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

Yuliya V. Karpievitch, Alan R. Dabney, Richard D. Smith

Research output: Contribution to journalArticlepeer-review

164 Citations (Scopus)


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.

Original languageEnglish
Article numberS5
JournalBMC Bioinformatics
Issue numberSupp. 16
Publication statusPublished - 1 Jan 2012
Externally publishedYes


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