首页 | 本学科首页   官方微博 | 高级检索  
     


Analyzing Multiple‐Probe Microarray: Estimation and Application of Gene Expression Indexes
Authors:Mehdi Maadooliat  Jianhua Z. Huang  Jianhua Hu
Affiliation:1. Graduate student, Department of Statistics, Texas A&M University, College Station, TX, USA;2. Professor, Department of Statistics, Texas A&M University, College Station, TX, USA;3. Associate Professor, Department of Biostatistics, Division of Quantitative Sciences, University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
Abstract:Summary Gene expression index estimation is an essential step in analyzing multiple probe microarray data. Various modeling methods have been proposed in this area. Amidst all, a popular method proposed in Li and Wong (2001) is based on a multiplicative model, which is similar to the additive model discussed in Irizarry et al. (2003a) at the logarithm scale. Along this line, Hu et al. (2006) proposed data transformation to improve expression index estimation based on an ad hoc entropy criteria and naive grid search approach. In this work, we re‐examined this problem using a new profile likelihood‐based transformation estimation approach that is more statistically elegant and computationally efficient. We demonstrate the applicability of the proposed method using a benchmark Affymetrix U95A spiked‐in experiment. Moreover, We introduced a new multivariate expression index and used the empirical study to shows its promise in terms of improving model fitting and power of detecting differential expression over the commonly used univariate expression index. As the other important content of the work, we discussed two generally encountered practical issues in application of gene expression index: normalization and summary statistic used for detecting differential expression. Our empirical study shows somewhat different findings from the MAQC project ( MAQC, 2006 ).
Keywords:Differential expression detection  Fold change  Multivariate expression index  Normalization  Profile likelihood  Transformation model
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号