高维生物学数据两阶段组合降维策略研究  

Two-stage Combinational Dimension Reduction Strategy for Analyzing High-dimensional Data in Biology Field

作　　者：荀鹏程[1,2] 钱国华[3] 赵杨[1,2] 于浩[1,2] 陈峰[1,2] 

机构地区：[1]南京医科大学公共卫生学院流行病与卫生统计学系,210029 [2]南京医科大学现代毒理学教育部重点实验室,210029 [3]昆山市卫生监督所,215300

出　　处：《中国卫生统计》2012年第5期626-629,共4页Chinese Journal of Health Statistics

基　　金：国家自然科学基金(81072389;30901232);江苏省高校自然科学研究重大项目(10KJA330034)资助;江苏高校优势学科建设工程资助

摘　　要：目的探讨高维生物学数据的多阶段组合降维策略。方法以微阵列数据的判别分析为例,采用实际数据和模拟数据相结合的方法,提出"初步选维→进一步降维"的两阶段组合降维策略,并与后续的"判别→验证"相结合,形成了"选维→降维→判别→验证"的判别分析思路。以后续判别分析的预测效果、预测结果的稳定性与敏感性等为指标,对2种单一降维(PCA,PLS)方法和4种组合降维方法(PCA+SIR、PCA+SAVE、PLS+SIR和PLS+SAVE)进行了考察。结果从判别模型的预测效果、预测结果的稳定性及敏感性来看,PLS优于PCA,PLS+SIR/SAVE的组合降维效果更佳。结论用t计分法选维,以"PLS+SIR/SAVE"法进行降维的两阶段组合降维策略,对于微阵列数据判别分析,是实用的、可行的。Objective To explore multi-stage combinational dimension reduction strategy for analyzing high-dimensional data in biology field. Methods Two-stage combinational strategy incorporated in a four- step procedure, i. e. ＂variable pre-selection→further dimensionality reduction→discrimination→validation＂, was put forward and applied to publicly available microarray data as well as simulated ones. In this process, the rela- tive performances of six dimension reduction methods, including PCA, PLS ,PCA ＋ SIR,PCA ＋ SAVE ,PLS ＋ SIR and PLS ＋ SAVE, were evalua- ted. Results Considering the prediction quality, the stability of the pre- diction results as well as the sensitivity to the number of genes： （ 1 ） PLS performed was superior to PCA; （2） PLS ＋ SIR or PLS ＋ SAVE performed much better than other methods. Conclusion The results indicate that two stage combinational strategy proposed, i. e. variable pre-selection based on t-scores followed by PLS ＋ SIR or PLS ＋ SAVE,is feasible and practical in the discriminate analysis for microarray data.

关 键 词：两阶段组合降维 偏最小二乘 切片逆回归 切片均方误差估计 微阵列数据 判别分析 

分 类 号：R311[医药卫生—基础医学]