pandasaurus_cxg-0.1.2.tar.gz

# pandasaurus_cxg STATUS: early Beta A library for retreiving and leveraging the semantic context of ontogy annotation in [CxG standard](https://github.com/chanzuckerberg/single-cell-curation/blob/main/schema/3.0.0/schema.md) [AnnData files](https://anndata.readthedocs.io/en/latest/). Slide summarising intended functionality  ## Installation Available on [PyPi](https://pypi.org/project/pandasaurus-cxg/0.1.1/) $ pip3 install pandasaurus_cxg ## Usage The `AnndataEnricher` and `AnndataAnalyzer` classes can be used both individually and in conjunction with the `AnndataEnrichmentAnalyzer` wrapper class. The `AnndataEnrichmentAnalyzer` class serves as a convenient way to leverage the functionalities of both `AnndataEnricher` and `AnndataAnalyzer`. ### Using AnndataEnricher and AnndataAnalyzer Individually You can use the `AnndataEnricher` and `AnndataAnalyzer` classes separately to perform specific tasks on your data. For instance, `AnndataEnricher` facilitates data enrichment, while `AnndataAnalyzer` provides various analysis tools for Anndata objects. ```python from pandasaurus_cxg.anndata_enricher import AnndataEnricher ade = AnndataEnricher.from_file_path("test/data/modified_human_kidney.h5ad") ade.simple_enrichment() ade.minimal_slim_enrichment(["blood_and_immune_upper_slim"]) ``` ```python from pandasaurus_cxg.anndata_analyzer import AnndataAnalyzer ada = AnndataAnalyzer.from_file_path("./immune_example.h5ad", author_cell_type_list = ['subclass.full', 'subclass.l3', 'subclass.l2', 'subclass.l1', 'class', 'author_cell_type']) ada.co_annotation_report() ``` ### Using AnndataEnrichmentAnalyzer Wrapper The AnndataEnrichmentAnalyzer class wraps the functionality of both AnndataEnricher and AnndataAnalyzer, offering a seamless way to perform enrichment and analysis in one go. ```python from pandasaurus_cxg.enrichment_analysis import AnndataEnrichmentAnalyzer from pandasaurus_cxg.graph_generator.graph_generator import GraphGenerator aea = AnndataEnrichmentAnalyzer("test/data/modified_human_kidney.h5ad") aea.contextual_slim_enrichment() aea.co_annotation_report() gg = GraphGenerator(aea) gg.generate_rdf_graph() gg.set_label_adding_priority(["class", "cell_type", "subclass.l1", "subclass.l1", "subclass.full", "subclass.l2", "subclass.l3"]) gg.add_label_to_terms() gg.enrich_rdf_graph() gg.save_rdf_graph(file_name="kidney_new", _format="ttl") ``` More examples and detailed explanation can be found in jupyter notebook given in [Snippets](#Snippets) ## Snippets https://github.com/INCATools/pandasaurus_cxg/blob/roadmap/walkthrough.ipynb ## Roadmap https://github.com/INCATools/pandasaurus_cxg/blob/roadmap/ROADMAP.md