Rnalysis

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* This version fixes a bug with displaying the tutorial videos in the graphical user interface.

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* This version introduces a graphical user interface for RNAlysis, as well as new functions for KEGG Pathways enrichment analysis.


Added
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* RNAlysis now includes a graphical user interface
* Pipelines can now be imported and exported
* Enrichment and single-set-enrichment for KEGG pathway data

Changed
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* Added function FeatureSet.user_defined_enrichment(), which will replace FeatureSet.enrich_hypergeometric() and FeatureSet.enrich_randomization()
* Updated signature of enrichment.venn_diagram
* enrichment.venn_diagram and enrichment.upset_plot can now be generated on a user-supplied FIgure
* Clustering functions now apply a power transform to count data prior to clustering by default
* Non-deprecated enrichment functions no longer filter the background set by biotype by default
* Changed signature of CountFilter.pca, CountFilter.box_plot, CountFilter.enhanced_box_plot, CountFilter.clustergram, and CountFilter.pairplot to ensure consistency among visualization functions.

Fixed
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* enrichment.venn_diagram can now be plotted with outlines when the circles are unweighted
* Fixed bug in Pipeline.apply_to() where a Filter object would be returned even when the Pipeline was applied inplace

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* This version fixes issues with running GO enrichment that resulted from recent changes to UniProt's API. Moreover, this version slightly improves the performance of some functions.

Changed
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* Fixed issues with running GO enrichment that resulted from changes to UniProt's API.
* Some functions that fetch annotations now cache their results, leading to improved runtimes.
* Updated the documentation of some functions to better reflect their usage and input parameters.

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* This version introduces multiple new features, as well as generally improved graphs and quality-of-life changes.

Added
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* GO enrichment can now generate Ontology Graphs for the statistically significant GO terms.
* Added CountFilter.split_clicom(), an implementation of the CLICOM ensemble-based clustering method (Mimaroglu and Yagci 2012).
* Added Filter.transform(), a method that can transform your data tables with either predefined or user-defined transformations.

Changed
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* CountFilter.pairplot() now uses a logarithmic scale by default.
* Visually improved the graphs generated by many functions, including CountFilter.pairplot() and CountFilter.plot_expression().
* The clusters resulting from all clustering functions are now sorted by size instead of being sorted randomly.

Fixed
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* Minor bug fixes.

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* This version introduces small bug fixes, as well as a new function in the Filtering module.

Added
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* Added Filter.majority_vote_intersection(), which returns a set/string of the features that appear in at least (majority_threhold * 100)% of the given Filter objects/sets.

Changed
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* When mapping/inferring taxon IDs during GO enrichment analysis, organisms will now be prioritized based on their taxon ID values (numerically lower IDs will be considered to be more relevant).

Fixed
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* Fixed bug that occured when mapping/inferring taxon IDs during GO enrichment analysis, where integer taxon IDs would be matched by name similarity before trying an exact ID match, leading to spurious matches.
* Fixed bug that occursed when plotting clustering results with style='all' on Python 3.8.

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* This version introduces new method to cluster your read count matrices, including K-Means/Medoids clustering, Hierarchical clustering, and HDBSCAN.
* This version introduces many new ways to perform enrichment analysis and to visualize your results, including highly customizable GO Enrichment, enrichment based on ranked lists of genes, and enrichment for non-categorical attributes.
* This version introduces Pipelines - a quicker and more convenient way to apply a particular analysis pipeline to multiple Filter objects.
* This version improves the performance of many functions in RNAlysis, and in particular the performance of randomization tests.
* This version includes changes to names and signatures of some functions in the module, as elaborated below.


Added
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* Added class Pipeline to filtering module, which applies a series of filter functions to specified Filter objects.
* Added CountFilter.split_kmeans(), CountFilter.split_kmedoids(), CountFilter.split_hierarchical() and CountFilter.split_hdbscan(), which split your read count matrices into clusters with similar expression patterns.
* Added class RankedSet to enrichment module, which accepts a ranked list of genes/features, and can perform single-list enrichment analysis
* Added RankedSet.single_set_enrichment(), which can perfofm single-list enrichment analysis of user-defined attributes using XL-mHG test (see `Eden et al. (PLoS Comput Biol, 2007) <https://dx.doi.org/10.1371/journal.pcbi.0030039>`_ and `Wagner (PLoS One, 2015) <https://dx.doi.org/10.1371/journal.pone.0143196>`_ ).
* Added FeatureSet.go_enrichment() and RankedSet.single_set_go_enrichment(), which let you compute Gene Ontology enrichment for any organism of your choice, and filter the GO annotations used according to your preferences.
* Added FeatureSet.enrich_hypergeometric(), which can perform enrichment analysis using the Hypergeometric Test.
* Added more visualization functions, such CountFilter.enhanced_box_plot().
* Added FeatureSet.change_set_name(), to give a new 'set_name' to a FeatureSet object.


Changed
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* FeatureSet.enrich_randomization_parallel() was deprecated. Instead, you can compute your enrichment analysis with parallel computing by calling FeatureSet.enrich_randomization() with the argument 'parallel_processing=True'. Moreover, parallel session will now start automatically if one was not already active.
* Improved running time of enrich_randomization() about six-fold.
* Filter objects can be created from any delimiter-separated file format (.csv, .tsv, .txt, etc).
* CountFilter.pca() can now be plotted without labeled points.
* Filter.index_string is now sorted by the current order of indices in the Filter object, instead of by alphabetical order.
* CountFilter.violin_plot() now accepts a y_title argument.
* Added more optional arguments to visualization functions such as CountFilter.violin_plot() and CountFilter.clustergram().
* Automatic filenames for Filter objects should now reflect more clearly the operations that were performed.
* The DataFrame returned by enrich_randomization() and enrich_randomization_parallel() now contains the additional column 'data_scale', determined by the new optional argument 'data_scale'.
* The columns 'n obs' and 'n exp' in the DataFrame returned by enrich_randomization() and enrich_randomization_parallel() were renamed to 'obs' and 'exp' respectively.
* FeatureSets no longer support in-place set operations (intersection, union, difference, symmetric difference). Instead, these functions return a new FeatureSet.
* Filter.biotypes_from_ref_table() now accepts the boolean parameter 'long_format' instead of the str parameter 'format'.
* Filter.biotypes_from_ref_table() and FeatureSet.biotypes_from_ref_table() now count features which do not appear in the Biotype Reference Table as '_missing_from_biotype_reference' instead of 'not_in_biotype_reference'.

Fixed
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* Updated type-hinting of specific functions.
* Filter.biotypes_from_ref_table() and FeatureSet.biotypes_from_ref_table() now support Biotype Reference Tables with different column names.
* Generally improved performance of RNAlysis.
* Fixed bug in Filter.filter_percentile() where the value at the exact percentile speficied (e.g. the median for percentile=0.5) would be removed from the Filter object.
* Fixed bug in enrichment.FeatureSet, where creating a FeatureSet from input string would result in an empty set.
* Various minor bug fixes.