scGate: marker-based purification of cell types from heterogeneous single-cell RNA-seq datasets

Area of Science:

• Bioinformatics
• Computational Biology
• Genomics

Background:

• Single-cell data analysis often requires isolating specific cell populations from complex, heterogeneous datasets.
• Current methods for cell purification can be labor-intensive and may require extensive training data or reference profiles.

Purpose of the Study:

• To present scGate, an automated algorithm for marker-based cell population purification in single-cell data.
• To provide a tool that does not require prior training data or reference gene expression profiles.

Main Methods:

• scGate employs a hierarchical gating strategy based on user-defined markers, similar to flow cytometry.
• The algorithm is implemented as an R package and integrates with the Seurat framework.
• It is applicable to various single-cell data modalities, including RNA-seq, ATAC-seq, and CITE-seq.

Main Results:

• scGate automates the purification of specific cell populations from heterogeneous single-cell datasets.
• The algorithm demonstrates superior performance compared to state-of-the-art single-cell classifiers.
• It offers an intuitive interface for researchers to isolate cell populations of interest.

Conclusions:

• scGate provides an efficient and automated solution for cell population purification in single-cell bioinformatics.
• Its flexibility across multiple data modalities and integration with existing frameworks make it a valuable tool for researchers.
• The package is readily available with reproducible workflows for diverse applications.