# Release Notes¶

## Version 1.0¶

### 1.3.1 2021-04-09¶

Bugfixes

Fix estimator’s lineages color/names mismatch when reading from

`anndata.AnnData`

PR 556.Remove heuristics used to determine which solver to use PR 558.

### 1.3.0 2021-03-29¶

This release includes some major additions which make CellRank more applicable with and without RNA velocity information. In particular, it includes:

Additions

Add new kernel

`cellrank.tl.kernels.CytoTRACEKernel`

which computes cell-cell transition probabilities based on the CytoTRACE score [Gulati*et al.*, 2020], a measure of differentiation potential, PR 527.Add external API

`cellrank.external`

with a stationary optimal transport kernel`cellrank.external.kernels.OTKernel`

contributed from [Zhang*et al.*, 2021], as well as a contributing guide, PR 522.Rename

`cellrank.tl.kernels.PalantirKernel`

to`cellrank.tl.kernels.PseudotimeKernel`

and add hard thresholding scheme inspired by [Setty*et al.*, 2019], a soft thresholding scheme inspired by [Stassen*et al.*, 2021] and a custom scheme when computing the transition matrix, see e.g.`cellrank.tl.kernels.SoftThresholdScheme`

PR 514.Add more flexibility to

`cellrank.tl.kernels.ConnectivityKernel`

, allowing it to use any cell-cell similarities from`anndata.AnnData.obsp`

, such as spatial similarities from`squidpy`

[Palla*et al.*, 2021] PR 501.Revamp Pancreas Advanced tutorial to showcase CellRank’s modular structure of kernels and estimators. PR 32.

Add 2 new tutorials:

Beyond RNA velocity: shows how to use CellRank when no RNA velocity information is available. PR 32

Creating a new kernel: explains how to create your own custom kernel class that estimates cell-cell transition probabilities PR 31.

Add projection of transition matrix onto an embedding

`cellrank.tl.kernels.Kernel.compute_projection()`

Add random walk simulation and visualization in an embedding

`cellrank.tl.kernels.Kernel.plot_random_walks()`

PR 537.Add

`cellrank.tl.Lineage.priming_degree()`

PR 502 which estimates a cell’s plasticity/differentiation potential based on ideas by [Setty*et al.*, 2019] and [Velten*et al.*, 2017].Add checks for transition matrix irreducibility PR 516.

Add Zebrafish development dataset from [Farrell

*et al.*, 2018] PR 539.Speed-up stationary distribution calculation in

`pygpcca`

PR 22.

Bugfixes

### 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 [Velten*et al.*, 2017], 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.

### 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 [Li*et al.*, 2021] 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 [Biddy*et al.*, 2018].

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 for`cellrank.pl.gene_trends()`

Various minor bugfixes

### 1.0.0-rc.11 2020-09-25¶

Rename

`final states`

to`terminal 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`

Add 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 probabilities

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