Efficient and scalable Python implementation of ANCOM-BC and ANCOM-BC2 for omics differential abundance testing.

Python implementations of ANCOM-BC and ANCOM-BC2 achieve 380- and 147-fold faster runtimes with over 96% lower memory use on large datasets.

• Why it matters: Handling high-dimensional multi-omics data with existing R packages can cause computational bottlenecks, limiting analysis of large-scale datasets and complex models.
• What they did: Native Python versions were developed using NumPy and SciPy for vectorization and optimization, ensuring efficiency without sacrificing statistical accuracy, and validated against original R results.
• The result: These implementations enable analysis of significantly larger datasets, broadening applications to diverse omics data types and complex statistical models, thus advancing large-scale microbiome research.