Toward digital staining using imaging mass spectrometry and random forests

Michael Hanselmann, Ullrich Köthe, Marc Kirchner, Bernhard Y. Renard, Erika R. Amstalden, Kristine Glunde, Ron M A Heeren, Fred A. Hamprecht

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish (US)
Pages (from-to)3558-3567
Number of pages10
JournalJournal of Proteome Research
Volume8
Issue number7
DOIs
StatePublished - Jul 6 2009

Fingerprint

Mass spectrometry
Mass Spectrometry
Staining and Labeling
Imaging techniques
Noise
Classifiers
Tissue
Forests

Keywords

  • Bioinformatics
  • Hyperspectral images
  • Imaging mass spectrometry
  • Markov Random Fields
  • Random Forest classification
  • Smoothing
  • Spectral images

ASJC Scopus subject areas

  • Biochemistry
  • Chemistry(all)

Cite this

Hanselmann, M., Köthe, U., Kirchner, M., Renard, B. Y., Amstalden, E. R., Glunde, K., ... Hamprecht, F. A. (2009). Toward digital staining using imaging mass spectrometry and random forests. Journal of Proteome Research, 8(7), 3558-3567. https://doi.org/10.1021/pr900253y

Toward digital staining using imaging mass spectrometry and random forests. / Hanselmann, Michael; Köthe, Ullrich; Kirchner, Marc; Renard, Bernhard Y.; Amstalden, Erika R.; Glunde, Kristine; Heeren, Ron M A; Hamprecht, Fred A.

In: Journal of Proteome Research, Vol. 8, No. 7, 06.07.2009, p. 3558-3567.

Research output: Contribution to journalArticle

Hanselmann, M, Köthe, U, Kirchner, M, Renard, BY, Amstalden, ER, Glunde, K, Heeren, RMA & Hamprecht, FA 2009, 'Toward digital staining using imaging mass spectrometry and random forests', Journal of Proteome Research, vol. 8, no. 7, pp. 3558-3567. https://doi.org/10.1021/pr900253y
Hanselmann M, Köthe U, Kirchner M, Renard BY, Amstalden ER, Glunde K et al. Toward digital staining using imaging mass spectrometry and random forests. Journal of Proteome Research. 2009 Jul 6;8(7):3558-3567. https://doi.org/10.1021/pr900253y
Hanselmann, Michael ; Köthe, Ullrich ; Kirchner, Marc ; Renard, Bernhard Y. ; Amstalden, Erika R. ; Glunde, Kristine ; Heeren, Ron M A ; Hamprecht, Fred A. / Toward digital staining using imaging mass spectrometry and random forests. In: Journal of Proteome Research. 2009 ; Vol. 8, No. 7. pp. 3558-3567.
@article{8f49195d3fbe4e8c9bdd4809e6cebc4a,
title = "Toward digital staining using imaging mass spectrometry and random forests",
abstract = "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.",
keywords = "Bioinformatics, Hyperspectral images, Imaging mass spectrometry, Markov Random Fields, Random Forest classification, Smoothing, Spectral images",
author = "Michael Hanselmann and Ullrich K{\"o}the and Marc Kirchner and Renard, {Bernhard Y.} and Amstalden, {Erika R.} and Kristine Glunde and Heeren, {Ron M A} and Hamprecht, {Fred A.}",
year = "2009",
month = "7",
day = "6",
doi = "10.1021/pr900253y",
language = "English (US)",
volume = "8",
pages = "3558--3567",
journal = "Journal of Proteome Research",
issn = "1535-3893",
publisher = "American Chemical Society",
number = "7",

}

TY - JOUR

T1 - Toward digital staining using imaging mass spectrometry and random forests

AU - Hanselmann, Michael

AU - Köthe, Ullrich

AU - Kirchner, Marc

AU - Renard, Bernhard Y.

AU - Amstalden, Erika R.

AU - Glunde, Kristine

AU - Heeren, Ron M A

AU - Hamprecht, Fred A.

PY - 2009/7/6

Y1 - 2009/7/6

N2 - 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.

AB - 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.

KW - Bioinformatics

KW - Hyperspectral images

KW - Imaging mass spectrometry

KW - Markov Random Fields

KW - Random Forest classification

KW - Smoothing

KW - Spectral images

UR - http://www.scopus.com/inward/record.url?scp=67650370012&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67650370012&partnerID=8YFLogxK

U2 - 10.1021/pr900253y

DO - 10.1021/pr900253y

M3 - Article

C2 - 19469555

AN - SCOPUS:67650370012

VL - 8

SP - 3558

EP - 3567

JO - Journal of Proteome Research

JF - Journal of Proteome Research

SN - 1535-3893

IS - 7

ER -