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Normalization and missing value imputation for label-free LC-MS analysis

Citation

Karpievitch, YV and Dabney, AR and Smith, RD, Normalization and missing value imputation for label-free LC-MS analysis, BMC Bioinformatics, 13, (Suppl 16) Article S5. ISSN 1471-2105 (2012) [Refereed Article]


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Copyright 2012 the authors Licenced under Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)

DOI: doi:10.1186/1471-2105-13-S16-S5

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.

Item Details

Item Type:Refereed Article
Research Division:Mathematical Sciences
Research Group:Statistics
Research Field:Biostatistics
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Mathematical Sciences
Author:Karpievitch, YV (Dr Yuliya Karpievitch)
ID Code:80961
Year Published:2012
Web of Science® Times Cited:67
Deposited By:Mathematics and Physics
Deposited On:2012-11-19
Last Modified:2015-01-27
Downloads:247 View Download Statistics

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