Title

Integration of pre-normalized microarray data using quantile correction 

Authors

Takashi Yoneya¹*, Tatsuya Miyazawa²

Affiliation

¹Drug Discovery Research Laboratories, Kyowa Hakko Kirin Co Ltd, 1188, Shimotogari, Nagaizumi-cho, Sunto-gun, Shizuoka, 411-8731, Japan; ²Innovative Drug Research Laboratories, Kyowa Hakko Kirin Co Ltd, 3-6-6 Asahi-machi, Machida-shi, Tokyo 194-8533, Japan 

Email

takashi.yoneya@kyowa-kirin.co.jp; *Corresponding author  

Article Type

Hypothesis

Date

Received October 30, 2010; Accepted January 19, 2011; Published February 07, 2011
 

An enormous amount of microarray data has been collected and accumulated in public repositories. Although some of the depositions include raw and processed data, significant parts of them include processed data only. If we need to combine multiple datasets for specific purposes, the data should be adjusted prior to use to remove bias between the datasets. We focused on a GeneChip platform and a pre-processing method, RMA, and examined simple quantile correction as the post-processing method for integration. Integration of the data pre-processed by RMA was evaluated using artificial spike-in datasets and real microarray datasets of atopic dermatitis and lung cancer. Studies using the spike-in datasets show that the quantile correction for data integration reduces the data quality at some extent but it should be acceptable level. Studies using the real datasets show that the quantile correction significantly reduces the bias. These results show that the quantile correction is useful for integration of multiple datasets processed by RMA, and encourage effective use of public microarray data.  

Keywords

data integration, quantile correction, microarray, RMA, GeneChip

Citation

Yoneya & Miyazawa. Bioinformation 5(9): 382-385 (2011)

Edited by

P Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.