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

BMC Bioinformatics

Research

Alan R Dabney¹  Richard D Smith²  Yuliya V Karpievitch³ 

[1] Department of Statistics, Texas A&M University, College Station, TX, USA;Fundamental and Computational Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA;School of Mathematics and Physics, University of Tasmania, Hobart, Tasmania, Australia;

关键词: Mass Spectrometry Data;    Batch Effect;    Peptide Abundance;    Instrument Detection Limit;    Surrogate Variable Analysis;   

DOI  :  10.1186/1471-2105-13-S16-S5

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Karpievitch et al.; licensee BioMed Central Ltd. 2012