Release Notes¶

Version 1.0¶

This release includes:

Additions

Completely refactored the underlying code base of GPCCA and set it up as it’s own package called pyGPCCA with documentation and an example. Going forwards, this will ensure that one of the “engines” of CellRank is also easy to maintain to extend. Further, this will make CellRank’s installation more convenient by not needing to vendorize additional dependencies PR 472.
Add cellrank.pl.circular_projection() visualizing computed fate probabilities as done in [Velten17], see Plot circular embedding. PR 459.
Allow legends not to be plotted by passing legend_loc="none", as done in scVelo PR 470.

Bugfixes

Fix a bug when computing the Schur decomposition for reducible Markov chains (Schur vectors appear to not be D-orthogonal). GPCCA requires the leading Schur vectors to be orthogonal w.r.t. a symmetric, positive definite matrix \(D\) PR 453.
Fix not falling back to mode='monte_carlo' if no jax is found when using mode='stochastic' in cellrank.tl.kernels.VelocityKernel.compute_transition_matrix() PR 472.
Fix pandas v1.0.1 indexing error in cellrank.tl.lineage_drivers() PR 475.
Fix not correctly propagating colors during aggregation in cellrank.tl.Lineage PR 482.

This release includes:

Additions

cellrank.tl.lineage_drivers() computes p-values for the identified driver genes now, using either a Fisher-transformation to approximate the distribution of the test statistic under the null hypothesis or an exact, permutation based test. Corrects for multiple-testing.
cellrank.tl.kernels.VelocityKernel.compute_transition_matrix() now allows different metrics to be used to compare velocity vectors with expression-differences across neighboring cells. We add cosine-correlation and dot-product schemes and we allow the user to input their own scheme. It has been shown recently by [Li2020] that the choice of metric can lead to slightly different results. Users can now also supply their own scheme as long as it follows the signature of cellrank.tl.kernels.SimilaritySchemeABC.
cellrank.datasets.reprogramming() has been added to allow for easy reproducibility of the time & memory benchmarking results in our CellRank preprint. This is a reprogramming dataset from [Morris18].

Bugfixes

Fix not vendorizing correct msmtools which sometimes caused densification of a sparse matrix.
Bump scanpy version requirement to 1.6 to fix plotting PR 444.

Fix a bug when subsetting cellrank.tl.Lineage
Add renaming terminal states cellrank.tl.estimators.BaseEstimator.rename_terminal_states()
Enable negative binomial distribution for cellrank.ul.models.GAMR
Remove previously deprecated functions
Add cellrank.ul.models.FailedModel inspired by the maybe monad
Allow returning models when doing bulk fitting
Add transpose parameter for cellrank.pl.gene_trends()
Various other minor bugfixes