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Rules followed: main.nf should only contain workflows that are standalone i.e.: shouldn't take any inputs Any workflows that take input(s) should be put in ./workflows
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| nextflow.preview.dsl=2 | ||
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| import static groovy.json.JsonOutput.* | ||
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| ////////////////////////////////////////////////////// | ||
| // Import sub-workflows | ||
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| include '../../utils/processes/utils.nf' params(params) | ||
| include SC__H5AD_TO_FILTERED_LOOM from '../../utils/processes/h5adToLoom.nf' params(params) | ||
| include FILE_CONVERTER from '../../utils/workflows/fileConverter.nf' params(params) | ||
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| include QC_FILTER from './qc_filter.nf' params(params) | ||
| include NORMALIZE_TRANSFORM from './normalize_transform.nf' params(params) | ||
| include HVG_SELECTION from './hvg_selection.nf' params(params) | ||
| include SC__SCANPY__REGRESS_OUT from '../processes/regress_out.nf' params(params) | ||
| include NEIGHBORHOOD_GRAPH from './neighborhood_graph.nf' params(params) | ||
| include DIM_REDUCTION_PCA from './dim_reduction_pca.nf' params(params) | ||
| include './dim_reduction.nf' params(params) | ||
| include '../processes/cluster.nf' params(params) | ||
| include CLUSTER_IDENTIFICATION from './cluster_identification.nf' params(params) | ||
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| // reporting: | ||
| include UTILS__GENERATE_WORKFLOW_CONFIG_REPORT from '../../utils/processes/reports.nf' params(params) | ||
| include SC__SCANPY__MERGE_REPORTS from '../processes/reports.nf' params(params) | ||
| include SC__SCANPY__REPORT_TO_HTML from '../processes/reports.nf' params(params) | ||
| include COMBINE_REPORTS from './combine_reports.nf' params(params) | ||
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| workflow SINGLE_SAMPLE { | ||
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| take: | ||
| // Expects (sampleId, h5ad) | ||
| data | ||
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| main: | ||
| // Process the data | ||
| out = params.sc.scanpy.containsKey("filter") ? QC_FILTER( data ).filtered : data | ||
| out = params.sc.scanpy.containsKey("data_transformation") && | ||
| params.sc.scanpy.containsKey("normalization") ? NORMALIZE_TRANSFORM( out ) : out | ||
| out = HVG_SELECTION( out ) | ||
| out = params.sc.scanpy.containsKey("regress_out") ? SC__SCANPY__REGRESS_OUT( out.scaled ) : out.scaled | ||
| DIM_REDUCTION_PCA( out ) | ||
| NEIGHBORHOOD_GRAPH( DIM_REDUCTION_PCA.out ) | ||
| DIM_REDUCTION_TSNE_UMAP( NEIGHBORHOOD_GRAPH.out ) | ||
| CLUSTER_IDENTIFICATION( | ||
| NORMALIZE_TRANSFORM.out, | ||
| DIM_REDUCTION_TSNE_UMAP.out.dimred_tsne_umap, | ||
| "No Batch Effect Correction" | ||
| ) | ||
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| // Reporting | ||
| samples = data.map { it -> it[0] }.view() | ||
| UTILS__GENERATE_WORKFLOW_CONFIG_REPORT( | ||
| file(workflow.projectDir + params.utils.workflow_configuration.report_ipynb) | ||
| ) | ||
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| ipynbs = COMBINE_REPORTS( | ||
| samples, | ||
| UTILS__GENERATE_WORKFLOW_CONFIG_REPORT.out, | ||
| QC_FILTER.out.report, | ||
| HVG_SELECTION.out.report, | ||
| DIM_REDUCTION_TSNE_UMAP.out.report, | ||
| CLUSTER_IDENTIFICATION.out.report | ||
| ) | ||
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| def clusteringParams = SC__SCANPY__CLUSTERING_PARAMS( clean(params.sc.scanpy.clustering) ) | ||
| merged_report = SC__SCANPY__MERGE_REPORTS( | ||
| ipynbs, | ||
| "merged_report", | ||
| clusteringParams.isParameterExplorationModeOn() | ||
| ) | ||
| SC__SCANPY__REPORT_TO_HTML(SC__SCANPY__MERGE_REPORTS.out) | ||
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| // Conversion | ||
| // Convert h5ad to X (here we choose: loom format) | ||
| if(params.sc.scanpy.containsKey("filter")) { | ||
| filtered_loom = SC__H5AD_TO_FILTERED_LOOM( QC_FILTER.out.filtered ) | ||
| // In parameter exploration mode, this automatically merge all the results into the resulting loom | ||
| final_processed_scope_loom = FILE_CONVERTER( | ||
| CLUSTER_IDENTIFICATION.out.marker_genes.groupTuple(), | ||
| 'loom', | ||
| QC_FILTER.out.filtered | ||
| ) | ||
| } else { | ||
| filtered_loom = SC__H5AD_TO_FILTERED_LOOM( SC__FILE_CONVERTER.out ) | ||
| final_processed_scope_loom = FILE_CONVERTER( | ||
| CLUSTER_IDENTIFICATION.out.marker_genes.groupTuple(), | ||
| 'loom', | ||
| SC__FILE_CONVERTER.out | ||
| ) | ||
| } | ||
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| // Publishing | ||
| final_published_data = SC__PUBLISH_H5AD( | ||
| CLUSTER_IDENTIFICATION.out.marker_genes.map { | ||
| it -> tuple(it[0], it[1], null) | ||
| }, | ||
| params.global.project_name+".single_sample.output" | ||
| ) | ||
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| emit: | ||
| filtered_data = params.sc.scanpy.containsKey("filter") ? QC_FILTER.out.filtered : Channel.empty() | ||
| filtered_loom | ||
| hvg_data = HVG_SELECTION.out.hvg | ||
| dr_pca_data = DIM_REDUCTION_PCA.out | ||
| final_processed_data = CLUSTER_IDENTIFICATION.out.marker_genes | ||
| final_published_data | ||
| final_processed_scope_loom | ||
| reports = ipynbs | ||
| merged_report | ||
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| } |