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immuneML 3.0.14 documentation
immuneML 3.0.14 documentation
  • Quickstart
    • Quickstart: Galaxy with button-based tools
    • Quickstart: Galaxy with YAML-based tools
    • Quickstart: command-line interface with YAML
    • LIgO simulation quickstart
  • Installing immuneML
    • Install immuneML with a package manager
    • Setting up immuneML with Docker
    • Running immuneML in the cloud
  • YAML specification
    • How to specify an analysis with YAML
    • Dataset parameters
    • Encoding parameters
    • ML method parameters
    • Report parameters
    • Preprocessing parameters
    • Simulation parameters
    • Instruction parameters
  • Tutorials
    • Analyzing Your Own Dataset
    • How to import data into immuneML
    • How to generate a dataset with random sequences
    • Dataset simulation with LIgO
      • YAML specification of the LigoSim instruction for introducing immune signals
      • How to simulate co-occuring immune signals
      • Paired chain simulations in LIgO
      • Simulation with custom signal functions
    • How to train and assess a receptor or repertoire-level ML classifier
    • How to apply previously trained ML models to a new dataset
    • How to perform an exploratory data analysis
    • How to find motifs associated with disease or antigen binding state
      • Discovering positional motifs using precision and recall thresholds
      • Discovering motifs learned by classifiers
      • Recovering simulated immune signals
      • Comparing baseline motif frequencies in repertoires
    • How to perform clustering analysis
  • immuneML & Galaxy
    • Introduction to Galaxy
    • immuneML Galaxy tools
    • ML basics: Training classifiers with the simplified Galaxy interface
  • Use case examples
    • Manuscript use case 1: Reproduction of a published study inside immuneML
    • Manuscript use case 2: Extending immuneML with a deep learning component for predicting antigen specificity of paired receptor data
    • Manuscript use case 3: Benchmarking ML methods on ground-truth synthetic data
    • Integration use case: post-analysis of sequences with Immcantation
    • Integration use case: post-analysis of sequences with immunarch
    • Integration use case: Performing analysis on immuneSIM-generated repertoires
  • Troubleshooting
  • Developer documentation
    • Information for new developers
    • Set up immuneML for development
    • How to add a new encoding
    • How to add a new machine learning method
    • How to add a new report
    • How to add a new preprocessing
    • immuneML data model
    • immuneML execution flow
    • immuneML
      • immuneML package
        • immuneML.IO package
          • immuneML.IO.dataset_export package
          • immuneML.IO.dataset_import package
          • immuneML.IO.ml_method package
        • immuneML.analysis package
          • immuneML.analysis.criteria_matches package
          • immuneML.analysis.data_manipulation package
          • immuneML.analysis.entropy_calculations package
        • immuneML.api package
          • immuneML.api.aggregated_runs package
          • immuneML.api.galaxy package
        • immuneML.app package
        • immuneML.caching package
        • immuneML.data_model package
          • immuneML.data_model.datasets package
        • immuneML.dev_util package
        • immuneML.dsl package
          • immuneML.dsl.definition_parsers package
          • immuneML.dsl.import_parsers package
          • immuneML.dsl.instruction_parsers package
          • immuneML.dsl.semantic_model package
          • immuneML.dsl.symbol_table package
        • immuneML.encodings package
          • immuneML.encodings.abundance_encoding package
          • immuneML.encodings.atchley_kmer_encoding package
          • immuneML.encodings.deeprc package
          • immuneML.encodings.distance_encoding package
          • immuneML.encodings.evenness_profile package
          • immuneML.encodings.kmer_frequency package
            • immuneML.encodings.kmer_frequency.sequence_encoding package
          • immuneML.encodings.motif_encoding package
          • immuneML.encodings.onehot package
          • immuneML.encodings.preprocessing package
          • immuneML.encodings.protein_embedding package
          • immuneML.encodings.reference_encoding package
          • immuneML.encodings.word2vec package
            • immuneML.encodings.word2vec.model_creator package
        • immuneML.environment package
        • immuneML.example_weighting package
          • immuneML.example_weighting.predefined_weighting package
        • immuneML.hyperparameter_optimization package
          • immuneML.hyperparameter_optimization.config package
          • immuneML.hyperparameter_optimization.core package
          • immuneML.hyperparameter_optimization.states package
          • immuneML.hyperparameter_optimization.strategy package
        • immuneML.ml_methods package
          • immuneML.ml_methods.classifiers package
          • immuneML.ml_methods.clustering package
          • immuneML.ml_methods.dim_reduction package
          • immuneML.ml_methods.generative_models package
          • immuneML.ml_methods.pytorch_implementations package
          • immuneML.ml_methods.util package
        • immuneML.ml_metrics package
        • immuneML.pairwise_repertoire_comparison package
        • immuneML.preprocessing package
          • immuneML.preprocessing.filters package
        • immuneML.presentation package
          • immuneML.presentation.html package
        • immuneML.reports package
          • immuneML.reports.clustering_method_reports package
          • immuneML.reports.clustering_reports package
          • immuneML.reports.data_reports package
          • immuneML.reports.encoding_reports package
          • immuneML.reports.gen_model_reports package
          • immuneML.reports.ml_reports package
          • immuneML.reports.multi_dataset_reports package
          • immuneML.reports.train_gen_model_reports package
          • immuneML.reports.train_ml_model_reports package
        • immuneML.simulation package
          • immuneML.simulation.dataset_generation package
          • immuneML.simulation.implants package
          • immuneML.simulation.simulation_strategy package
          • immuneML.simulation.util package
        • immuneML.util package
        • immuneML.workflows package
          • immuneML.workflows.instructions package
            • immuneML.workflows.instructions.apply_gen_model package
            • immuneML.workflows.instructions.clustering package
            • immuneML.workflows.instructions.dataset_generation package
            • immuneML.workflows.instructions.exploratory_analysis package
            • immuneML.workflows.instructions.ligo_sim_feasibility package
            • immuneML.workflows.instructions.ligo_simulation package
            • immuneML.workflows.instructions.ml_model_application package
            • immuneML.workflows.instructions.subsampling package
            • immuneML.workflows.instructions.train_gen_model package
          • immuneML.workflows.steps package
            • immuneML.workflows.steps.data_splitter package
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YAML parameter details¶

The different components used inside an immuneML analysis are called definitions. These analysis components are used inside workflows called instructions. The following pages document all possible parameters of each of the definitions and instructions in great detail.

Parameter details

  • Dataset parameters
  • Encoding parameters
  • ML method parameters
  • Report parameters
  • Preprocessing parameters
  • Simulation parameters
  • Instruction parameters
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