Imputing gene expression to maximize platform compatibility

作     者：Zhou, Weizhuang Han, Lichy Altman, Russ B. 

作者机构：Stanford Univ Dept Bioengn Stanford CA 94305 USA Stanford Univ Biomed Informat Training Program Stanford CA 94305 USA Stanford Univ Dept Genet Stanford CA 94305 USA 

出 版 物：《BIOINFORMATICS》 (生物信息学)

年 卷 期：2017年第33卷第4期

页      面：522-528页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0836[工学-生物工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Institutes of Health [GM102365, LM05652, GM61374] Pfizer [IC2014-1387] National Science Scholarship from A*STAR, Singapore 

主　　题：GENE expression BIOLOGICAL databases MICROARRAY technology GENES INFERENTIAL statistics 

摘      要：Microarray measurements of gene expression constitute a large fraction of publicly shared biological data, and are available in the Gene Expression Omnibus (GEO). Many studies use GEO data to shape hypotheses and improve statistical power. Within GEO, the Affymetrix HG-U133A and HG-U133 Plus 2.0 are the two most commonly used microarray platforms for human samples;the HG-U133 Plus 2.0 platform contains 54 220 probes and the HG-U133A array contains a proper subset (21 722 probes). When different platforms are involved, the subset of common genes is most easily compared. This approach results in the exclusion of substantial measured data and can limit downstream analysis. To predict the expression values for the genes unique to the HG-U133 Plus 2.0 platform, we constructed a series of gene expression inference models based on genes common to both platforms. Our model predicts gene expression values that are within the variability observed in controlled replicate studies and are highly correlated with measured data. Using six previously published studies, we also demonstrate the improved performance of the enlarged feature space generated by our model in downstream analysis.