Predicting lung aging using scRNA-Seq data.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2024-12-19 eCollection Date: 2024-12-01 DOI:10.1371/journal.pcbi.1012632

Qi Song, Alex Singh, John E McDonough, Taylor S Adams, Robin Vos, Ruben De Man, Greg Myers, Laurens J Ceulemans, Bart M Vanaudenaerde, Wim A Wuyts, Xiting Yan, Jonas Schupp, James S Hagood, Naftali Kaminski, Ziv Bar-Joseph

引用次数: 0

Abstract

Age prediction based on single cell RNA-Sequencing data (scRNA-Seq) can provide information for patients' susceptibility to various diseases and conditions. In addition, such analysis can be used to identify aging related genes and pathways. To enable age prediction based on scRNA-Seq data, we developed PolyEN, a new regression model which learns continuous representation for expression over time. These representations are then used by PolyEN to integrate genes to predict an age. Existing and new lung aging data we profiled demonstrated PolyEN's improved performance over existing methods for age prediction. Our results identified lung epithelial cells as the most significant predictors for non-smokers while lung endothelial cells led to the best chronological age prediction results for smokers.

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使用scRNA-Seq数据预测肺老化。

基于单细胞rna测序数据（scRNA-Seq）的年龄预测可以为患者对各种疾病和病症的易感性提供信息。此外，这种分析还可以用于识别与衰老相关的基因和途径。为了实现基于scRNA-Seq数据的年龄预测，我们开发了PolyEN，这是一种新的回归模型，可以随着时间的推移学习表达的连续表示。然后，PolyEN使用这些表征来整合基因以预测年龄。我们分析的现有和新的肺老化数据表明，PolyEN的性能优于现有的年龄预测方法。我们的研究结果表明，肺上皮细胞是非吸烟者最重要的预测因子，而肺内皮细胞对吸烟者的实际年龄预测效果最好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

期刊介绍： PLOS Computational Biology features works of exceptional significance that further our understanding of living systems at all scales—from molecules and cells, to patient populations and ecosystems—through the application of computational methods. Readers include life and computational scientists, who can take the important findings presented here to the next level of discovery. Research articles must be declared as belonging to a relevant section. More information about the sections can be found in the submission guidelines. Research articles should model aspects of biological systems, demonstrate both methodological and scientific novelty, and provide profound new biological insights. Generally, reliability and significance of biological discovery through computation should be validated and enriched by experimental studies. Inclusion of experimental validation is not required for publication, but should be referenced where possible. Inclusion of experimental validation of a modest biological discovery through computation does not render a manuscript suitable for PLOS Computational Biology. Research articles specifically designated as Methods papers should describe outstanding methods of exceptional importance that have been shown, or have the promise to provide new biological insights. The method must already be widely adopted, or have the promise of wide adoption by a broad community of users. Enhancements to existing published methods will only be considered if those enhancements bring exceptional new capabilities.