Journal
JOURNAL OF PROTEOME RESEARCH
Volume 8, Issue 7, Pages 3558-3567Publisher
AMER CHEMICAL SOC
DOI: 10.1021/pr900253y
Keywords
imaging mass spectrometry; Random Forest classification; Markov Random Fields; smoothing; bioinformatics; spectral images; hyperspectral images
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Funding
- DFG [HA4364/2-1]
- Robert Bosch GmbH
- National Institutes of Health of the USA [R01 CA134695]
- The Netherlands BSIK program Virtual Laboratory for e-science
- Stichting voor Fundamenteel Onderzoek der MaLerie (FOM)
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We show on imaging mass spectrometry (IMS) data that the Random Forest classifier can be used for automated tissue classification and that it results in predictions with high sensitivities and positive predictive values, even when intersample variability is present in the data. We further demonstrate how Markov Random Fields and vector-valued median filtering can be applied to reduce noise effects to further improve the classification results in a posthoc smoothing step. Our study gives clear evidence that digital staining by means of IMS constitutes a promising complement to chemical staining techniques.
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