title: Normalization and missing value imputation for label-free LC-MS analysis.
abstract: 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.
authors.affiliation: School of Mathematics and Physics, University of Tasmania, Hobart, Tasmania, Australia. yuliya.karpievitch@utas.edu.au
authors.forename: Yuliya V
authors.initials: YV
authors.lastname: Karpievitch
authors.forename: Alan R
authors.initials: AR
authors.lastname: Dabney
authors.forename: Richard D
authors.initials: RD
authors.lastname: Smith
mesh_terms: D015982:Bias; D002853:Chromatography, Liquid / Q000706:statistics & numerical data*; D013058:Mass Spectrometry / Q000706:statistics & numerical data*; D040901:Proteomics / Q000706:statistics & numerical data*; D012107:Research Design