Numerical experiments with MALDI Imaging data

Kobarg, Jan Hendrik; Maaß, Peter; Oetjen, Janina; Tropp, Oren; Hirsch, Eyal; Sagiv, Chen; Golbabaee, Mohammad

doi:10.1007/s10444-013-9325-0

Cited by 8 publications

(10 citation statements)

References 17 publications

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“…Computational and statistical methods for MALDI-IMS analysis have been proposed ( Alexandrov, 2012 ; Jones et al , 2012 ; Kobarg et al , 2013 ; Trede et al. , 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Deconvolving molecular signatures of interactions between microbial colonies

et al. 2015

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Motivation: The interactions between microbial colonies through chemical signaling are not well understood. A microbial colony can use different molecules to inhibit or accelerate the growth of other colonies. A better understanding of the molecules involved in these interactions could lead to advancements in health and medicine. Imaging mass spectrometry (IMS) applied to co-cultured microbial communities aims to capture the spatial characteristics of the colonies’ molecular fingerprints. These data are high-dimensional and require computational analysis methods to interpret.Results: Here, we present a dictionary learning method that deconvolves spectra of different molecules from IMS data. We call this method MOLecular Dictionary Learning (MOLDL). Unlike standard dictionary learning methods which assume Gaussian-distributed data, our method uses the Poisson distribution to capture the count nature of the mass spectrometry data. Also, our method incorporates universally applicable information on common ion types of molecules in MALDI mass spectrometry. This greatly reduces model parameterization and increases deconvolution accuracy by eliminating spurious solutions. Moreover, our method leverages the spatial nature of IMS data by assuming that nearby locations share similar abundances, thus avoiding overfitting to noise. Tests on simulated datasets show that this method has good performance in recovering molecule dictionaries. We also tested our method on real data measured on a microbial community composed of two species. We confirmed through follow-up validation experiments that our method recovered true and complete signatures of molecules. These results indicate that our method can discover molecules in IMS data reliably, and hence can help advance the study of interaction of microbial colonies.Availability and implementation: The code used in this paper is available at: https://github.com/frizfealer/IMS_project.Contact: vjojic@cs.unc.eduSupplementary information: Supplementary data are available at Bioinformatics online.

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“…Computational and statistical methods for MALDI-IMS analysis have been proposed ( Alexandrov, 2012 ; Jones et al , 2012 ; Kobarg et al , 2013 ; Trede et al. , 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Deconvolving molecular signatures of interactions between microbial colonies

et al. 2015

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“…Each peak was fitted with an exponentially modified Gaussian function following the procedure pointed out in [18,19]. The isotopic ratios shown in Table 1 were derived from the peak areas and their 3σ errors were estimated from counting statistics.…”

Section: Resultsmentioning

confidence: 99%

Development of a sequential data correction method for isotope ratio analysis by resonance ionization mass spectrometry

Noto

Tomita

Furuta

et al. 2015

Journal of Nuclear Science and Technology

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A sequential data correction method using several experimental parameters of pulsed resonance ionization mass spectrometry was developed to reduce mass discrimination effects for isotope ratio analysis as well as fluctuations in the ion count rate. This correction method was applied to isotope ratio determination of stable titanium isotopes using a two-step, single color ionization scheme. A significant improvement of the precision of the measured isotopic ratio of stable titanium isotopes was demonstrated after correction with a parameter set consisting of laser power, atmospheric pressure, and laser frequency.

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“…The data used in this paper was obtained by a MALDI imaging experiment, see Figure 2 for a schematic experimental setup. In our numerical experiments, we used a classical rat brain dataset which has been used in several data processing papers before [2,3,22]. It constitutes a standard test set for hyperspectral data analysis.…”

Section: Maldi Imagingmentioning

confidence: 99%

A Survey on Surrogate Approaches to Non-negative Matrix Factorization

Fernsel

Maaß

2018

Vietnam J. Math.

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Motivated by applications in hyperspectral imaging we investigate methods for approximating a high-dimensional non-negative matrix Y by a product of two lower-dimensional, non-negative matrices K and X . This so-called nonnegative matrix factorization is based on defining suitable Tikhonov functionals, which combine a discrepancy measure for Y ≈ KX with penalty terms for enforcing additional properties of K and X . The minimization is based on alternating minimization with respect to K or X , where in each iteration step one replaces the original Tikhonov functional by a locally defined surrogate functional. The choice of surrogate functionals is crucial: It should allow a comparatively simple minimization and simultaneously its first order optimality condition should lead to multiplicative update rules, which automatically preserve non-negativity of the iterates. We review the most standard construction principles for surrogate functionals for Frobenius-norm and Kullback-Leibler discrepancy measures. We extend the known surrogate constructions by a general framework, which allows to add a large variety of penalty terms. The paper finishes by deriving the corresponding alternating minimization schemes explicitely and by applying these methods to MALDI imaging data.

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Numerical experiments with MALDI Imaging data

Cited by 8 publications

References 17 publications

Deconvolving molecular signatures of interactions between microbial colonies

Deconvolving molecular signatures of interactions between microbial colonies

Development of a sequential data correction method for isotope ratio analysis by resonance ionization mass spectrometry

A Survey on Surrogate Approaches to Non-negative Matrix Factorization

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