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Single-Cell Clustering Based on Shared Nearest Neighbor and Graph Partitioning.

Xiaoshu Zhu^1,2, Jie Zhang², Yunpei Xu¹

¹Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 410083, Hunan, China.

Interdisciplinary Sciences, Computational Life Sciences

|February 23, 2020

Summary

This summary is machine-generated.

Structural Shared Nearest Neighbor-Louvain (SSNN-Louvain) improves cell subtype discovery from single-cell RNA sequencing data. This novel method enhances graph-based clustering by integrating graph structure information and community detection for higher accuracy.

Keywords:

Clustering Louvain community detection Shared nearest neighbor Similarity Single-cell RNA-seq

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Area of Science:

Computational Biology
Bioinformatics
Genomics

Background:

Single-cell RNA sequencing (scRNA-seq) clustering is crucial for identifying cell subtypes and understanding disease mechanisms.
Existing graph-based clustering methods often rely on k-nearest neighbor (KNN) and shared nearest neighbor (SNN) graphs, neglecting graph structure information.
Accurate edge weighting is essential for effective graph-based clustering in scRNA-seq analysis.

Purpose of the Study:

To develop a novel method, Structural Shared Nearest Neighbor-Louvain (SSNN-Louvain), for improved scRNA-seq data clustering.
To integrate graph structure information and community detection into a single framework for enhanced clustering accuracy.
To address limitations of existing SNN-based methods by incorporating structural graph properties.

Main Methods:

SSNN-Louvain defines edge weights based on the ratio of shared nearest neighbors to nearest neighbors, incorporating graph structure.
A modified Louvain community detection algorithm is employed to identify cell subtypes (modules) within the graph.
The method integrates SNN graph properties with community detection without requiring additional parameter tuning beyond neighbor count.

Main Results:

SSNN-Louvain demonstrated superior performance compared to five existing methods across 16 real scRNA-seq datasets.
The approach achieved the best average performance, indicating its robustness and effectiveness in cell subtype identification.
Experimental results validate the integration of graph structure and community detection for improved clustering accuracy.

Conclusions:

SSNN-Louvain offers a significant advancement in scRNA-seq data clustering by effectively leveraging graph structure information.
The method provides a robust and accurate approach for discovering cell subtypes, aiding disease mechanism analysis.
SSNN-Louvain represents a valuable tool for the bioinformatics community, enhancing the interpretability of scRNA-seq data.