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

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

• Dabney, Alan

published proceedings

• BMC Bioinformatics

altmetric score

• 1

author list (cited authors)

• Karpievitch, Y. V., Dabney, A. R., & Smith, R. D.

citation count

• 244

complete list of authors

• Karpievitch, Yuliya V||Dabney, Alan R||Smith, Richard D

publication date

• November 2012

publisher

• Springer Nature  Publisher

published in

• BMC Bioinformatics  Journal

keywords

• Bias
• Chromatography, Liquid
• Mass Spectrometry
• Proteomics
• Research Design

Digital Object Identifier (DOI)

• 10.1186/1471-2105-13-S16-S5

URI

• https://hdl.handle.net/1969.1/179234

start page

• S5

volume

• 13 Suppl 16

issue

• Suppl 16