Young William Chad, Raftery Adrian E, Yeung Ka Yee
Department of Statistics, University of Washington, Box 354322, Seattle, WA 98195.
Institute of Technology, University of Washington Tacoma, Campus Box 358426, 1900 Commerce Street, Tacoma, WA 98402.
Ann Appl Stat. 2016 Feb;11(4):1998-2026. doi: 10.1214/17-AOAS1051. Epub 2017 Dec 28.
The NIH Library of Integrated Network-based Cellular Signatures (LINCS) contains gene expression data from over a million experiments, using Luminex Bead technology. Only 500 colors are used to measure the expression levels of the 1,000 landmark genes measured, and the data for the resulting pairs of genes are deconvolved. The raw data are sometimes inadequate for reliable deconvolution, leading to artifacts in the final processed data. These include the expression levels of paired genes being flipped or given the same value, and clusters of values that are not at the true expression level. We propose a new method called model-based clustering with data correction (MCDC) that is able to identify and correct these three kinds of artifacts simultaneously. We show that MCDC improves the resulting gene expression data in terms of agreement with external baselines, as well as improving results from subsequent analysis.
美国国立卫生研究院基于综合网络的细胞特征库（LINCS）包含使用Luminex微珠技术进行的超过100万次实验的基因表达数据。仅使用500种颜色来测量所检测的1000个标志性基因的表达水平，并且对所得基因对的数据进行反卷积处理。原始数据有时不足以进行可靠的反卷积，从而导致最终处理数据中出现伪像。这些伪像包括配对基因的表达水平被翻转或赋予相同的值，以及值的聚类不在真实表达水平上。我们提出了一种称为基于模型的聚类与数据校正（MCDC）的新方法，该方法能够同时识别和校正这三种伪像。我们表明，MCDC在与外部基线的一致性方面改进了所得的基因表达数据，同时也改善了后续分析的结果。
