Add snpclustering subworkflow #11879
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| BEGIN { | ||
| FS = OFS = "\t" | ||
| } | ||
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| NR == 1 { | ||
| sub(/^#/, "") | ||
| $0 = $0 | ||
| for (i = 1; i <= NF; i++) { | ||
| if ($i == "IID") { | ||
| $i = "sample_id" | ||
| } | ||
| } | ||
| next | ||
| } | ||
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| { | ||
| } |
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| @@ -0,0 +1,125 @@ | ||
| include { BEAGLE5_BEAGLE } from '../../../modules/nf-core/beagle5/beagle' | ||
| include { PLINK2_VCF } from '../../../modules/nf-core/plink2/vcf' | ||
| include { PLINK2_PCA } from '../../../modules/nf-core/plink2/pca' | ||
| include { GAWK as GAWK_EIGENVEC_TO_TSV } from '../../../modules/nf-core/gawk' | ||
| include { CUSTOM_PCACLUSTERING } from '../../../modules/nf-core/custom/pcaclustering' | ||
| include { CUSTOM_CLUSTERMETRICS } from '../../../modules/nf-core/custom/clustermetrics' | ||
| include { CUSTOM_CLUSTERVISUALIZATION } from '../../../modules/nf-core/custom/clustervisualization' | ||
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| workflow SNPCLUSTERING { | ||
| take: | ||
| vcf_ch | ||
| refpanel_ch | ||
| genmap_ch | ||
| region | ||
| npcs | ||
| use_approx | ||
| algorithm | ||
| n_clusters | ||
| dbscan_eps | ||
| dbscan_min_samples | ||
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| main: | ||
| // Bundled awk program (see ./awk/eigenvec_to_tsv.awk); shipped alongside | ||
| // the subworkflow and passed to GAWK_EIGENVEC_TO_TSV as `program_file` so | ||
| // the awk source stays readable as awk rather than as an escaped Groovy | ||
| // string. | ||
| ch_eigenvec_to_tsv_awk = file("${moduleDir}/awk/eigenvec_to_tsv.awk", checkIfExists: true) | ||
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| /* | ||
| * Build BEAGLE input tuple: | ||
| * tuple val(meta), path(vcf), path(vcf_index), path(refpanel), path(refpanel_index), | ||
| * path(genmap), path(exclsamples), path(exclmarkers), val(region) | ||
| */ | ||
| ch_beagle_input = vcf_ch.map { meta, vcf, vcf_index -> | ||
| tuple( | ||
| meta, | ||
| vcf, | ||
| vcf_index, | ||
| [], | ||
| [], | ||
| [], | ||
| [], | ||
| [], | ||
| region | ||
| ) | ||
| } | ||
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| BEAGLE5_BEAGLE(ch_beagle_input) | ||
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| /* | ||
| * Convert imputed VCF to PLINK2 pfiles | ||
| */ | ||
| PLINK2_VCF(BEAGLE5_BEAGLE.out.vcf) | ||
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| /* | ||
| * PLINK2_PCA expects: | ||
| * tuple val(meta), val(npcs), val(use_approx), path(pgen), path(psam), path(pvar) | ||
| */ | ||
| ch_plink_pca_input = PLINK2_VCF.out.pgen | ||
| .join(PLINK2_VCF.out.pvar) | ||
| .join(PLINK2_VCF.out.psam) | ||
| .map { meta, pgen, pvar, psam -> | ||
| tuple(meta, npcs, use_approx, pgen, psam, pvar) | ||
|
dbaku42 marked this conversation as resolved.
Outdated
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| } | ||
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| PLINK2_PCA(ch_plink_pca_input) | ||
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| /* | ||
| * Convert .eigenvec to a tab-separated file for downstream clustering | ||
| * modules: strips the leading '#' from the header and renames the IID | ||
| * column to sample_id. Output keeps its default naming (<meta.id>.eigenvec) | ||
| * — only the tab-separated content matters downstream, not the extension. | ||
| */ | ||
| GAWK_EIGENVEC_TO_TSV( | ||
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Contributor
There was a problem hiding this comment. Personally, I wonder if having a dedicated process to remove
Contributor
Author
There was a problem hiding this comment. Fair question. I left it as a dedicated step so custom/pcaclustering stays generic (any TSV of sample × features) and does not absorb PLINK-specific quirks (# header, IID/FID). The rename to sample_id also matches what clustermetrics / clustervisualization already expect. I’d rather keep that adapter at the subworkflow boundary than special-case PLINK inside the clustering module.
Contributor
There was a problem hiding this comment. I think it would be better to special-case PLINK format in your clustering module, since it seems to commonly consume files that were generated in PLINK format. Which in my view would actually make the module more generic? I understand that this is a custom module, but to me it would facilitate wider adoption if this module was tailored to the inputs, and it is a very simple operation (e.g. My main concern here is that I assume spawning all of these jobs creates some overhead, and the jobs do very little. Since @famosab has been involved more than I am, maybe there are considerations here that I am not aware of. |
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| PLINK2_PCA.out.evecfile, | ||
| ch_eigenvec_to_tsv_awk, | ||
| false | ||
| ) | ||
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| /* | ||
| * PCA clustering | ||
| * CUSTOM_PCACLUSTERING(tsv, algorithm, n_clusters, dbscan_eps, dbscan_min_samples) | ||
| */ | ||
| CUSTOM_PCACLUSTERING( | ||
| GAWK_EIGENVEC_TO_TSV.out.output, | ||
| algorithm, | ||
| n_clusters, | ||
| dbscan_eps, | ||
| dbscan_min_samples | ||
| ) | ||
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| /* | ||
| * Metrics and visualization both expect one tuple input channel | ||
| * built from: meta + tsv + cluster assignments | ||
| */ | ||
| ch_cluster_analysis_input = GAWK_EIGENVEC_TO_TSV.out.output | ||
| .join(CUSTOM_PCACLUSTERING.out.clusters) | ||
| .map { meta, tsv, clusters -> | ||
| tuple(meta, tsv, clusters) | ||
| } | ||
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| CUSTOM_CLUSTERMETRICS(ch_cluster_analysis_input) | ||
| CUSTOM_CLUSTERVISUALIZATION(ch_cluster_analysis_input) | ||
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| emit: | ||
| imputed_vcf = BEAGLE5_BEAGLE.out.vcf | ||
| beagle_log = BEAGLE5_BEAGLE.out.log | ||
| pgen = PLINK2_VCF.out.pgen | ||
| pvar = PLINK2_VCF.out.pvar | ||
| psam = PLINK2_VCF.out.psam | ||
| evecfile = PLINK2_PCA.out.evecfile | ||
| evfile = PLINK2_PCA.out.evfile | ||
| pca_log = PLINK2_PCA.out.logfile | ||
| tsv = GAWK_EIGENVEC_TO_TSV.out.output | ||
| clusters = CUSTOM_PCACLUSTERING.out.clusters | ||
| cluster_info = CUSTOM_PCACLUSTERING.out.info | ||
| metrics = CUSTOM_CLUSTERMETRICS.out.metrics | ||
| k_sweep = CUSTOM_CLUSTERMETRICS.out.k_sweep | ||
| selected = CUSTOM_CLUSTERMETRICS.out.selected | ||
| metric_plots = CUSTOM_CLUSTERMETRICS.out.plots | ||
| umap_tsv = CUSTOM_CLUSTERVISUALIZATION.out.umap_tsv | ||
| tsne_tsv = CUSTOM_CLUSTERVISUALIZATION.out.tsne_tsv | ||
| umap_png = CUSTOM_CLUSTERVISUALIZATION.out.umap_png | ||
| tsne_png = CUSTOM_CLUSTERVISUALIZATION.out.tsne_png | ||
| } | ||
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