Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11861/7458
Title: ICN: A normalization method for gene expression data considering the over-expression of informative genes
Authors: Cheng, Lixin 
Wang, Xuan 
Wong, Pak-Kan 
Lee, Kwan-Yeung 
Li, Le 
Xu, Bin 
Wang, Dong 
Prof. LEUNG Kwong Sak 
Issue Date: 2016
Source: Molecular BioSystems, 2016, vol. 12(10), pp. 3057-3066
Journal: Molecular BioSystems 
Abstract: The global increase of gene expression has been frequently established in cancer microarray studies. However, many genes may not deliver informative signals for a given experiment, due to insufficient expression or even non-expression, despite the DNA microarrays massively measuring genes in parallel. Hence the informative gene set, rather than the whole genome, should be more reasonable to represent the genome expression level. We observed that the trend of over-expression for informative genes is more obvious in human cancers, which is to some extent masked using the whole genome without any filtering. Accordingly we proposed a novel normalization method, Informative CrossNorm (ICN), which performs the cross normalization (CrossNorm) on the expression matrix merely containing the informative genes. ICN outperforms other methods with a consistently high precision, F-score, and Matthews correlation coefficient as well as an acceptable recall based on three available spiked-in datasets with ground truth. In addition, nine potential therapeutic target genes for esophageal squamous cell carcinoma (ESCC) were identified using ICN integrated with a protein-protein interaction network, which biologically demonstrates that ICN shows superior performance. Consequently, it is expected that ICN could be applied routinely in cancer microarray studies. © The Royal Society of Chemistry 2016.
Type: Peer Reviewed Journal Article
URI: http://hdl.handle.net/20.500.11861/7458
ISSN: 1742206X
DOI: 10.1039/c6mb00386a
Appears in Collections:Applied Data Science - Publication

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