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.

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.

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