main.nf

nextflow.enable.dsl=2

// Include subworkflows
include { PREPARE }     from './subworkflows/prepare'
include { DECONVOLVE } from './subworkflows/deconvolve'
include { ASSESS }      from './subworkflows/assess'

// Main workflow
workflow {
    // Convert tool names to canonical case
    def mapping = [
        'bayesprism':'BayesPrism',
        'dwls'      :'DWLS',
        'grood'     :'GrooD',
        'music'     :'MuSiC',
        'scaden'    :'Scaden'
    ]
    def toolsList = (params.tools instanceof List) ? params.tools : params.tools?.toString()?.split(/\s*,\s*/)?.findAll { it } ?: []
    def displayTools = toolsList.collect { t ->
        def s = t?.toString()?.trim()
        if (!s) {
            s
        } else {
            mapping.get(s.toLowerCase(), s)
        }
    }
    def tools_str = displayTools.join(',')
    
    // Log parameters
    log.info """\
    DECONVOLUTION BENCHMARKING PIPELINE
    ===================================
    Input samplesheet               : ${params.input}
    Output directory                : ${params.output_dir}
    Tools to benchmark              : ${tools_str}
    """.stripIndent()

    // Parse the samplesheet into a channel of jobs
    def jobs_ch = Channel
        .fromPath(params.input, checkIfExists: true)
        .splitCsv(header:true)
        .map { row ->
            def bulkPath  = file(row.bulk)
            def scPath    = file(row.sc_ref)
            def hasTruth  = (row.containsKey('truth') && row.truth)
            def truthPath = hasTruth ? file(row.truth) : null
            def truthBase = hasTruth ? truthPath.baseName : 'notruth'
            [
                tag  : "${bulkPath.baseName}_${scPath.baseName}_${truthBase}",
                bulk : bulkPath,
                sc   : scPath,
                truth: truthPath
            ]
        }

    // Run preparation subworkflow
    def prepared_ch    = PREPARE(jobs_ch)

    // Run deconvolution subworkflow
    def predictions_ch = DECONVOLVE(prepared_ch)

    // Run assessment subworkflow
    ASSESS(predictions_ch, prepared_ch)
}