Contents Menu Expand Light mode Dark mode Auto light/dark, in light mode Auto light/dark, in dark mode Skip to content
immuneML 3.0.3 documentation
immuneML 3.0.3 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
    • 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
  • 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
Back to top
View this page

AIRR-C Deep Dive TutorialΒΆ

This page contains resources for the immuneML Deep Dive Tutorial at the AIRR Community Meeting VI.

  • Example dataset: 100_receptors.tsv

  • Example YAML specification: slides_example.yaml

  • Extra example dataset: 100_extra_receptors.tsv

  • Example ML application YAML: ml_application.yaml

Copyright © 2021-2023, Milena Pavlovic, Lonneke Scheffer, Keshav Motwani, Victor Greiff, Geir Kjetil Sandve
Made with Sphinx and @pradyunsg's Furo