Introduction

STATUS: early Beta

A library for retrieving and leveraging the semantic context of ontology 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 ![image](https://github.com/INCATools/pandasaurus_cxg/assets/112839/3082dcd2-dd2f-469d-9076-4eabcc83130d)

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.

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"])

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.

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

https://github.com/INCATools/pandasaurus_cxg/blob/roadmap/walkthrough.ipynb

Roadmap

https://github.com/INCATools/pandasaurus_cxg/blob/roadmap/ROADMAP.md