Orthogonal projection correction for confounders in biological data classification

Pub Date : 2015-08-01 DOI:10.1504/IJDMB.2015.071553
Limin Li, Shuqin Zhang
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引用次数: 2

Abstract

The existence of confounders such as population structure in genome-wide association study makes it difficult to apply machine learning methods directly to solve biological problems. It is still unclear how to effectively correct confounders. In this work, we propose an Orthogonal Projection Correction (OPC) method to correct confounders. This is achieved by orthogonally decomposing each feature to a confounding component and a non-confounding component, such that the original data can be best reconstructed by only the non-confounding components of features. The confounder space is built based on prior knowledge, and each feature is projected to its orthogonal complement space. This OPC procedure is shown to be kernelisable. We then propose a ProSVM method by integrating the OPC method and support vector machine for classification. In the experiments, our OPC method for confounder correction improves the tumour diagnosis based on samples from different labs and phenotype prediction in the presence of population structure.
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生物数据分类中混杂因素的正交投影校正
在全基因组关联研究中,由于群体结构等混杂因素的存在,使得机器学习方法难以直接应用于解决生物学问题。目前还不清楚如何有效地纠正混杂因素。在这项工作中,我们提出了一种正交投影校正(OPC)方法来校正混杂。这是通过将每个特征正交分解为一个混杂成分和一个非混杂成分来实现的,这样只有特征的非混杂成分才能最好地重建原始数据。基于先验知识构建混杂空间,并将每个特征投影到其正交补空间。这个OPC程序是可内核化的。然后,我们提出了一种结合OPC方法和支持向量机进行分类的provm方法。在实验中,我们用于混杂校正的OPC方法改进了基于不同实验室样本的肿瘤诊断和存在群体结构的表型预测。
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