nextflow.config

/*
 * -------------------------------------------------
 *  multi-omics-pipeline Nextflow config file
 * -------------------------------------------------
 * Default config options for all environments.
 * Cluster-specific config options is saved 
 * in the configs folder and imported under a profile
 * name here.
 */

 // Global default parameters
params {
    outdir          =   null ?: "results"
    pipeline_dir    =   null ?: "$projectDir"
    publish_relevant = true
    // Purposedly null to always require input
    allocation_code =  System.getenv('ALLOCATION_CODE') ?: "invalid allocation code"
    samplesheet     =   null
    data_dir        = "data/"
    help            =   false
    verbose         =   false
    // Testing purpose parameters
    debug           =   true
    apptainer_cache_dir = System.getenv('APPTAINER_IMAGE_CACHE_DIR') ?: "${HOME}/.apptainer/cache"
    // Max resource options
    // Defaults only, expecting to be overwritten
    max_memory      =   '128.GB'
    max_cpus        =   8
    max_time        =   '240.h'

    
    // Input parameters
    // Options to control behavior of workflows
    train               = true  // To false if just want to test the splitting and not go further at train stage
                                // This setting is higher than the one inside a .nf file
    // Param to control if minimal setting or some more thorough test
    // oneData			        = false // This overide minimal and use one data only
    // minimal			        = false
    // Options to run part of workflows
    
    // TODO: redundant parameters here
    runSimple           = false // Legacy param
    runInnerCV          = false // Default to run inner cv in all possible methods
    runAllMethods       = true  // Run all multiomics integration methods, overrides runPython or runR
    runPython           = true  // Run methods implemented in Python only
    runR                = true  // Run methods implemented in R only
    runDownstreamAnalysis   = false // Run downstream analysis of the methods on a full size dataset
    runPerformanceEvaluation = true // Run the cross validation on to measure methods classification performance
    // ===============================
    // Skip certain method or not
    skip_caret_multimodal = false
    skip_cplr           = false
    skip_diablo         = false
    skip_integrao       = false
    skip_demo_logit     = true
    skip_rgcca          = false
    skip_mogonet        = false
    skip_mofa           = false
    skip_goat           = true
    skip_sgmr           = true
    skip_sksurv         = true
    skip_sklearn        = true
    
    /*Parameters for methods*/
    // This parameter is for component based methods number of comps
    num_comps           = 2 // This is number of components , default to 1
    // Mogonet parameters
    he_base_dim         = 100 // Base dimension for the HE layer, default to 100 for real data, 1 for simulated data

    /* Prepare data related */
    /* Splitting related */
    split_type          = "skf"     // Type of splitting to perform, one of "skf" (stratified k fold) or 
                                    // or "sgkf" (stratidied group k fold ) "logo" (leave one group out) 
    k_fold_number       = 5         // Number of folds to generate (omitted if split type = logo)
    split_dir	        = "splits"  // Directory to splitted indices
    outcome_type        = "classification" // "classification" or "survival". Survival requires
                                            // colData/metadata columns: time (numeric days) and status (0/1)
    /* Survival task parameters (used when outcome_type = "survival") */
    time_col            = "time"    // colData column with follow-up time in days
    event_col           = "status"  // colData column with event indicator (0/1)
    /* Threshold to convert the predicted probability to class */
    threshold           = 0.5


    /* For feature selction workflow */
    selectFeature       = true  // Default runs feature selection on all possible methods
    n_percent           = 10    // N percent of features to be selected
    // Method specific params
    // DIABLO
    diablo_design_connection = ['full', 'null'] // connection for design matrix
    // SKLEARN
    single_modality_mode = false
    // Available classifiers
    sklearn_classifier_names = ['Logit', 'Random_Forest', 'MLP', 'XGBoost']
    sklearn_reduction = ["empty", "pca50"] // Preprocessing done to data
    // Survival model stuff
    survival_model_names = ["rsf"]
    // If sklearn wants to do single input mode, default: false
    sklearn_single_mode = false
    // For preprocessing data
    filter_low_var      = "0" // This treats as false in R, one of "1" or "0"
    // Resource parameters
    // Base size thresholds
    small_dataset_threshold = "50.MB"
    medium_dataset_threshold = "200.MB" 
    large_dataset_threshold = "1.GB"
    // Array sizes for different tasks
    gpu_array_size = 1 // Small arrays for GPU jobs
    cpu_generic_array_size = 1 // Generic CPU array size
    cpu_preprocess_array_size = 1 // Preprocessing array size
    cpu_training_array_size = 1 // Training array size
    cpu_prediction_array_size = 1 // Prediction array size
    feature_selection_array_size = 1 // Feature selection array size



}

// Widely-used environment variables
env {
    subworkflowDir    =   "$projectDir/subworkflows"
    modulesDir        =   "$projectDir/modules"
    configDir         =   "$projectDir/conf"
    PYTHONPATH        =   "${projectDir}/bin/python_utils"
}

// Load base.config by default for all pipelines
includeConfig 'conf/base.config'
// Load profiles
profiles {
  // These are for development purposes
    standard {
        process.executor = 'local'
        includeConfig 'conf/local.config' 
    }

    
    docker {
        // The profile name is just also docker (but not the nextflow.docker scope)
        // Note: this below refers to docker scope, so the prefix must exist
        docker.enabled		    = true
        docker.temp		        =   'auto'
        // This is temporal fix
        docker.runOptions	      =   "-u \$(id -u):\$(id -g) --entrypoint '' -v ${projectDir}:/home/rstudio/${projectDir}"
        apptainer.enabled = false
        singularity.enabled = false
    }
    
    apptainer {
        apptainer.enabled                 = true
        apptainer.autoMounts              = true
        apptainer.cacheDir                = params.apptainer_cache_dir
	apptainer.libraryDir		  = params.apptainer_cache_dir
        conda.enabled           = false
        docker.enabled          = false
        singularity.enabled     = false
        podman.enabled          = false
        shifter.enabled         = false
        charliecloud.enabled    = false
    }

    gpu {
        docker.runOptions       = '-u $(id -u):$(id -g) --gpus all'
        apptainer.runOptions    = '--nv'
        singularity.runOptions  = '--nv'
    }

    arc_local {
        process.executor    =   'local' 
        includeConfig 'conf/dev-sockeye.config' 
    }

    sockeye {
        includeConfig 'conf/sockeye.config'
    }


    /*
    These two profiles CANNOT be used together simultaneously
    One for real data
    One for simulated data
    */
    real_data {
	    includeConfig 'conf/real_data.config'
    }
    
    simulated_data {
	    includeConfig 'conf/simulated_data.config'
    }

    // For survival task
    run_survival {
	    includeConfig 'conf/run_survival.config'
    }

    // Legacy ones

    run_mogonet_only {
        includeConfig 'conf/run_mogonet_only.config'
    }

    /* 
        These are for testing the pipeline run
        - Use the minimal to pull containers on first run on LOGIN node
        - Use the full to run the pipeline on compute nodes from launch_MESSI_pipeline.sh instead
    */
    test { includeConfig 'conf/test.config' }
    test_full { includeConfig 'conf/test_full.config' }


}

// Process option cache and their shell option
process {
    cache = 'lenient'
    // Capture exit codes from upstream processes when piping
    shell = ['/bin/bash', '-euo', 'pipefail']
    
    /* 
        MODULE CONTAINER CONFIG
    */

    // Assigning module containers here
    
    withLabel: caret_multimodal { container = 'tonyliang19/caret_multimodal:latest' }
    withLabel: generic  { container = 'tonyliang19/save_simulate:latest' }
    withLabel: mogonet  { container = 'tonyliang19/mogonet:latest'       }
    withLabel: mofa     { container = 'tonyliang19/mofa:latest'          }
    withLabel: rgcca    { container = 'tonyliang19/rgcca:latest'         }
    withLabel: codia    { container = 'tonyliang19/codia:latest'         }
    withLabel: integrao { container = 'tonyliang19/integrao:latest'      }
    withLabel: sklearn  { container = 'tonyliang19/eipy:latest'          } // fix: should have a new sklearn container instead, for now use eipy bcz it contains XGBoost
    withLabel: sksurv   { container = 'tonyliang19/sksurv:latest'        }

}

// Pipeline information after exectuion
def trace_timestamp = new java.util.Date().format( 'yyyy-MM-dd_HH-mm-ss')
timeline {
  enabled = true
  file    = "${params.outdir}/pipeline_info/execution_timeline_${trace_timestamp}.html"
}
report {
  enabled = true
  file    = "${params.outdir}/pipeline_info/execution_report_${trace_timestamp}.html"
}
trace {
  enabled = true
  file    = "${params.outdir}/pipeline_info/execution_trace_${trace_timestamp}.txt"
  // This could be optional to provide
  fields  = 'process, tag, realtime, %cpu,%mem, rss, peak_rss, peak_vmem, duration, memory'
}
dag {
  enabled = true
  file    = "${params.outdir}/pipeline_info/pipeline_dag_${trace_timestamp}.html"
}

// Meta data information
manifest {
  name            =   'CompBio-Lab/MESSI-pipeline'
  author          =   """Tony Liang"""
  version         =   '1.0.0dev'
  homePage        =   'https://github.com/CompBio-Lab/MESSI-pipeline'
  description     =   """Benchmark Multiomics integration methods over single cell and spatial bulk data"""
  mainScript      =   'main.nf'
  nextflowVersion =   '>=24.10.2' // Minimal version to work
}

// Load modules.config for DSL2 module specific options
includeConfig 'conf/modules.config'

// Enable resources/usr/bin scripts, although they need executable permission
nextflow {
    enable.moduleBinaries = true
}


// Function to ensure that resource requirements don't go beyond
// a maximum limit
def check_max(obj, type) {
  if (type == 'memory') {
      try {
          if (obj.compareTo(params.max_memory as nextflow.util.MemoryUnit) == 1)
              return params.max_memory as nextflow.util.MemoryUnit
          else
              return obj
      } catch (all) {
          println "   ### ERROR ###   Max memory '${params.max_memory}' is not valid! Using default value: $obj"
          return obj
      }
  } else if (type == 'time') {
      try {
          if (obj.compareTo(params.max_time as nextflow.util.Duration) == 1)
              return params.max_time as nextflow.util.Duration
          else
              return obj
      } catch (all) {
          println "   ### ERROR ###   Max time '${params.max_time}' is not valid! Using default value: $obj"
          return obj
      }
  } else if (type == 'cpus') {
      try {
          return Math.min( obj, params.max_cpus as int )
      } catch (all) {
          println "   ### ERROR ###   Max cpus '${params.max_cpus}' is not valid! Using default value: $obj"
          return obj
      }
  }
}