School of Mathematics and Physics, University of Tasmania, Hobart, Tasmania, Australia.
BMC Bioinformatics. 2012;13 Suppl 16(Suppl 16):S5. doi: 10.1186/1471-2105-13-S16-S5. Epub 2012 Nov 5.
Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.
shotgun 蛋白质组学数据受到各种已知和未知的系统偏差以及高比例的缺失值的影响。通常，在进行统计推断之前，会进行归一化处理，试图从数据中去除系统偏差，有时还会进行缺失值插补，以获得完整的强度矩阵。在这里，我们讨论了几种归一化和处理缺失值的方法，有些最初是为微阵列数据开发的，有些是专门为基于质谱的数据分析开发的。
