This article is part of the supplement: Statistical mass spectrometry-based proteomics
Normalization and missing value imputation for label-free LC-MS analysis
1 School of Mathematics and Physics, University of Tasmania, Hobart, Tasmania, Australia
2 Department of Statistics, Texas A&M University, College Station, TX, USA
3 Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
BMC Bioinformatics 2012, 13(Suppl 16):S5 doi:10.1186/1471-2105-13-S16-S5Published: 5 November 2012
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.