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CHANGELOG.rst

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Changelog

All notable changes to this project will be documented in this file.

The format is based on Keep a Changelog.

0.4.6

Fixed

  • Fixed bug in AbstractEpistasis.preferences with returnformat of 'tidy'. Previously the wildtype was set incorrectly for missing values.

0.4.5

Added

  • The new AbstractEpistasis.single_mut_effects method.
  • Options returnformat and stringency_param to AbstractEpistasis.preferences and utils.scores_to_prefs.

Changed

  • AbstractEpistasis.preferences and utils.scores_to_prefs return site as integer.

0.4.4

Fixed

  • Errors related to using pandas.query for nan values. Not sure of the cause, but the errors are fixed now.

0.4.3

Changed

  • Eliminated the default log base for conversion of scores / phenotypes. This is because base 2 gave excessively flat preferences, and the choice of a base is something that the user should need to think about. Added explanation about the consequences of this choice to docs and examples.
  • The preferenes returned by scores_to_prefs and AbstractEpistasis.preferences are now naturally sorted by site.

0.4.2

Added

  • The new AbstractEpistasis.preferences method gets amino-acid preferences from phenotypes.
  • Added utils.scores_to_prefs.

0.4.1

Fixed

  • The isplines module now uses a simple dict-implemented cache rather than methodtools.lru_cache. This fixes excess memory usage and allows objects to be pickled.
  • AbstractEpistasis internally clears the cache via __getstate__ to reduce size of pickled objects. This avoids pickled models being huge. Also added the clearcache option to AbstractEpistasis.fit to serve a similar purpose of memory savings.

0.4.0

Added

  • Added additional forms of likelihood function to the global epistasis models. This involves substantial re-factoring the epistasis models in globalepistasis. In particular, the MonotonicSplineEpistasis and NoEpistasis classes no longer are fully concrete subclasses of AbstractEpistasis. Instead, there are also likelihood calculation subclasses (GaussianLikelihood and CauchyLikelihood), and the concrete subclasses inherit from both an epistasis function and likelihood calculation subclass. So for instance, what was previously MonotonicSplineEpistasis (with Gaussian likelihood assumed) is now MonotonicSplineEpistasisGaussianLikelihood. Note that this an API-breaking change.
  • Added the narrow_bottleneck.ipynb notebook to demonstrate use of the Cauchy likelihood for analysis of experiments with a lot of noise.
  • Added the predict_variants.ipynb to demonstrate prediction of variant phenotypes using global epistasis models.
  • Added simulate.codon_muts.

Fixed

  • Some minor fixes to codonvariat_sim_data.ipynb.

0.3.0

Added

  • Added utils.tidy_to_corr.
  • Added binarymap module.
  • Added globalepistasis module.
  • Added ispline module.

Changed

  • Order of rows in data frames from CodonVariantTable.func_scores.
  • Updated codonvariant_sim_data.ipynb to be smaller and fit global epistasis models, and move plot formatting examples to a new dedicated notebook.
  • Changed SigmoidPhenotypeSimulator so that the enrichment is a sigmoidal function of the latent phenotype, and the observed phenotype is the log (base 2) of the latent phenotype. This change harmonizes the simulator with the definitions in the new globalepistasis module. Also changed the input to the latentPhenotype and observedPhenotype methods. Note that these are backwards-compatibility breaking changes.

Fixed

  • Removed use of deprecated Bio.Alphabet

0.2.0

Added

  • Capabilities to parse barcodes from Illumina data: FASTQ readers and IlluminaBarcodeParser.
  • CodonVariantTable.numCodonMutsByType method to get numerical values for codon mutations per variant.
  • Can specify names of columns when initializing a CodonVariantTable.
  • CodonVariantTable.func_scores now takes libraries rather than combine_libs argument.
  • Added CodonVariantTable.add_sample_counts_df method.
  • Added CodonVariantTable.plotVariantSupportHistogram method.
  • Added CodonVariantTable.avgCountsPerVariant and CodonVariantTable.plotAvgCountsPerVariant methods.
  • Add custom plotnine theme in plotnine_themes and improved formatting of plots from CodonVariantTable.
  • Added sample_rename parameter to CodonVariantTable plotting methods.
  • Added syn_as_wt to CodonVariantTable.classifyVariants.
  • Added random_seq and mutate_seq to simulate module.

Changed

  • Changed how variant_call_support set in simulate_CodonVariantTable.
  • Better xlimits on CodonVariantTable.plotCumulMutCoverage.

Fixed

  • Docs /formatting in Jupyter notebooks.
  • Fixed bugs that arose when pandas updated to 0.25 (related to groupby no longer dropping empty categories).
  • Bugs in CodonVariantTable histogram plots when samples set.

0.1.0

Initial release. Ported code from dms_tools2 and made some improvements.