Impact of data preprocessing on cell-type clustering based on single-cell RNA-seq data.

Area of Science:

• Computational biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) enables cell type characterization.
• Clustering algorithms are widely used for scRNA-seq data analysis.
• Data preprocessing significantly impacts clustering performance, with no universal optimal method.

Purpose of the Study:

• To develop a method for selecting the best data preprocessing strategy for single-cell clustering.
• To improve the accuracy and performance of the SC3 clustering algorithm.

Main Methods:

• A graph-based algorithm, SC3-e, was designed.
• SC3-e was evaluated on eight diverse scRNA-seq datasets.
• The algorithm specifically targets the SC3 clustering method.

Main Results:

• SC3-e consistently identified the optimal data preprocessing method for SC3.
• The use of SC3-e significantly enhanced the clustering performance of SC3.
• The algorithm demonstrated robust performance across multiple datasets.

Conclusions:

• SC3-e is an effective tool for optimizing data preprocessing in scRNA-seq analysis.
• The algorithm substantially improves cell-type clustering accuracy using SC3.
• SC3-e is expected to be valuable for studies involving human complex diseases and novel cell type discovery.