COM.on C.A.6:e4/26-29   Online published on Jan.17, 2012.
doi:10.4236/coca.2012.61004

REVIEW
Variable Selection and Dimensional Reduction in Genetic Analysis

Kelin Xu

MOE Key Laboratory of Contemporary Anthropology, School of Life-sciences, Fudan University, Shanghai 200433, China

ABSTRACT: In the context of the development of next generation sequencing, a large amount of genetic data is accumulated. We usually focus on the extraction of meaningful information from these data, considering the problem of computation cost and the challenges in traditional statistical method. Consequently, variable selection and dimension reduction play important roles in genomics. Here, I reviewed some popular methods referring to that, classifying them to three main types along with their properties and application fields.

Key words: Genetic data, high dimension hazard, variable selection, dimension reduction

遗传数据中的变量选择与降维

徐珂琳

复旦大学 生命科学学院 现代人类学教育部重点实验室, 上海200433

摘要：随着新一代测序技术与芯片杂交技术的发展，海量高维数据涌现在研究者们的面前，如何从这些高维数据中提取有效信息成为摆在人们面前的一大难题。在这种高维问题的背景下，许多基于低纬度的统计结论不再成立；另外，庞大的数据量对计算速度提出了很高的要求。于是，在这种数据驱动的研究背景下，变量选择与降维成为主要的研究方向。本文从当下遗传数据的特点出发，回顾了当今几种主流的变量选择与降维方法，如主成分分析、偏最小二乘回归、切片逆回归、LASSO等，并就这几种方法的性质与适用范围展开讨论。

关键词：遗传数据；高维问题；变量选择；降维

 

收稿日期：2011年12月7日  修回日期：2011年12月11日 联系人：徐珂琳 xukelin0202@gmail.com

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