Release Notes

Release Notes#

1.6.1#

Version fixing for scanpy (>=1.10) and python (<3.14).

1.6.0#

From now on, crested requires a python>=3.11 installation.

Features#

  • It’s now possible to import crested without having a tf or torch installation. This is useful if you only need the pp, io, datasets, or plotting functionality. A missing backend error is only shown on crested.tl import.

  • crested now imports lazily, meaning that the required functions only get imported when accessing them (as well as the torch/tf backend). This makes crested import much faster if you don’t need its predicting or training functions.

  • Now matches the latest scverse cookiecutter template (e.g. using “hatch” for running unittests)

  • memelite dependency is moved out of default installation to an optional dependency (see README.md), as this is only used in tfmodisco with tomtom

  • Store target start/end if using target_region_widths (so that there’s a record in the anndata where the output values are from)

  • Allow for a list/dict of files in import_bigwigs and import_beds rather than only a direct dir (to let you use files scattered across multiple directories, or a subset of bigwigs in a directory, or custom class names w/ a dict)

  • Add point downsampling and parallelization to scatter.class_density()

  • Change temp file creation while importing bigwigs (allows for importing bigwigs in parallel)

Bugfixes#

  • now use public function keras.config.backend() rather than keras.src.backend.config.backend() everywhere

  • Always use region names in crested.tl.predict to unify with model training side

  • Make get_model() and get_dataset() case-insensitive

  • Check for mismatches between genome, bed file, and bigwig files when reading in peak heights or tracks from bigwigs.

  • Raise warning if negative values are detected when reading in tracks or peak heights

  • Allow for use of a single string for a single model name in all plotting functions that take model_name (this previously gave a hard-to-parse error, since it looped over the individual letters in the string)

  • Add serialisation decorators to custom attention classes

Documentation#

  • Updated preprint citations to published papers

  • Updated CREsted citation from Zenodo to preprint

  • Added explicit data source block for models trained pretty directly on external datasets

  • Clarified tissue of BICCN models

  • Removed Chrombpnet citation in dilatedCNN documentation (by request of the authors)

  • Add tutorial for working with custom models and explain keras 3 flexibility in CREsted.

  • Fixed other small typos etc

1.5.0#

Features#

  • Faster sequence loading (updated crested.Genome class and _transform_input)

  • Better handling of very large inputs in crested.tl.predict by using a generator instead of copying everything to GPU first.

  • Added options for window shuffling ISM as well as option to plot ISM as letters

  • memelite update and removed tangermeme dependency

  • Add bbox_inches=tight to render_plot (small breaking)

  • New validation logic for highlight_positions parameters in locus_scoring to make errors more clear.

1.4.1#

Small release with the new models for the Hydropv2 papers.

Features#

  • new models in the model repository from the Hydropv2 paper

    • Embryo10x

    • EmbryoHydrop

    • MouseCortexHydrop

  • new argument option in crested.pl.heatmap.correlations_self and crested.pl.heatmap.correlations_predictions to order plot on class similarity

1.4.0#

This is the version that is released together with the preprint of the CREsted paper.

Features#

  • new models in the model repository from the CREsted paper

    • DeepCCL

    • DeepGlioma

    • DeepPBMC

    • DeepZebraFish

    • BorzoiBICCN

    • DeepBICCN2

  • Some new flexibility to the Borzoi model architectures.

  • Chrombpnet architecture is renamed to DilatedCNN to avoid confusion with the Chrombpnet framework (with backwards compatibility).

  • Updates to contribution scores calculations to reduce memory consumption and clean up function structure.

  • Now possible to choose a custom model save path when calling fit(...).

  • Borzoi peak regression notebook added.

  • New crested.pl.violin plotting module and added crested.pl.violin.correlations.

Bugfixes#

  • Fixed a bug that causes models to not be serialized correctly during saving, making them unable to load in compile=True mode.

1.3.0#

This is a big release wherein we introduce our model repository and do a functional refactoring of our tl.Crested class.

Features#

  • new crested.get_model function to fetch models from crested model repository

  • new enformer and borzoi model options in the crested zoo, as well as scripts for converting weights to keras format.

  • new cut site bigwigs option for mouse cortex dataset

  • new crested model repository in the readthedocs with model descriptions.

  • new pattern clustering plot pl.patterns.clustermap_with_pwm_logos that shows PWM logo plots below the heatmap.

  • Extra parameters options in modisco calculations and plotting regarding allowed seqlets per cluster and top_n_regions selection.

  • option to color lines in gene locus scoring plotting

  • extra ylim option in crested.pl.bar.prediction

  • gene locus scoring plotting improvements

Bugfixes#

  • importing bigwigs now correctly accounts for regions that were removed due to chromsizes

Notebooks#

  • Rewrote the tutorials to use the new functional API (WIP).

  • Expanded on the enhancer design section

Functional Refactor of crested.tl.Crested(…) class#

In this large refactor we’re moving everything from the Crested class that does not use both a model and the AnnDatamodule out to a new _tools.py module where everything will be functional. All the old functions remain in the Crested class for backward compatibility (for now) but will now raise a deprecation warning.

We’re giving up a bit of clarity for ease of use by combining functions that do the same on different inputs into one single function.

Equivalent new functions#

  • tl.Crested.get_embeddings(...) —> tl.extract_layer_embeddings(...)

  • tl.Crested.predict(...) —> tl.predict(...)

  • tl.Crested.predict_regions(...) —> tl.predict(...)

  • tl.Crested.predict_sequence(...) —> tl.predict(...)

  • tl.Crested.score_gene_locus(...) —> tl.score_gene_locus(...)

  • tl.Crested.calculate_contribution_scores(...) —> tl.contribution_scores(...)

  • tl.Crested.calculate_contribution_scores_regions(...) —> tl.contribution_scores(...)

  • tl.Crested.calculate_contribution_scores_sequence(...) —> tl.contribution_scores(...)

  • tl.Crested.calculate_contribution_scores_enhancer_design(...) —> tl.contribution_scores(...)

  • tl.Crested.tfmodisco_calculate_and_save_contribution_scores_sequences —> tl.contribution_scores_specific(...)

  • tl.Crested.tfmodisco_calculate_and_save_contribution_scores —> tl.contribution_scores(...)

  • tl.Crested.enhancer_design_motif_implementation —> tl.enhancer_design_motif_insertion

  • tl.Crested.enhancer_design_in_silico_evolution —> tl.enhancer_design_in_silico_evolution

New functions#

Some utility functions were hidden inside the Crested class but required to be made explicit.

  • utils.calculate_nucleotide_distribution (advised for enhancer design)

  • utils.derive_intermediate_sequences (required for inspecting intermediate results from enhancer design)

New behaviour#

  • All functions that accept a model can now also accept lists of models, in which case the results will be averaged across models.

  • All functions use a similar api, namely they expect some ‘input’ that can be converted to a one hot encoding (sequences, region names, anndatas with region names), but now the conversion happens behind the scenes so the user doesn’t have to worry about this and we don’t have a separate function per input format.

1.2.1#

Bug Fixes#

1.2.0#

Features#

Tutorials#

  • Introductory notebook now fully reproducible

Bug Fixes#

  • crested.tl.Crested.get_embeddings now correctly updates .varm if anndata is passed instead of .obsm.

  • Tangermeme moved out of optional dependencies for tf vs torch breaking mismatches.

  • Fixed calculation of contribution scores with torch backend when using incompatible numpy version.

  • Fix incorrect None return in Crested.test()

Breaking Changes#

  • If providing the same project_name and run_name, the Crested class will now assume that you want to continue training from existing checkpoints.

  • ‘genome_file’ argument name everywhere updated to ‘genome’

1.1.0#

Features#

  • enhancer design plotting and calculation functions in the crested.tl.Crested object

  • new module crested.tl.modisco with enhancer code analysis functionality

  • new plots for enhancer code analysis in crested.pl.patterns

  • new plot for sequence prediction in crested.pl.bar

  • hidden utility functions now public in crested.utils (such as one hot encoding)

  • CosineMSELog loss now default in peak_regression configs instead of CosineMSE

  • tangermeme tomtom option in tfmodisco functions

  • get_embeddings based on layer name in crested.tl.Crested

  • crested.utils.EnhancerOptimizer utility function

  • crested.utils.permute_model utility, useful for using with other toch packages

  • crested.utils.extract_bigwig_values_per_bp utility

  • improved correlation calculation speed in heatmaps

Tutorials#

  • introductory notebook is updated with new functionality

  • new enhancer code analysis tutorial

Bug Fixes#

  • transfer Learning function now doesn’t expect the dense block anymore

  • bigwigs importing checks if it can open the file instead of looking for .bw extension

  • no more ambiguous names in bed importing

  • version requirement for modiscolite

  • general pattern matching code bugfixes

Breaking Changes#

  • utility functions and logging moved to crested.utils

  • tfmodisco functionality moved to crested.tl.modisco

Other#

  • general documentation fixes

  • style checks now enforced on push

  • both tf and torch backend tests on push

1.0.0#

  • Initial release of CREsted

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