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单细胞时代的特征选择再探讨。

Feature selection revisited in the single-cell era.

机构信息

School of Mathematics and Statistics, University of Sydney, Sydney, NSW, 2006, Australia.

Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, NSW, 2145, Australia.

出版信息

Genome Biol. 2021 Dec 1;22(1):321. doi: 10.1186/s13059-021-02544-3.

Abstract

Recent advances in single-cell biotechnologies have resulted in high-dimensional datasets with increased complexity, making feature selection an essential technique for single-cell data analysis. Here, we revisit feature selection techniques and summarise recent developments. We review their application to a range of single-cell data types 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 and finally consider their scalability and make general recommendations on each type of feature selection method. We hope this review stimulates future research and application of feature selection in the single-cell era.

摘要

单细胞生物技术的最新进展产生了具有更高复杂性的高维数据集,使得特征选择成为单细胞数据分析的重要技术。在这里,我们回顾了特征选择技术,并总结了最新的进展。我们综述了它们在一系列传统细胞检测和成像技术以及最新的单细胞组学技术产生的单细胞数据类型中的应用。我们强调了一些挑战和未来的方向,最后考虑了它们的可扩展性,并对每种特征选择方法提出了一般性建议。我们希望这篇综述能激发单细胞时代特征选择的未来研究和应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dc84/8638336/7bb14a5914a3/13059_2021_2544_Fig1_HTML.jpg

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