Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs

Bemis, Kylie A.; Guo, Dan; Harry, April J.; Thomas, Mathew; Lanekoff, Ingela; Stenzel-Poore, Mary P.; Stevens, Susan L.; Laskin, Julia; Vitek, Olga

doi:10.1016/j.ijms.2018.07.006

Title: Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs

Journal Article · Fri Mar 01 00:00:00 EST 2019 · International Journal of Mass Spectrometry

DOI:https://doi.org/10.1016/j.ijms.2018.07.006· OSTI ID:1501645

Bemis, Kylie A.; Guo, Dan; Harry, April J.; Thomas, Mathew;

; Stenzel-Poore, Mary P.; Stevens, Susan L.; Laskin, Julia;

Mass Spectrometry Imaging (MSI) characterizes changes in chemical composition between regions of biological samples such as tissues. This translates into a statistical goal of class comparison, i.e. determining analytes that change in abundance between conditions more systematically than as expected by random variation. To reach accurate and reproducible conclusions, statistical analysis must appropriately reflect the initial research question, the design of the MSI experiment, and all the associated sources of variation. This manuscript highlights the importance of following these general statistical principles. Using the example of two case studies with complex experimental designs, and with dierent strategies of data acquisition, we demonstrate the extent to which choices made at key points of this workflow impact the results, and provide suggestions for appropriate design and analysis of MSI experiments that aim at detecting dierentially abundant analytes.

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Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE

DOE Contract Number:: AC05-76RL01830

OSTI ID:: 1501645

Report Number(s):: PNNL-SA-128968

Journal Information:: International Journal of Mass Spectrometry, Vol. 437, Issue C; ISSN 1387-3806

Publisher:: Elsevier

Country of Publication:: United States

Language:: English

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Colocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging Inglese, Paolo; Correia, Gonçalo; Pruski, Pamela Analytical Chemistry https://doi.org/10.1021/acs.analchem.8b05598	journal	May 2019
Colocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging. Inglese, Paolo; Correia, Gonçalo; Pruski, Pamela Apollo - University of Cambridge Repository https://doi.org/10.17863/cam.39158	text	January 2019

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