MarioniLab/miloR

R package implementation of Milo for testing for differential abundance in KNN graphs

What it solves

MiloR provides a method for differential abundance (DA) analysis on single-cell datasets. It allows researchers to identify which cell populations are significantly different in abundance between different experimental conditions, without relying on predefined cell-type clusters.");

How it works

The tool uses k-nearest neighbor (KNN) graphs to define neighborhoods of cells. Instead of clustering cells into fixed groups, it analyzes the abundance of cells within these overlapping neighborhoods across samples. It then applies statistical testing to determine if certain neighborhoods are differentially abundant between conditions.

Who it’s for

Bioinformaticians and biologists studying single-cell data who need to perform differential abundance testing to find changes in cell population proportions across different conditions.

Highlights

  • Graph-based approach: Uses KNN graphs to avoid the limitations of fixed clustering.

  • Mixed effect models: Supports the use of mixed effect models for dependent samples.

  • Integration: Can be integrated into scanpy/anndata workflows (via milopy).

  • Bioconductor compatibility: Available as a Bioconductor package for stable installation.

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