Release Notes¶
Version 1.0¶
1.2.0 2021-02-02¶
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 nojax
is found when usingmode='stochastic'
incellrank.tl.kernels.VelocityKernel.compute_transition_matrix()
PR 472.Fix
pandas
v1.0.1
indexing error incellrank.tl.lineage_drivers()
PR 475.Fix not correctly propagating colors during aggregation in
cellrank.tl.Lineage
PR 482.
1.1.0 2020-11-17¶
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 ofcellrank.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.
1.0.0 2020-10-17¶
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 monadAllow returning models when doing bulk fitting
Add
transpose
parameter forcellrank.pl.gene_trends()
Various other minor bugfixes
1.0.0-rc.11 2020-09-25¶
Rename
metastable states
tomacrostates
Rename
final states
toterminal states
Fix pickling if
cellrank.tl.estimators.BaseEstimator
Fix various color bugs
Update gallery
Other various minor changes
1.0.0-rc.0 2020-07-15¶
Fix pickling of
cellrank.tl.Lineage
improvementsAdd additional options to
cellrank.pl.heatmap()
Updated documentation
1.0.0-b.8 2020-07-12¶
Add installation options for PETSc and SLEPc
Add iterative solver for absorption proabilities
Add minor
cellrank.tl.Lineage
improvementsFix docstring issues
1.0.0-b.2 2020-07-02¶
Fix installation by including future-fstrings.
1.0.0-b.1 2020-07-02¶
Initial beta pre-release.