CrossNorm: a novel normalization strategy for microarray data in cancers
Publication in refereed journal


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摘要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.
著者Cheng LX, Lo LY, Tang NLS, Wang D, Leung KS
期刊名稱Scientific Reports
出版年份2016
月份1
日期6
卷號6
出版社NATURE PUBLISHING GROUP
國際標準期刊號2045-2322
電子國際標準期刊號2045-2322
語言英式英語
Web of Science 學科類別Multidisciplinary Sciences; Science & Technology - Other Topics

上次更新時間 2020-04-08 於 01:46