scICE: enhancing clustering reliability and efficiency of scRNA-seq data with multi-cluster label consistency...

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Clustering analysis is crucial for single-cell RNA sequencing (scRNA-seq) data interpretation.
• Stochastic processes in algorithms lead to clustering inconsistency, compromising reliability.
• Existing consensus clustering methods are computationally expensive for large scRNA-seq datasets.

Purpose of the Study:

• To develop a computationally efficient method for evaluating clustering consistency in scRNA-seq data.
• To provide a reliable and consistent clustering result for large-scale scRNA-seq analysis.
• To reduce the computational burden and improve the robustness of scRNA-seq data analysis.

Main Methods:

• Development of the single-cell Inconsistency Clustering Estimator (scICE).
• Evaluation of scICE's speed and performance against conventional consensus clustering methods (multiK, chooseR).
• Application of scICE to 48 real and simulated scRNA-seq datasets, including those with over 10,000 cells.

Main Results:

• scICE achieves up to a 30-fold improvement in speed compared to existing methods.
• The method successfully identifies consistent clustering results across diverse datasets.
• scICE substantially narrows down the number of clusters requiring further exploration.

Conclusions:

• scICE offers a fast and reliable solution for clustering inconsistency in scRNA-seq data.
• The method enables researchers to focus on more robust candidate clusters, reducing computational load.
• scICE enhances the overall efficiency and reliability of scRNA-seq data analysis.