An ancova approach to normalize microarray data, and its performance to existing methods.
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.
Chan
Shih-Huang
SH
Department of Statistics, National Cheng Kung University, Tainan, Taiwan, Republic of China. shchan@email.stat.ncku.edu.tw
Chen
Li-Ju
LJ
Chow
Nan-Hwa
NH
Liu
Hsiao-Sheng
HS
D000465:Algorithms; D000704:Analysis of Variance; D014408:Biomarkers, Tumor / Q000378:metabolism*; D002295:Carcinoma, Transitional Cell / Q000175:diagnosis / Q000378:metabolism*; D045744:Cell Line, Tumor; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods* / Q000592:standards; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D009363:Neoplasm Proteins / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000592:standards; D001749:Urinary Bladder Neoplasms / Q000175:diagnosis / Q000378:metabolism*
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins