title: Extensive increase of microarray signals in cancers calls for novel normalization assumptions.
abstract: When using microarray data for studying a complex disease such as cancer, it is a common practice to normalize data to force all arrays to have the same distribution of probe intensities regardless of the biological groups of samples. The assumption underlying such normalization is that in a disease the majority of genes are not differentially expressed genes (DE genes) and the numbers of up- and down-regulated genes are roughly equal. However, accumulated evidences suggest gene expressions could be widely altered in cancer, so we need to evaluate the sensitivities of biological discoveries to violation of the normalization assumption. Here, we analyzed 7 large Affymetrix datasets of pair-matched normal and cancer samples for cancers collected in the NCBI GEO database. We showed that in 6 of these 7 datasets, the medians of perfect match (PM) probe intensities increased in cancer state and the increases were significant in three datasets, suggesting the assumption that all arrays have the same median probe intensities regardless of the biological groups of samples might be misleading. Then, we evaluated the effects of three currently most widely used normalization algorithms (RMA, MAS5.0 and dChip) on the selection of DE genes by comparing them with LVS which relies less on the above-mentioned assumption. The results showed using RMA, MAS5.0 and dChip may produce lots of false results of down-regulated DE genes while missing many up-regulated DE genes. At least for cancer study, normalizing all arrays to have the same distribution of probe intensities regardless of the biological groups of samples might be misleading. Thus, most current normalizations based on unreliable assumptions may distort biological differences between normal and cancer samples. The LVS algorithm might perform relatively well due to that it relies less on the above-mentioned assumption. Also, our results indicate that genes may be widely up-regulated in most human cancer.
authors.affiliation: Bioinformatics Centre, School of Life Science, University of Electronic Science and Technology of China, Chengdu, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Wang
authors.forename: Lixin
authors.initials: L
authors.lastname: Cheng
authors.forename: Mingyue
authors.initials: M
authors.lastname: Wang
authors.forename: Ruihong
authors.initials: R
authors.lastname: Wu
authors.forename: Pengfei
authors.initials: P
authors.lastname: Li
authors.forename: Bin
authors.initials: B
authors.lastname: Li
authors.forename: Yuannv
authors.initials: Y
authors.lastname: Zhang
authors.forename: Yunyan
authors.initials: Y
authors.lastname: Gu
authors.forename: Wenyuan
authors.initials: W
authors.lastname: Zhao
authors.forename: Chenguang
authors.initials: C
authors.lastname: Wang
authors.forename: Zheng
authors.initials: Z
authors.lastname: Guo
mesh_terms: D000465:Algorithms; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D030541:Databases, Genetic; D006801:Humans; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism / Q000473:pathology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*