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immuneML 3.0.15 documentation
immuneML 3.0.15 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
    • How to train a generative model
  • 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
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YAML parameter detailsΒΆ

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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