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Updated: May 5, 2026
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Identification of Circular RNAs using RNA Sequencing
Published on: November 14, 2019
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Procrustes是一种机器学习方法,可以从临床RNA测序数据中删除跨平台批量效应
Nikita Kotlov1, Kirill Shaposhnikov1, Cagdas Tazearslan1
1BostonGene, Corp., Waltham, MA, 02453, USA.
Communications biology
|March 30, 2024
概括
我们开发了一种新的RNA测序协议和机器学习算法Procrustes,以标准化基因表达数据. 这提高了对外体捕获和个性化瘤学的多-A RNA-seq方法之间的可比性.
科学领域:
- 基因组学就是基因组学.
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 个性化瘤学依赖于基因表达分析.
- 来自不同协议的RNA测序 (RNA-seq) 数据的标准化 (外因子捕获[EC]和多A) 对于准确的比较至关重要.
- 固定在形式氨酸,嵌入氨酸的样本对RNA-seq.提出了独特的挑战.
研究的目的:
- 优化基于EC的RNA-seq协议,用于固定在甲中的,嵌入在中的样品.
- 开发一个机器学习算法 (Procrustes) 来纠正EC和多ARNA-seq数据之间的批量效应.
- 为了提高跨不同RNA-seq协议的基因表达测量的可比性.
主要方法:
- 为FFPE样本开发和优化了一个基于EC的RNA-seq协议.
- 创建了一个机器学习算法,Procrustes,用于批量效应校正.
- 应用Procrustes对来自EC和poly-A协议的RNA-seq数据,并与其他方法进行基准测试.
主要成果:
- 甲虫显著改善了EC和多A协议之间的基因表达相关性,从26%提高到61% (N=20,062).
- 癌症特异性和微环境基因的相关性从36%增加到84% (N=1,438).
- Procrustes的表现优于其他批次校正方法,并且可以将单个样本数据投射到更大的队列中.
结论:
- 优化的EC协议和Procrustes算法提高了RNA-seq数据的可比性.
- Procrustes有效地减轻了批量效应,提高了基因表达分析的准确性.
- 这种方法支持基于基因表达的治疗决策在个性化瘤学,通过使单个瘤样本的强有力的分析.
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