PyLiger

PyLiger integrates single-cell multi-omic datasets in Python to enable scalable joint analysis across transcriptomics, epigenomics, and proteomics.

Key Features:

• Performance Efficiency: Provides a reported 2-5× computational speedup relative to the R predecessor for single-cell multi-omic integration tasks.
• Interoperability with AnnData: Supports the AnnData format for annotated single-cell datasets to enable integration within existing Python-based workflows.
• Flexible Analysis Modes: Offers both on-disk and in-memory analysis options, with on-disk capability enabling processing of large datasets with fixed memory usage.
• Multi-omic Integration: Integrates data across transcriptomics, epigenomics, and proteomics to preserve dataset-specific features during joint analysis.
• Gene Ontology Enrichment Analysis: Provides functionality for gene ontology enrichment analysis to interpret biological processes and pathways from integrated results.

Scientific Applications:

• Single-cell multi-omic integration: Jointly analyzes transcriptomic, epigenomic, and proteomic layers to study relationships across molecular modalities.
• Cellular heterogeneity analysis: Supports investigations of cell-type diversity and state variation within complex tissues using integrated multi-omic profiles.
• Functional interpretation: Enables pathway and biological-process interpretation of integrated datasets via gene ontology enrichment analysis.

Methodology:

Implements algorithms to infer genomic experimental relationships across different omic layers and supports on-disk and in-memory computation with AnnData interoperability.