Data Imputation in Merged Isobaric Labeling-Based Relative Quantification Datasets

Nicolai Bjødstrup Palstrøm, Rune Matthiesen, Hans Christian Beck

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Abstrakt

The data-dependent acquisition in mass spectrometry-based proteomics combined with quantitative analysis using isobaric labeling (iTRAQ and TMT) inevitably introduces missing values in proteomic experiments where a number of LC-runs are combined, especially in the growing field of shotgun clinical proteomics, where the protein profiles from the proteomics analysis of several hundred patient samples are compared and correlated to clinical traits such as a specific disease or disease treatment in order to link specific outcomes to one or more proteins. In the context of clinical research it is evident that missing values in such datasets reduce the power of the downstream statistical analysis therefore may hampers the linking of the expression of disease traits to the expression of specific proteins that may be useful for prognostic, diagnostic, or predictive purposes. In our study, we tested three data imputation approaches initially developed for microarray data for the imputation of missing values in datasets that are generated by several runs of shotgun proteomic experiments and where the data were relative protein abundances based on isobaric tags (iTRAQ and TMT). Our conclusion is that imputation methods based on k Nearest Neighbors successfully impute missing values in datasets with up to 50% missing values.

OriginalsprogEngelsk
TitelMass Spectrometry Data Analysis in Proteomics
RedaktørerRune Matthiesen
ForlagHumana Press
Publikationsdato2020
Sider297-308
ISBN (Trykt)9781493997435
ISBN (Elektronisk)9781493997442
DOI
StatusUdgivet - 2020
NavnMethods in Molecular Biology
Vol/bind2051
ISSN1064-3745

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