Kernelized partial least squares for feature reduction and classification of gene microarray data.
Kernelized partial least squares for feature reduction and classification of gene microarray data.
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DOI：
10.1186/1752-0509-5-s3-s13
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发表时间：
2011
影响因子：
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通讯作者：
Deng Y
中科院分区：
文献类型：
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作者：
Land WH;Qiao X;Margolis DE;Ford WS;Paquette CT;Perez-Rogers JF;Borgia JA;Yang JY;Deng Y
本文的主要目标是： 1.) 通过消除那些对肺部“最佳”染色体生物标志物无贡献的特征（或探针），将统计学习理论（SLT），特别是偏最小二乘法（PLS）和核化PLS（K-PLS）应用于通用的“特征丰富/案例贫乏”（也称为“大p小n”或“高维、低样本量”）微阵列问题癌症，以及 2.) 定量测量和验证（通过独立方式）该 PLS 过程的功效。第二个目标是将诊断和预后生物医学应用的这些重大改进整合到临床研究领域。也就是说，设计一个框架，将 SLT 结果转换为直接、有用的临床信息，用于患者护理或药物研究。因此，我们提出并初步评估了一种将 PLS、K-PLS 和支持向量机 (SVM) 与公认且易于理解的传统生物统计“黄金标准”、Cox 比例风险模型和 Kaplan-Meier 生存分析方法相结合的过程。具体来说，这种新组合将通过 PLS 和 Kaplan-Meier 以及 PLS 和 Cox 风险比 (CHR) 进行说明，并且可以轻松扩展到 K-PLS 和 SVM 范式。最后，这些先前描述的过程包含在我们整体特征缩减/评估过程的精细特征选择（FFS）组件中，该组件由以下组件组成：1.）粗略特征缩减，2.）精细特征选择和3.）分类（如本文所述）和预测。我们的 PLS 和 K-PLS 结果表明，作为我们整体特征缩减过程的一部分，这些技术在噪声微阵列数据上表现良好。最佳表现是 36 个月之前或之后复发分类的接受者操作特征 (ROC) 曲线下面积 (AUC) 为 0.794，对于 60 个月之前或之后复发分类为 0.869 AUC。分类组的 Kaplan-Meier 曲线明显分开，36 个月和 60 个月的 p 值均低于 4.5e-12。 CHR 也很好，比率为 2.846341（36 个月）和 3.996732（60 个月）。 PLS和K-PLS等SLT技术可以有效解决分析微阵列等生物医学数据的难题。本文展示的与已建立的生物统计技术的结合使这些方法能够从学术研究转向临床实践。
The primary objectives of this paper are: 1.) to apply Statistical Learning Theory (SLT), specifically Partial Least Squares (PLS) and Kernelized PLS (K-PLS), to the universal "feature-rich/case-poor" (also known as "large p small n", or "high-dimension, low-sample size") microarray problem by eliminating those features (or probes) that do not contribute to the "best" chromosome bio-markers for lung cancer, and 2.) quantitatively measure and verify (by an independent means) the efficacy of this PLS process. A secondary objective is to integrate these significant improvements in diagnostic and prognostic biomedical applications into the clinical research arena. That is, to devise a framework for converting SLT results into direct, useful clinical information for patient care or pharmaceutical research. We, therefore, propose and preliminarily evaluate, a process whereby PLS, K-PLS, and Support Vector Machines (SVM) may be integrated with the accepted and well understood traditional biostatistical "gold standard", Cox Proportional Hazard model and Kaplan-Meier survival analysis methods. Specifically, this new combination will be illustrated with both PLS and Kaplan-Meier followed by PLS and Cox Hazard Ratios (CHR) and can be easily extended for both the K-PLS and SVM paradigms. Finally, these previously described processes are contained in the Fine Feature Selection (FFS) component of our overall feature reduction/evaluation process, which consists of the following components: 1.) coarse feature reduction, 2.) fine feature selection and 3.) classification (as described in this paper) and prediction. Our results for PLS and K-PLS showed that these techniques, as part of our overall feature reduction process, performed well on noisy microarray data. The best performance was a good 0.794 Area Under a Receiver Operating Characteristic (ROC) Curve (AUC) for classification of recurrence prior to or after 36 months and a strong 0.869 AUC for classification of recurrence prior to or after 60 months. Kaplan-Meier curves for the classification groups were clearly separated, with p-values below 4.5e-12 for both 36 and 60 months. CHRs were also good, with ratios of 2.846341 (36 months) and 3.996732 (60 months). SLT techniques such as PLS and K-PLS can effectively address difficult problems with analyzing biomedical data such as microarrays. The combinations with established biostatistical techniques demonstrated in this paper allow these methods to move from academic research and into clinical practice.