SwePub - search: WFRF:(Bemis Kylie A.)

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1.	Bemis, Kylie A., et al. (author) Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs 2019 In: International Journal of Mass Spectrometry. - : ELSEVIER SCIENCE BV. - 1387-3806 .- 1873-2798. ; 437, s. 49-57 Journal article (peer-reviewed)abstract Mass Spectrometry Imaging (MSI) characterizes changes in chemical composition between regions of biological samples such as tissues. One goal of statistical analysis of MSI experiments is 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 different 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 differentially abundant analytes. (C) 2018 Elsevier B.V. All rights reserved.

Bemis, Kylie A., et al. (author)
Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs
2019
In: International Journal of Mass Spectrometry. - : ELSEVIER SCIENCE BV. - 1387-3806 .- 1873-2798. ; 437, s. 49-57
Journal article (peer-reviewed)abstract
- Mass Spectrometry Imaging (MSI) characterizes changes in chemical composition between regions of biological samples such as tissues. One goal of statistical analysis of MSI experiments is 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 different 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 differentially abundant analytes. (C) 2018 Elsevier B.V. All rights reserved.

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