An ancova approach to normalize microarray data, and its performance to existing methods

Shih Huang Chan, Li Ju Chen, Nan Hwa Chow, Hiao Sheng Liu

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)

摘要

A microarray experiment includes many steps, and each one of them may include systematic variations. To have a sound analysis, the systematic bias must be identified and removed prior to the data being analyzed. Based on the M-A dependency observed by Dudoit et al. (2002), we suggest that, instead of using the lowess normalization, a new normalization method called ANCOVA be used for dealing with genes with replicates. Simulation studies have shown that the performance of the suggested ANCOVA method is superior to any of the available approaches with regards to the Fisher's Z score and concordance rate. We used a microarray data from bladder cancer to illustrate the application of our approach. The edge the ANCOVA method has over the existing normalization approaches is further confirmed through real-time PCR.

原文English
頁(從 - 到)257-268
頁數12
期刊Journal of Bioinformatics and Computational Biology
3
發行號2
DOIs
出版狀態Published - 2005 四月

All Science Journal Classification (ASJC) codes

  • 生物化學
  • 分子生物學
  • 電腦科學應用

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