Statistical detection of differentially abundant ions in mass spectrometry-based imaging experiments with complex designs
- Northeastern Univ., Boston, MA (United States)
- Purdue Univ., West Lafayette, IN (United States); Northeastern Univ., Boston, MA (United States)
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Uppsala Univ., Uppsala (Sweden)
- Oregon Health & Science Univ., Portland, OR (United States)
- Purdue Univ., West Lafayette, IN (United States)
Here, 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.
- Research Organization:
- Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- Grant/Contract Number:
- ES024229-01; AC05-76RL01830
- OSTI ID:
- 1468953
- Report Number(s):
- PNNL-SA-128968; PII: S1387380617303615
- Journal Information:
- International Journal of Mass Spectrometry, Vol. 437; ISSN 1387-3806
- Publisher:
- ElsevierCopyright Statement
- Country of Publication:
- United States
- Language:
- English
Web of Science
Colocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging
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journal | May 2019 |
Colocalization Features for Classification of Tumors Using Desorption Electrospray Ionization Mass Spectrometry Imaging.
|
text | January 2019 |
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