Cheng Lixin, Lo Leung-Yau, Tang Nelson L S, Wang Dong, Leung Kwong-Sak
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Department of Chemical Pathology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong.
Sci Rep. 2016 Jan 6;6:18898. doi: 10.1038/srep18898.
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.
归一化对于消除微阵列数据中的偏差以进行准确分析至关重要。现有的微阵列基因表达数据归一化方法通常假定所研究的样本之间存在相似的全局表达模式。然而，基因表达全局变化的情况在癌症中占主导地位，这使得该假设无效。为了缓解这个问题，我们在此提出并开发了一种新的归一化策略——交叉归一化（CrossNorm），用于处理样本间转录本水平不平衡的微阵列数据。传统方法，如RMA和LOESS，会任意抹平病例组和对照组之间的差异，导致基因表达估计出现偏差。值得注意的是，在利用原始信号的总体统计信息的交叉归一化策略下应用这些方法时，对于一系列公开可用的数据集，包括滴定实验、模拟数据、掺入数据以及跨越各种癌症类型的几个实际微阵列数据集，结果显示在估计转录本水平动态方面具有显著提高的稳健性和准确性。这些结果对于过去和未来基于差异不可忽略的微阵列样本的癌症研究具有重要意义。此外，只要实验在不同条件之间存在全局表达变化，该策略也可应用于其他类型的高通量数据。
