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Variability Assessment of Label-Free LC-MS Experiments for Difference Detection

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Book cover Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry

Part of the book series: Frontiers in Probability and the Statistical Sciences ((FROPROSTAS))

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Abstract

In the analysis of data acquired from label-free experiments by liquid chromatography coupled with mass spectrometry (LC-MS), accounting for potential sources of variability can improve the detection of true differences in ion abundance. Mixed effects models are commonly used to estimate variabilities due to heterogeneity of the biological specimen, differences in sample preparation, and instrument variation. In this chapter, we investigate the mixed effects models and evaluate their performance in difference detection, in comparison to other methods such as marginal t-test, which uses the average over analytical and technical replicates within each biological sample for statistical analysis. Experimental design including replication assignment and sample size calculation is discussed. These are highly dependent on the variation contributed by the different sources, which can be estimated from LC-MS pilot studies prior to running large-scale label-free experiments.

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Acknowledgements

This work is in part supported by the National Institutes of Health Grants U01CA185188 and R01GM086746 awarded to HWR.

Author information

Authors and Affiliations

  1. Lombardi Comprehensive Cancer Center, Georgetown University, Washington, DC, USA

    Yi Zhao, Tsung-Heng Tsai, Cristina Di Poto & Habtom W. Ressom

  2. Mitchell Cancer Institute, University of South Alabama, Mobile, AL, USA

    Lewis K. Pannell

  3. Department of Mathematics and Statistics, Georgetown University, Washington, DC, USA

    Mahlet G. Tadesse

Authors
  1. Yi Zhao
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  2. Tsung-Heng Tsai
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  3. Cristina Di Poto
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  4. Lewis K. Pannell
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  5. Mahlet G. Tadesse
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  6. Habtom W. Ressom
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Corresponding author

Correspondence to Habtom W. Ressom .

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Editors and Affiliations

  1. Department of Biostatistics, University of Florida, Gainesville, Florida, USA

    Susmita Datta

  2. Department of Medical Statistics and Bioinformatics, Leiden University Medical Centre, RC Leiden, The Netherlands

    Bart J. A. Mertens

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Zhao, Y., Tsai, TH., Di Poto, C., Pannell, L.K., Tadesse, M.G., Ressom, H.W. (2017). Variability Assessment of Label-Free LC-MS Experiments for Difference Detection. In: Datta, S., Mertens, B. (eds) Statistical Analysis of Proteomics, Metabolomics, and Lipidomics Data Using Mass Spectrometry. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-45809-0_9

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