Robustifying genomic classifiers to batch effects via ensemble learning.
Robustifying genomic classifiers to batch effects via ensemble learning.
复制标题
通过集成学习增强基因组分类器的批量效果。
DOI：
10.1093/bioinformatics/btaa986
复制
发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Parmigiani,Giovanni
中科院分区：
文献类型：
--
作者：
Zhang,Yuqing;Patil,Prasad;Johnson,WEvan;Parmigiani,Giovanni
动机由于实际的限制，基因组数据通常是分批生产的，这可能导致批次之间的差异导致数据出现不必要的变化。这种“批效应”往往对下游生物分析产生负面影响，需要慎重考虑。在实践中，批处理效果通常由专门设计的软件来解决，这些软件合并来自不同批次的数据，然后估计批处理效果并从数据中删除它们。在这里，我们关注分类和预测问题，并提出了一种基于集成学习的不同策略。我们首先在每个批次中建立预测模型，然后通过集合加权方法对它们进行整合。结果我们通过针对不同人群的结核病感染研究，对这两种策略进行了系统比较。在一项研究中，我们模拟了在研究的随机子集中增加的异质性水平，我们将其视为模拟批次。然后，我们使用这两种方法来开发疾病状态二元指标的基因组分类器。我们在另一项针对不同人群队列的独立研究中评估了预测的准确性。我们观察到，在独立验证中，合并后进行批量调整在低异质性水平下提供了更好的判别，而我们的集成学习策略实现了更稳健的性能，特别是在批次效应的高严重程度下。这些观察结果为处理基因组分类器开发和评估中的批处理效应提供了实用指南。可用性和实现本文的基础数据可在本文及其在线补充材料中获得。处理后的数据可在Github存储库中与实现代码一起获得，网址为https://github.com/zhangyuqing/bea_ensemble.Supplementary information。补充数据可在bioinformaticsonline上获得。
MotivationGenomic data are often produced in batches due to practical restrictions, which may lead to unwanted variation in data caused by discrepancies across batches. Such ‘batch effects’ often have negative impact on downstream biological analysis and need careful consideration. In practice, batch effects are usually addressed by specifically designed software, which merge the data from different batches, then estimate batch effects and remove them from the data. Here, we focus on classification and prediction problems, and propose a different strategy based on ensemble learning. We first develop prediction models within each batch, then integrate them through ensemble weighting methods.ResultsWe provide a systematic comparison between these two strategies using studies targeting diverse populations infected with tuberculosis. In one study, we simulated increasing levels of heterogeneity across random subsets of the study, which we treat as simulated batches. We then use the two methods to develop a genomic classifier for the binary indicator of disease status. We evaluate the accuracy of prediction in another independent study targeting a different population cohort. We observed that in independent validation, while merging followed by batch adjustment provides better discrimination at low level of heterogeneity, our ensemble learning strategy achieves more robust performance, especially at high severity of batch effects. These observations provide practical guidelines for handling batch effects in the development and evaluation of genomic classifiers.Availability and implementationThe data underlying this article are available in the article and in its online supplementary material. Processed data is available in the Github repository with implementation code, at https://github.com/zhangyuqing/bea_ensemble.Supplementary informationSupplementary data are available atBioinformaticsonline.
登录
查看更多内容
雄性仓鼠生殖内分泌学
影响因子：
--
作者：
A. Bartke
通讯作者：
A. Bartke
影响因子：
3.6
作者：
W. Neaves
通讯作者：
W. Neaves
影响因子：
4.8
作者：
DESJARDINS, C;EWING, LL;JOHNSON, BH
通讯作者：
JOHNSON, BH
影响因子：
4.8
作者：
G. Hung
通讯作者：
G. Hung
长时间暴露于非刺激性光周期对金仓鼠神经内分泌睾丸轴活动的影响。
影响因子：
3.6
作者：
Fred W. Turek;Jeffrey A. Elliott;Janet D. Alvis;Michael Menaker
通讯作者：
Michael Menaker