add subworkflow for functional enrichment analysis #7254
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| // | ||
| // Perform enrichment analysis | ||
| // | ||
| include { GPROFILER2_GOST } from "../../../modules/nf-core/gprofiler2/gost/main.nf" | ||
| include { CUSTOM_TABULARTOGSEAGCT } from '../../../modules/nf-core/custom/tabulartogseagct/main.nf' | ||
| include { CUSTOM_TABULARTOGSEACLS } from '../../../modules/nf-core/custom/tabulartogseacls/main.nf' | ||
| include { CUSTOM_TABULARTOGSEACHIP } from '../../../modules/nf-core/custom/tabulartogseachip/main.nf' | ||
| include { GSEA_GSEA } from '../../../modules/nf-core/gsea/gsea/main.nf' | ||
| include { PROPR_GREA } from "../../../modules/nf-core/propr/grea/main.nf" | ||
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| // Combine meta maps, including merging non-identical values of shared keys (e.g. 'id') | ||
| def mergeMaps(meta, meta2){ | ||
| (meta + meta2).collectEntries { k, v -> | ||
| meta[k] && meta[k] != v ? [k, "${meta[k]}_${v}"] : [k, v] | ||
| } | ||
| } | ||
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| workflow DIFFERENTIAL_FUNCTIONAL_ENRICHMENT { | ||
| take: | ||
| // input data for functional analysis | ||
| // They can be the results from differential expression analysis or abundance matrix | ||
| // The functional analysis method to run should be explicitly provided | ||
| ch_input // [ meta_input, input file, method to run ] | ||
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| // gene sets and background | ||
| ch_gene_sets // [ meta_gmt, gmt file ] | ||
| ch_background // [ meta_background, background file ] | ||
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| // other - for the moment these files are only needed for GSEA | ||
| ch_contrasts // [ meta_contrast, contrast_variable, reference, target ] | ||
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There was a problem hiding this comment. (1) When providing results from differential expression analysis, a contrast would not be needed. IMO it would make sense to keep differential testing entirely out of this workflow. In case (1), it's not needed, and in case (2), a sample x signature matrix is produced. This matrix could just be fed into a differential analysis workflow (e.g. limma) again. Like that we can keep the complexity of this subworkflow low. There was a problem hiding this comment. I'm aware that the gsea module does support providing a contrast directly when using gene expression data. However, I believe that there's no point in using this mode. In this case, GSEA anyway just computes a metric (signal2noise, t-statistic, ...) based on these variables (see docs), so we can as well provide a fold change or DE-statistic directly, while having the advantage that we can provide a full model definition including covariates to the DE method. There was a problem hiding this comment.
Fair point, but it's what diff. ab. does right now. I'd recognised the possbility of a switch, but hadn't got round to it: nf-core/differentialabundance#36 There was a problem hiding this comment. Fair point that we could model the current matrix-driven GSEA as part of the differential subworkflow, alongside LIMMA et al though. There was a problem hiding this comment. I wanted the current subworkflow to be able to produce the exact behaviour of how the modules are used in the DA pipeline, hence GSEA is taking gene expression data instead of DE. Not sure why this is mode is chosen for the pipeline, maybe @pinin4fjords can step in here? But yes, I do agree that it would be conceptually cleaner if the subworkflow just takes in DE output, and so for GSEA. There was a problem hiding this comment. I'd be in favor of taking this chance of streamlining the workflow now that we are anyway changing quite a few things. We can help with implementing a module for preranked GSEA if required1. (alternatively, the decoupler module is kind of ready, and it comes with a very fast GSEA implementation -- it should generate the same results in terms of scores and p-values, but it doesn't produce all outputs such as the "leading edge" plots). Footnotes
There was a problem hiding this comment. Not to be a pain, but I do like those leading edge plots, they are useful. There was a problem hiding this comment. I'm not against a preranked GSEA module. But what I really would like to get rid of is the contrast specification in this subworkflow. There was a problem hiding this comment. I would actually like to start integrating the subworkflows in the current DA pipeline hopefully next week. |
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| ch_samplesheet // [ meta_exp, samples sheet ] | ||
| ch_featuresheet // [ meta_exp, features sheet, features id, features symbol ] | ||
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| main: | ||
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| ch_versions = Channel.empty() | ||
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| // Add method information into meta map of ch_input | ||
| // This information is used later to determine which method to run for each input | ||
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| ch_input = ch_input | ||
| .combine(ch_gene_sets) | ||
| .combine(ch_background) | ||
| .multiMap { | ||
| meta_input, file, analysis_method, meta_gmt, gmt, meta_background, background -> | ||
| def meta_new = meta_input + [ 'method': analysis_method ] | ||
| input: | ||
| [ meta_new, file ] | ||
| gene_sets: | ||
| [ meta_new, gmt ] // NOTE here we assume that the modules will not make use of meta_gmt and meta_background | ||
| background: | ||
| [ meta_new, background ] | ||
| } | ||
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| // In the case of GSEA, it needs additional files coming from other channels that other methods don't use | ||
| // here we define the input channel for the GSEA section | ||
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| def criteria = multiMapCriteria { meta_input, input, gmt, meta_exp, samplesheet, featuresheet, features_id, features_symbol, meta_contrasts, variable, reference, target -> | ||
| def meta_contrasts_new = meta_contrasts + [ 'variable': variable, 'reference': reference, 'target': target ] // make sure variable, reference, target are in the meta | ||
| def meta_all = mergeMaps(meta_contrasts_new, meta_input) | ||
| input: | ||
| [ meta_all, input ] | ||
| gene_sets: | ||
| [ meta_all, gmt ] | ||
| contrasts_and_samples: | ||
| [ meta_all, samplesheet ] | ||
| features: | ||
| [ meta_exp, featuresheet ] | ||
| features_cols: | ||
| [ features_id, features_symbol ] | ||
| } | ||
| ch_preinput_for_gsea = ch_input.input | ||
| .join(ch_input.gene_sets) | ||
| .filter{ it[0].method == 'gsea' } | ||
| .combine(ch_samplesheet.join(ch_featuresheet)) | ||
| .combine(ch_contrasts) | ||
| .multiMap(criteria) | ||
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| // ---------------------------------------------------- | ||
| // Perform enrichment analysis with gprofiler2 | ||
| // ---------------------------------------------------- | ||
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| GPROFILER2_GOST( | ||
| ch_input.input.filter{ it[0].method == 'gprofiler2' }, | ||
| ch_input.gene_sets.filter{ it[0].method == 'gprofiler2'}, | ||
| ch_input.background.filter{ it[0].method == 'gprofiler2'} | ||
| ) | ||
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| // ---------------------------------------------------- | ||
| // Perform enrichment analysis with GSEA | ||
| // ---------------------------------------------------- | ||
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| // NOTE that GCT input can be more than 1, if they come from different tools (eg. limma, deseq2). | ||
| // CLS input can be as many as combinations of input x contrasts | ||
| // Whereas features can be only one file. | ||
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| CUSTOM_TABULARTOGSEAGCT(ch_preinput_for_gsea.input) | ||
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| CUSTOM_TABULARTOGSEACLS(ch_preinput_for_gsea.contrasts_and_samples) | ||
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| CUSTOM_TABULARTOGSEACHIP( | ||
| ch_preinput_for_gsea.features.first(), | ||
| ch_preinput_for_gsea.features_cols.first() | ||
| ) | ||
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| ch_input_for_gsea = CUSTOM_TABULARTOGSEAGCT.out.gct | ||
| .join(CUSTOM_TABULARTOGSEACLS.out.cls) | ||
| .join( ch_preinput_for_gsea.gene_sets ) | ||
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| GSEA_GSEA( | ||
| ch_input_for_gsea, | ||
| ch_input_for_gsea.map{ tuple(it[0].reference, it[0].target) }, | ||
| CUSTOM_TABULARTOGSEACHIP.out.chip.first() | ||
| ) | ||
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| // ---------------------------------------------------- | ||
| // Perform enrichment analysis with GREA | ||
| // ---------------------------------------------------- | ||
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| PROPR_GREA( | ||
| ch_input.input.filter{ it[0].method == 'grea' }, | ||
| ch_input.gene_sets.filter{ it[0].method == 'grea' } | ||
| ) | ||
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| emit: | ||
| // here we emit the outputs that will be useful afterwards in the | ||
| // nf-core/differentialabundance pipeline | ||
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| // gprofiler2-specific outputs | ||
| gprofiler2_all_enrich = GPROFILER2_GOST.out.all_enrich | ||
| gprofiler2_sub_enrich = GPROFILER2_GOST.out.sub_enrich | ||
| gprofiler2_plot_html = GPROFILER2_GOST.out.plot_html | ||
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| // gsea-specific outputs | ||
| gsea_report = GSEA_GSEA.out.report_tsvs_ref | ||
| .join(GSEA_GSEA.out.report_tsvs_target) | ||
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| // grea-specific outputs | ||
| grea_results = PROPR_GREA.out.results | ||
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| // tool versions | ||
| versions = ch_versions | ||
| .mix(GPROFILER2_GOST.out.versions) | ||
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| .mix(CUSTOM_TABULARTOGSEAGCT.out.versions) | ||
| .mix(CUSTOM_TABULARTOGSEACLS.out.versions) | ||
| .mix(CUSTOM_TABULARTOGSEACHIP.out.versions) | ||
| .mix(GSEA_GSEA.out.versions) | ||
| .mix(PROPR_GREA.out.versions) | ||
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There was a problem hiding this comment. Why are there no emissions? |
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| } | ||
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Would this necessarily need to be a GMT file?
E.g. decoupler uses weighted gene sets for PROGENy and collecTRI analyses that are typically provided as a long-form data frame. If we included deconvolution as functional analysis tool, it would typically use a signature matrix.
It really depends a bit on the scope of this subworkflow. But if the plan is to support a wide range of functional analysis tools as suggested in nf-core/differentialabundance#367, it would be good to keep this generic.
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Agreed, but I'm always wary of premature over-engineering. I don't object to gmt in the first instance.
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Basically my suggestion comes down to making the gene sets + background method-specific. Already the current workflow logic doesn't really care about whether it's a gmt file or not. It's only specified in the comment.
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Do you think it is possible to standardize the gene set input format for all methods, and then each module deals with the reformatting to the proper format specifically needed for the method?
Otherwise I would imagine it become confusing from the pipeline's user perspective to have to provide the input gene set with certain format depending on the method chosen, etc.
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I see your point. It would be really nice to include omnipathdb, then the coupling between signature and method can be automatized based on name.
For now, we could have a method specific gene sets channel just as input channel.
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I'm not thrilled by the prospect of a multiplicity of gene_set inputs per method.
@suzannejin Maybe the gene sets should actually be part of ch_input. That way, there could be method-specific gene set files in that channel, and we wouldn't need multiple input channels