IKAP-Identifying K mAjor cell Population groups in single-cell RNA-sequencing analysis

Area of Science:

• Computational Biology
• Genomics
• Bioinformatics

Background:

• Single-cell RNA sequencing (scRNA-seq) analysis involves distinct clustering and differential gene expression steps to identify cell types.
• The interdependence between clustering and differential expression analysis can create bottlenecks, necessitating iterative parameter tuning.
• Current methods often require manual adjustments to achieve biologically relevant cell groupings.

Purpose of the Study:

• To develop an automated algorithm, IKAP, for efficient cell group identification and differentiation in scRNA-seq data.
• To improve the accuracy and biological relevance of cell identity determination by optimizing clustering parameters.
• To accelerate the analysis pipeline for scRNA-seq data.

Main Methods:

• IKAP algorithm systematically tunes clustering parameters to identify major cell groups.
• The algorithm was tested on peripheral blood mononuclear cell datasets and a mouse cortex dataset.
• Recursive application of IKAP was used to identify cell subtypes within major cell types.

Main Results:

• IKAP successfully identified major cell types (T cells, B cells, NK cells, monocytes) in human and mouse datasets using default parameters.
• Cell groups identified by IKAP exhibited more distinguishing differentially expressed genes compared to conventional methods.
• IKAP enabled the delineation of cell subtypes, creating a multi-layered cell ontology.

Conclusions:

• IKAP enhances the automation of scRNA-seq analysis by optimizing clustering for improved cell group differentiation.
• The algorithm facilitates the discovery of distinguishing differentially expressed genes, refining cell identity.
• IKAP contributes to a more efficient and accurate multi-layered cell ontology construction from scRNA-seq data.