Feature selection revisited in the single-cell era

TL;DR

This paper reviews recent advances in feature selection techniques tailored for high-dimensional single-cell data, emphasizing their applications, challenges, and future directions in the context of emerging biotechnologies.

Contribution

It provides a comprehensive overview of feature selection methods adapted for single-cell data, highlighting recent developments and offering guidance for future research.

Findings

Summarizes diverse feature selection techniques for single-cell data.

Highlights challenges in scalability and application.

Provides recommendations for future research directions.

Abstract

Feature selection techniques are essential for high-dimensional data analysis. In the last two decades, their popularity has been fuelled by the increasing availability of high-throughput biomolecular data where high-dimensionality is a common data property. Recent advances in biotechnologies enable global profiling of various molecular and cellular features at single-cell resolution, resulting in large-scale datasets with increased complexity. These technological developments have led to a resurgence in feature selection research and application in the single-cell field. Here, we revisit feature selection techniques and summarise recent developments. We review their versatile application to a range of single-cell data types including those generated from traditional cytometry and imaging technologies and the latest array of single-cell omics technologies. We highlight some of the challenges and future directions on which feature selection could have a significant impact. Finally, we consider the scalability and make general recommendations on the utility of each type of feature selection method. We hope this review serves as a reference point to stimulate future research and application of feature selection in the single-cell era.