Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model.

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) reveals cellular heterogeneity but presents analysis challenges due to data variability.
• Existing clustering methods often require time-consuming parameter tuning, leading to inconsistent results.

Purpose of the Study:

• To develop a simple, robust, and efficient clustering method for scRNA-seq data analysis.
• To address the limitations of parameter-dependent clustering algorithms.

Main Methods:

• Proposed a novel regularized Gaussian graphical clustering (RGGC) method.
• RGGC utilizes high-order correlations and subspace learning, with robustness across a range of regularization parameters (λ).
• Integrated with the Louvain community detection algorithm for automatic cluster number determination, eliminating the need for manual parameter tuning.

Main Results:

• RGGC demonstrated computational efficiency and top performance on simulated and benchmark scRNA-seq datasets.
• The method proved robust, allowing fixed parameter settings (λ=2 or λ=log(p)) without cross-validation.
• Successfully detected inter-sample cell heterogeneity in glioblastoma scRNA-seq data.

Conclusions:

• RGGC offers a parameter-free, efficient, and robust solution for scRNA-seq data clustering.
• The method effectively identifies cell subpopulations and inter-sample heterogeneity.
• RGGC advances scRNA-seq data analysis, enabling more reliable discovery of cellular diversity.