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

Journal Article · Mon Nov 05 00:00:00 EST 2012 · BMC Bioinformatics, 13(Suppl 16):Article No. S5

DOI:https://doi.org/10.1186/1471-2105-13-S16-S5· OSTI ID:1074318

Karpievitch, Yuliya; Dabney, Alan R; Smith, Richard D

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.

• Research Organization: Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Environmental Molecular Sciences Lab. (EMSL)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-76RL01830
• OSTI ID: 1074318
• Report Number(s): PNNL-SA-94014; 34708; KP1601010
• Journal Information: BMC Bioinformatics, 13(Suppl 16):Article No. S5, Journal Name: BMC Bioinformatics, 13(Suppl 16):Article No. S5
• Country of Publication: United States
• Language: English

Related Subjects

Environmental Molecular Sciences Laboratory