# scAGCI: an anchor graph-based method for cell clustering from integrated scRNA-seq and scATAC-seq data

## TL;DR

This paper introduces scAGCI, a new method for clustering cells using both RNA and ATAC data, improving accuracy and efficiency in identifying cell types and subtypes.

## Contribution

scAGCI introduces dynamic anchor unification to better model high-order feature interactions in multi-omics data.

## Key findings

- scAGCI outperforms 13 existing methods in clustering performance and computational efficiency.
- The method preserves biologically meaningful patterns through marker gene enrichment and functional analysis.
- Dynamic anchor unification enhances the integration of scRNA-seq and scATAC-seq data.

## Abstract

Single-cell multi-omics clustering confronts noise and heterogeneity barriers. Current multi-view anchor graph approaches, though successful in noise reduction, inadequately model higher order feature interactions. To address this issue, we propose scAGCI, a cell clustering method based on anchor graphs that integrates both scRNA-seq and scATAC-seq data. Our method captures specific and shared anchor graphs representing the properties of omics data in the process of dynamic anchor unification, and mines high-order shared information to complete the omics representation. Subsequently, clustering results are obtained by integrating the specific and shared omics representation. Benchmarking against 13 state-of-the-art methods confirms scAGCI’s superior clustering performance and computational efficiency in cell-type identification and subtype resolution. The method preserves biologically meaningful omics patterns, as evidenced by marker gene enrichment and functional analyses, establishing it as a robust tool for elucidating cellular heterogeneity in single-cell multi-omics data.