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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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immuneML & Galaxy¶

All of immuneMLs functionalities are also available through a Galaxy web interface as a collection of Galaxy tools. The immuneML Galaxy tools provide simplified, button-based interfaces to get started running different types of immuneML analyses, or can alternatively be run with a YAML input file to specify all parameters.

Galaxy tutorials

  • Introduction to Galaxy
  • immuneML Galaxy tools
  • ML basics: Training classifiers with the simplified Galaxy interface
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