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

Abstract

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.

Authors:
; ; ; ; ORCiD logo; ; ; ; ORCiD logo
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1501645
Report Number(s):
PNNL-SA-128968
Journal ID: ISSN 1387-3806
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
International Journal of Mass Spectrometry
Additional Journal Information:
Journal Volume: 437; Journal Issue: C; Journal ID: ISSN 1387-3806
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
NanoDESI, mass spectrometry, MSIQuickView

Citation Formats

Bemis, Kylie A., Guo, Dan, Harry, April J., Thomas, Mathew, Lanekoff, Ingela, Stenzel-Poore, Mary P., Stevens, Susan L., Laskin, Julia, and Vitek, Olga. Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs. United States: N. p., 2019. Web. doi:10.1016/j.ijms.2018.07.006.
Bemis, Kylie A., Guo, Dan, Harry, April J., Thomas, Mathew, Lanekoff, Ingela, Stenzel-Poore, Mary P., Stevens, Susan L., Laskin, Julia, & Vitek, Olga. Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs. United States. doi:10.1016/j.ijms.2018.07.006.
Bemis, Kylie A., Guo, Dan, Harry, April J., Thomas, Mathew, Lanekoff, Ingela, Stenzel-Poore, Mary P., Stevens, Susan L., Laskin, Julia, and Vitek, Olga. Fri . "Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs". United States. doi:10.1016/j.ijms.2018.07.006.
@article{osti_1501645,
title = {Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs},
author = {Bemis, Kylie A. and Guo, Dan and Harry, April J. and Thomas, Mathew and Lanekoff, Ingela and Stenzel-Poore, Mary P. and Stevens, Susan L. and Laskin, Julia and Vitek, Olga},
abstractNote = {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.},
doi = {10.1016/j.ijms.2018.07.006},
journal = {International Journal of Mass Spectrometry},
issn = {1387-3806},
number = C,
volume = 437,
place = {United States},
year = {2019},
month = {3}
}