# robin2: accelerating single-cell data clustering evaluation

## TL;DR

Robin2 is a new tool for evaluating clustering in single-cell RNA sequencing data, offering faster and more efficient analysis of cell subpopulations.

## Contribution

Robin2 introduces enhanced computational efficiency and scalability for clustering evaluation in high-dimensional single-cell RNA sequencing data.

## Key findings

- Robin2 reduces computational time by 9-fold on large-scale datasets using parallel processing.
- Application to Tabula Muris and PBMC datasets confirmed identification of biologically meaningful cell subpopulations with high statistical significance.

## Abstract

The rapid expansion of single-cell RNA sequencing (scRNA-seq) technologies has increased the need for robust and scalable clustering evaluation methods. To address these challenges, we developed robin2, an optimized version of our R package robin. It introduces enhanced computational efficiency, support for high-dimensional datasets, and harmonious integration with R’s base functionalities for robust network analysis.

robin2 offers improved functionality for clustering stability validation and enables systematic evaluation of community detection algorithms across various resolutions and pipelines. The application to Tabula Muris and PBMC scRNA-seq datasets confirmed its ability to identify biologically meaningful cell subpopulations with high statistical significance. The new version reduces computational time by 9-fold on large-scale datasets using parallel processing.

The robin2 package is freely available on CRAN at https://CRAN.R-project.org/package=robin. Comprehensive documentation and a detailed analysis vignette are available on GitHub at https://drighelli.github.io/scrobinv2/index.html.