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

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 imput...

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Detalles Bibliográficos
Autores principales: Karpievitch, Yuliya V, Dabney, Alan R, Smith, Richard D
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3489534/
https://www.ncbi.nlm.nih.gov/pubmed/23176322
http://dx.doi.org/10.1186/1471-2105-13-S16-S5
Descripción
Sumario: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.