A case study on the identification of confounding factors for gene disease association analysis.

ABSTRACT

Variation in the expression of genes arises from a variety of sources. It is important to remove sources of variation between arrays of non-biological origin. Non-biological variation, caused by lurking confounding factors, usually attracts little attention, although it may substantially influence the expression profile of genes. In this study, we proposed a method which is able to identify the potential confounding factors and highlight the non-biological variations. We also developed methods and statistical tests to study the confounding factors and their influence on the homogeneity of microarray data, gene selection, and disease classification. We explored an ovarian cancer gene expression profile and showed that data batches and arraying conditions are two confounding factors. Their influence on the homogeneity of data, gene selection, and disease classification are statistically analyzed. Experiments showed that after normalization, their influences were removed. Comparative studies further showed that the data became more homogeneous and the classification quality was improved. This research demonstrated that identifying and reducing the impact of confounding factors is paramount in making sense of gene-disease association analysis.