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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

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Published on: October 25, 2018

Feature selection revisited in the single-cell era.

Pengyi Yang^1,2,3, Hao Huang^4,5, Chunlei Liu⁵

¹School of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia. pengyi.yang@sydney.edu.au.

|December 1, 2021

Summary

Feature selection is crucial for analyzing complex single-cell data. This review covers recent developments, applications, challenges, and recommendations for feature selection methods in the single-cell era.

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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Area of Science:

Single-cell biology
Bioinformatics
Data science

Background:

Single-cell biotechnologies generate high-dimensional, complex datasets.
Feature selection is vital for effective single-cell data analysis.
Existing techniques require re-evaluation in the context of new technologies.

Purpose of the Study:

To revisit and summarize recent developments in feature selection techniques.
To review the application of these techniques across various single-cell data types.
To provide recommendations and highlight future directions for feature selection in single-cell research.

Main Methods:

Comprehensive literature review of feature selection methods.
Analysis of applications in cytometry, imaging, and omics technologies.
Evaluation of scalability and challenges.

Main Results:

Identified key advancements in feature selection algorithms.
Demonstrated applicability across diverse single-cell data modalities.
Highlighted challenges related to data complexity and scalability.

Conclusions:

Feature selection is indispensable for advancing single-cell data analysis.
Further research is needed to address scalability and emerging challenges.
This review offers guidance for selecting appropriate methods in the single-cell era.