The effects of normalization on the correlation structure of microarray data

Background: Stochastic dependence between gene expression levels in microarray data is of critical importance for the methods of statistical inference that resort to pooling test-statistics across genes. It is frequently assumed that dependence between genes (or tests) is suffciently weak to justify the proposed methods of testing for differentially expressed genes. A potential impact of between-gene correlations on the performance of such methods has yet to be explored. Results: The paper presents a systematic study of correlation between the t-statistics associated with different genes. We report the effects of four different normalization methods using a large set of microarray data on childhood leukemia in addition to several sets of simulated data. Our findings help decipher the correlation structure of microarray data before and after the application of normalization procedures. Conclusion: A long-range correlation in microarray data manifests itself in thousands of genes that a...

• Type: Journal
• Year: 2005
• Where: BMCBI
• Authors: Xing Qiu, Andrew I. Brooks, Lev Klebanov, Andrei Yakovlev
• Keywords: BMCBI 2005, Expressed Genes, Microarray Data, Stochastic Dependence