasta) {
         if (fasta.endsWith('.gz')) {
-            ch_genome_fasta    = GUNZIP_FASTA ( [ [:], ch_genome_fasta ] ).gunzip.map { it[1] }
+            ch_genome_fasta    = GUNZIP_FASTA ( [ [:], ch_genome_fasta ] ).gunzip.map { tuple -> tuple[1] }
         } else {
             ch_genome_fasta = channel.value( ch_genome_fasta )
         }
@@ -120,7 +120,7 @@ workflow SCRNASEQ {
     //
     if (gtf) {
         if (gtf.endsWith('.gz')) {
-            ch_gtf      = GUNZIP_GTF ( [ [:], ch_gtf ] ).gunzip.map { it[1] }
+            ch_gtf      = GUNZIP_GTF ( [ [:], ch_gtf ] ).gunzip.map { tuple -> tuple[1] }
         } else {
             ch_gtf = channel.value( ch_gtf )
         }
@@ -205,7 +205,7 @@ workflow SCRNASEQ {
         )
         ch_mtx_matrices = ch_mtx_matrices.mix( CELLRANGER_ALIGN.out.cellranger_matrices_raw, CELLRANGER_ALIGN.out.cellranger_matrices_filtered )
         ch_multiqc_files = ch_multiqc_files.mix(CELLRANGER_ALIGN.out.cellranger_out.map {
-            meta, outs -> outs.findAll{ it -> it.name == "web_summary.html"}
+            _meta, outs -> outs.findAll{ summary -> summary.name == "web_summary.html"}
         })
     }
 
@@ -263,12 +263,12 @@ workflow SCRNASEQ {
             // needs to have a collected map like that, so every sample from the samplesheet is analysed one at a time,
             // allowing to have multiple samples in the sheet, having all the data-type tuples initialized,
             // either empty or populated. It will be branched inside the subworkflow.
-            if (!map_collection_clone.any{ it.feature_type == 'gex' })    { map_collection_clone.add( [id: sample_id, feature_type: 'gex'   , gex:    empty_file, options:[:] ] ) }
-            if (!map_collection_clone.any{ it.feature_type == 'vdj' })    { map_collection_clone.add( [id: sample_id, feature_type: 'vdj'   , vdj:    empty_file, options:[:] ] ) }
-            if (!map_collection_clone.any{ it.feature_type == 'ab' })     { map_collection_clone.add( [id: sample_id, feature_type: 'ab'    , ab:     empty_file, options:[:] ] ) }
-            if (!map_collection_clone.any{ it.feature_type == 'beam' })   { map_collection_clone.add( [id: sample_id, feature_type: 'beam'  , beam:   empty_file, options:[:] ] ) } // currently not implemented, the input samplesheet checking will not allow it.
-            if (!map_collection_clone.any{ it.feature_type == 'crispr' }) { map_collection_clone.add( [id: sample_id, feature_type: 'crispr', crispr: empty_file, options:[:] ] ) }
-            if (!map_collection_clone.any{ it.feature_type == 'cmo' })    { map_collection_clone.add( [id: sample_id, feature_type: 'cmo'   , cmo:    empty_file, options:[:] ] ) }
+            if (!map_collection_clone.any{ m -> m.feature_type == 'gex' })    { map_collection_clone.add( [id: sample_id, feature_type: 'gex'   , gex:    empty_file, options:[:] ] ) }
+            if (!map_collection_clone.any{ m -> m.feature_type == 'vdj' })    { map_collection_clone.add( [id: sample_id, feature_type: 'vdj'   , vdj:    empty_file, options:[:] ] ) }
+            if (!map_collection_clone.any{ m -> m.feature_type == 'ab' })     { map_collection_clone.add( [id: sample_id, feature_type: 'ab'    , ab:     empty_file, options:[:] ] ) }
+            if (!map_collection_clone.any{ m -> m.feature_type == 'beam' })   { map_collection_clone.add( [id: sample_id, feature_type: 'beam'  , beam:   empty_file, options:[:] ] ) } // currently not implemented, the input samplesheet checking will not allow it.
+            if (!map_collection_clone.any{ m -> m.feature_type == 'crispr' }) { map_collection_clone.add( [id: sample_id, feature_type: 'crispr', crispr: empty_file, options:[:] ] ) }
+            if (!map_collection_clone.any{ m -> m.feature_type == 'cmo' })    { map_collection_clone.add( [id: sample_id, feature_type: 'cmo'   , cmo:    empty_file, options:[:] ] ) }
 
             // return final map
             map_collection_clone