Normalization and missing value imputation for label-free LC-MS analysis
journal contributionposted on 2023-05-17, 14:24 authored by Karpievitch, YV, Dabney, AR, Smith, RD
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.
Publication titleBMC Bioinformatics
Department/SchoolSchool of Natural Sciences
PublisherBiomed Central Ltd
Place of publicationMiddlesex House, 34-42 Cleveland St, London, England, W1T 4Lb
Rights statementCopyright 2012 the authors Licenced under Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0)