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在独立研究中对RNA-Seq数据预处理管道进行转录基因预测的比较
Richard Van1,2, Daniel Alvarez3,2, Travis Mize4
1School of Life Sciences, University of Nevada Las Vegas, Las Vegas, NV, USA.
BMC bioinformatics
|May 9, 2024
概括
对于RNA测序 (RNA-Seq) 的数据预处理方法可以影响癌症分类. 虽然批量效应校正在某些情况下改善了原产组织的预测,但在其他情况下它恶化了性能,表明预处理并不总是有益的.
科学领域:
- 在瘤学瘤学.
- 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:
- RNA测序 (RNA-Seq) 和机器学习 (ML) 实现了癌症分子分类.
- 整合多实验室数据集用于癌症预测因数据变化和噪音而具有挑战性.
- 数据预处理旨在协调数据集,以提高ML模型性能.
研究的目的:
- 研究数据预处理 (规范化,批量效应校正,缩放) 对癌症组织起源预测的影响.
- 使用大规模RNA-Seq数据集改进交叉研究预测.
- 评估预处理对癌症分类ML模型性能的影响.
主要方法:
- 对数据预处理技术的比较分析.
- 应用规范化,批量效应校正和数据缩放.
- 在来自大型患者队伍的RNA-Seq数据上训练ML模型.
主要成果:
- 数据预处理选择显著影响了原产地组织预测的分类器性能.
- 批量效应校正提高了GTEx数据集的预测准确性.
- 在聚合的ICGC和GEO数据集上进行预处理降低了性能.
结论:
- 批量效应校正可以在特定场景中改善基于RNA-Seq的癌症分类.
- 数据预处理并不普遍有益,并且可能阻碍性能.
- 仔细考虑预处理步骤对于癌症研究中强大的ML管道至关重要.
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