Evaluation of normalization methods for microarray data.
BACKGROUND
Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.


RESULTS
In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.


CONCLUSIONS
Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.
Park
Taesung
T
Department of Statistics, Seoul National University, Seoul, Korea. tspark@stats.snu.ac.kr
Yi
Sung-Gon
SG
Kang
Sung-Hyun
SH
Lee
SeungYeoun
S
Lee
Yong-Sung
YS
Simon
Richard
R
D000704:Analysis of Variance; D000818:Animals; D002454:Cell Differentiation / Q000235:genetics; D002540:Cerebral Cortex / Q000737:chemistry; D003198:Computer Simulation / Q000706:statistics & numerical data; D018507:Gene Expression Regulation, Developmental / Q000235:genetics; D009474:Neurons / Q000502:physiology; D016011:Normal Distribution; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods* / Q000706:statistics & numerical data*; D051381:Rats; D013234:Stem Cells / Q000737:chemistry