This version introduces quality-of-life changes to the graphical user interface, functions for translating gene IDs and running differential expression analysis, and extends RNAlysis to support Python versions 3.9 and 3.10.
Added the visualization function ma_plot() for CountFilter
Added functions for the normalization functions Relative Log Ratio (RLE), Trimmed Mean of M-values (TMM), Median of Ratios (MRN), Quantile normalization (quantile)
CountFilter.normalize_to_rpm() was renamed to CountFilter.normalize_to_rpm_htseqcount(), and was supplemented by the more general function for normalizing to Reads Per Million CountFilter.normalize_to_rpm()
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.
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.
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 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.
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.
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.
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.