Stabilizing Variance on Time-Course Microarray Data

• Main Authors: Yu-wen Huang, 黃郁雯
• Other Authors: Shih-Huang Chan
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/10637514065181449250

碩士 === 國立成功大學 === 統計學系碩博士班 === 97 === In recent years, the development of cDNA microarray technology allows people to investigate thousands of genes simultaneously. For microarray data, it is well known that gene expression is related with its variance in some way. Researchers do try to stabilize the variance so that the detection power can be greatly improved. However, none or very few researches focus on stabilizing the variance for multiple time-course microarray data. In this thesis, we evaluate the function of variance for time course microarray data and suggest a pooled approach to stabilize the variance. We extend the existing methods, such as Started Log (sLog), Log-Linear Hybrid (Hyb), Generalized Logarithm Transformation (glog) and Spread-versus-level plot transformation (SVL) to stabilize variance for time-course data. We also consider two nonparametric methods, Data-Driven Haar-Fisz Transformation for Microarray (DDHFm) and step function approaches, to deal with this problem. Simulation study shows that the three log transformation methods are better in stabilizing variance than another methods, and DDHFm transformation may not suitable for time-course microarray data. A real time-course microarray data is illustrated for application.