title: CrossNorm: a novel normalization strategy for microarray data in cancers.
abstract: Normalization is essential to get rid of biases in microarray data for their accurate analysis. Existing normalization methods for microarray gene expression data commonly assume a similar global expression pattern among samples being studied. However, scenarios of global shifts in gene expressions are dominant in cancers, making the assumption invalid. To alleviate the problem, here we propose and develop a novel normalization strategy, Cross Normalization (CrossNorm), for microarray data with unbalanced transcript levels among samples. Conventional procedures, such as RMA and LOESS, arbitrarily flatten the difference between case and control groups leading to biased gene expression estimates. Noticeably, applying these methods under the strategy of CrossNorm, which makes use of the overall statistics of the original signals, the results showed significantly improved robustness and accuracy in estimating transcript level dynamics for a series of publicly available datasets, including titration experiment, simulated data, spike-in data and several real-life microarray datasets across various types of cancers. The results have important implications for the past and the future cancer studies based on microarray samples with non-negligible difference. Moreover, the strategy can also be applied to other sorts of high-throughput data as long as the experiments have global expression variations between conditions.
authors.affiliation: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Lixin
authors.initials: L
authors.lastname: Cheng
authors.affiliation: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Leung-Yau
authors.initials: LY
authors.lastname: Lo
authors.affiliation: Department of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Nelson L S
authors.initials: NL
authors.lastname: Tang
authors.affiliation: College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
authors.affiliation: Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
authors.forename: Kwong-Sak
authors.initials: KS
authors.lastname: Leung
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D066264:Datasets as Topic; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009369:Neoplasms / Q000628:therapy*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D015203:Reproducibility of Results