cDNA Microarray Experiment: Design Issues in Early Stage and the Need of Normalization.
- Author:
Byung Soo KIM
1
;
Sunho LEE
;
Sun Young RHA
;
Hyun Cheol CHUNG
Author Information
1. Department of Applied Statistics, Yonsei University, Seoul 120-749, Korea. bskim@yonsei.ac.kr
- Publication Type:Original Article
- Keywords:
cDNA microarray;
Homotypic experiment
- MeSH:
Animals;
DNA, Complementary*;
Fibrosarcoma;
Humans;
Noise;
Oligonucleotide Array Sequence Analysis*;
Rats;
RNA;
Statistics as Topic
- From:Cancer Research and Treatment
2003;35(6):533-540
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
PURPOSE: The cDNA microarray has become a useful tool for observing the expression of thousands of genes simultaneously. However, obtaining good quality microarray data is not easy due to the inherent noise at various stages of the experiment. Therefore, it is essential to understand the source of the variation in the microarray experiment and its size as an initial step of the data analyses. MATERIALS AND METHODS: The total RNA extracted from HT-1080 fibrosarcoma and normal rat tissues were hybridized to the cDNA microarrays with 0.5 K human and 5 K rat genes, respectively. A homotypic reaction and dye swap experiments were used to identify the sources of the variation. RESULTS: The relative fluorescent intensities of the microarray, if unnormalized, have a large variation, particularly in the lower intensity region. The distribution of the log intensity ratios also exhibit some departure from a band around zero, which is the distribution pattern expected when the majority of genes in the microarray are not regulated. Normalization of the log ratios is usually required as a means of preprocessing the data. We claim that a within-print tip group, an intensity-dependent normalization through a loess fit adjustment will be useful for this purpose, particularly in the initial stages of the microarray experiment. CONCLUSION: For proper data analysis, an understanding the source of the variation and preprocessing of data with a suitable normalization method will be important. It is important to have an interactive cooperation between a researcher and a statistician from the early stages of the study design and to the final stages of data analysis.
