Affiliations: [a] College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, China | [b] Cancer Institute, Hangzhou Cancer Hospital, Hangzhou, Zhejiang, China | [c] Department of Civil Engineering, Monash University, VIC, Australia
Correspondence:
[*]
Corresponding author: Bin Han, College of Life Information Science and Instrument Engineering, Hangzhou Dianzi University, Hangzhou, Zhejiang, China. E-mail: [email protected]
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.
Keywords: Microarray, confounding factors, obscuring variation, gene-disease association analysis
