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Extracting relevant features from single-cell trajectories 🎯
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ℹ️ Tutorials

Calculating differentially expressed features across a trajectory

Included are methods to

  • Calculate the overall feature importance, using calculate_overall_feature_importance
  • Calculate the importance of a feature at a bifurcation point, using calculate_milestone_feature_importance

The plotting of the top features is nicely intergrated into dynplot

dynplot heatmap

Latest changes

Check out news(package = "dynwrap") or for a full list of changes.

Recent changes in dynfeature 1.0.0 (28-03-2019)

  • MINOR CHANGE: Use only one core by default.

  • MINOR CHANGE: Support sparse matrices

Recent changes in dynfeature 0.2.0 (25-10-2018)

  • SPEED UP: Added fi_ranger_rf_lite(), which scales much better w.r.t. the number of samples and features, at the cost of increasing loss of accuracy at higher dimension sizes.

  • MAJOR CHANGES: Large cleanup of the code. Most notably,

    • The format of feature importance method specification and its parameters, with format fi_method = fi_example_method(param1 = 10, param2 = 4). Before, it had to be specified as method = "example_method", method_params = list(param1 = 10, param2 = 4).
  • MINOR CHANGE: Whenever possible, output columns are now factors instead of characters.

  • MINOR CHANGE: Add NEWS, and add news section to README.

  • DOCUMENTATION: Turned on markdown for Roxygen.

  • DOCUMENTATION: Improved documentation on expression_source.

  • TESTING: Improved testing with a larger dataset, and will check whether the overall feature importance produces decent results.

  • MINOR CHANGE: Feature importance functions will always return factors instead of characters.

Dynverse dependencies

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