Major updates in Monocle 3

Monocle 3 has been re-engineered to analyze large, complex single-cell datasets. The algorithms at the core of Monocle 3 are highly scalable and can handle millions of cells. Monocle 3 adds some powerful new features that enable the analysis of organism- or embryo-scale experiments:

  • A better structured workflow to learn developmental trajectories.
  • Support for the UMAP algorithm to initialize trajectory inference.
  • Support for trajectories with multiple roots.
  • Ways to learn trajectories that have loops or points of convergence.
  • Algorithms that automatically partition cells to learn disjoint or parallel trajectories using ideas from "approximate graph abstraction".
  • A new statistical test for genes that have trajectory-dependent expression. This replaces both the old differentialGeneTest() function and BEAM().
  • Project query data set onto a reference.
  • Transfer annotations to query data set from reference.
  • Save and load Monocle objects and transformation models.
  • Mixed negative binomial distribution for fit_models.
  • A 3D interface to visualize trajectories and gene expression.

Most of the algorithmic details in Monocle 3 are described in Cao & Spielmann et al.

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