期刊
JOURNAL OF PROTEOME RESEARCH
卷 8, 期 7, 页码 3558-3567出版社
AMER CHEMICAL SOC
DOI: 10.1021/pr900253y
关键词
imaging mass spectrometry; Random Forest classification; Markov Random Fields; smoothing; bioinformatics; spectral images; hyperspectral images
资金
- 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)
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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