Normalization and Missing Value Imputation for Label-free LC-MS Analysis

Overview

Journal BMC Bioinformatics

Publisher Biomed Central

Specialty Biology

Date 2012 Nov 27

PMID 23176322

Citations 142

Authors

Yuliya V Karpievitch

Alan R Dabney

Richard D Smith

Affiliations

Soon will be listed here.

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.

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