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Integration of pre-normalized microarray data using quantile correction

By Takashi Yoneya and Tatsuya Miyazawa


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

Topics: Hypothesis
Publisher: Biomedical Informatics
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Provided by: PubMed Central

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  1. (2011). cal/technotes/plier_technote.pdf Edited by P Kangueane Citation: Yoneya & Miyazawa.