TNorm: An unsupervised batch effects correction method for gene expression data classification

Abstract

In the field of biomedical research, gene expression analysis helps to identify the disease-related genes as genetic markers for diagnosis. As there is a huge number of publicly available gene expression datasets, the ongoing challenge is to utilize those available data effectively. Merging microarray datasets from different batches to improve the statistical power of a study is one of the active research topics. However, various works have addressed the issue of batch effects variation, which describes variation in gene expression levels induced by different experimental environments. Ignoring this variation may result in erroneous findings in a study. This work proposes a method for batch effect correction by mapping underlying topology of different batches. The mapping process for cross-batch normalization is examined using basic linear transformation. The comparative study of three cancers is conducted to compare the proposed method with a proven batch effects correction method. The results show that our method outperforms the existing method in most cases.