Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model

Wang, Yuan; Zhou, Xiaobo; Wang, Honghui; Li, King C.; Yao, Lixiu; Wong, Stephen T.C.

doi:10.1093/bioinformatics/btn143

Cited by 17 publications

(19 citation statements)

References 25 publications

(30 reference statements)

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“…For example, in the case where the proposal is to add a new point source, the auxiliary parameters, u, are simply the parameters describing the new point source. This method is known as the reversible jump MCMC (RJMCMC) (Green 1995;Wang et al 2008;Hastie & Green 2012), which is a variant of MCMC that allows across-model moves in a pool of models indexed by their dimensionality. We inherit the reversible jump formalism in implementing transdimensional proposals.…”

Section: Transdimensional Samplingmentioning

confidence: 99%

Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs

Daylan

Portillo

Finkbeiner

2017

ApJ

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The detection of point sources in images is a fundamental operation in astrophysics, and is crucial for constraining population models of the underlying point sources or characterizing the background emission. Standard techniques fall short in the crowded-field limit, losing sensitivity to faint sources and failing to track their covariance with close neighbors. We construct a Bayesian framework to perform inference of faint or overlapping point sources. The method involves probabilistic cataloging, where samples are taken from the posterior probability distribution of catalogs consistent with an observed photon count map. In order to validate our method, we sample random catalogs of the gamma-ray sky in the direction of the North Galactic Pole (NGP) by binning the data in energy and point-spread function classes. Using three energy bins spanning 0.3-1, 1-3,and 3-10 GeV, we identify 18 2 2 %. These uncertainties in the flux distribution are fully marginalized over the number as well as the spatial and spectral properties of the unresolved point sources. This marginalization allows a robust test of whether the apparently isotropic emission in an image is due to unresolved point sources or of truly diffuse origin.

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Section: Transdimensional Samplingmentioning

confidence: 99%

Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs

Daylan

Portillo

Finkbeiner

2017

ApJ

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“…There exist several publications aiming at bringing the Bayesian modeling framework to spectral data analysis. Razul et al (2003); Fischer and Dose (2005); Wang et al (2008); Nagata et al (2012); Tokuda et al (2016) used Bayesian modeling combined with computational methods such as reversiblejump Markov chain Monte Carlo (RJMCMC) or the exchange Monte Carlo method for accurate spectrum variable estimation in various areas such as nuclear emission spectroscopy and mass spectrometry. For Raman spectral data analysis, Zhong et al (2011) used the Bayesian framework and combined Gibbs and RJMCMC sampler to infer mixture information from a set of multiplexed surface-enhanced Raman spectroscopy (SERS) measurements.…”

Section: Introductionmentioning

confidence: 99%

Bayesian modeling and computation for analyte quantification in complex mixtures using Raman spectroscopy

Han

Ram

2020

Computational Statistics & Data Analysis

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In this work, we propose a two-stage algorithm based on Bayesian modeling and computation aiming at quantifying analyte concentrations or quantities in complex mixtures with Raman spectroscopy. A hierarchical Bayesian model is built for spectral signal analysis, and reversible-jump Markov chain Monte Carlo (RJMCMC) computation is carried out for model selection and spectral variable estimation. Processing is done in two stages. In the first stage, the peak representations for a target analyte spectrum are learned. In the second, the peak variables learned from the first stage are used to estimate the concentration or quantity of the target analyte in a mixture. Numerical experiments validated its quantification performance over a wide range of simulation conditions and established its advantages for analyte quantification tasks under the small training sample size regime over conventional multivariate regression algorithms. We also used our algorithm to analyze experimental spontaneous Raman spectroscopy data collected for glucose concentration estimation in biopharmaceutical process monitoring applications. Our work shows that this algorithm can be a promising complementary tool alongside conventional multivariate regression algorithms in Raman spectroscopy-based mixture quantification studies, especially when collecting a large training dataset with high quality is challenging or resourceintensive.

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“…However, the use of other component functions has also been studied [18]. Several advantages of using mixture modeling for protein MS spectra are highlighted in the referenced studies [18][19][20][21][22][23][24]. Using mixture models potentially allows for more accurate peak detection and quantification.…”

Section: Introductionmentioning

confidence: 99%

“…Applications of mixture modeling to proteomic MS spectra were researched in [18][19][20][21][22][23][24] by analyzing proteomic actual mass spectra, or their fragments, and by conducting experiments involving fitting mixture models to data. A computational model and some exemplary results were presented in [22].…”

Section: Introductionmentioning

confidence: 99%

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

et al. 2015

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Mixture - modeling of mass spectra is an approach with many potential applications including peak detection and quantification, smoothing, de-noising, feature extraction and spectral signal compression. However, existing algorithms do not allow for automated analyses of whole spectra. Therefore, despite highlighting potential advantages of mixture modeling of mass spectra of peptide/protein mixtures and some preliminary results presented in several papers, the mixture modeling approach was so far not developed to the stage enabling systematic comparisons with existing software packages for proteomic mass spectra analyses. In this paper we present an efficient algorithm for Gaussian mixture modeling of proteomic mass spectra of different types (e.g., MALDI-ToF profiling, MALDI-IMS). The main idea is automated partitioning of protein mass spectral signal into fragments. The obtained fragments are separately decomposed into Gaussian mixture models. The parameters of the mixture models of fragments are then aggregated to form the mixture model of the whole spectrum. We compare the elaborated algorithm to existing algorithms for peak detection and we demonstrate improvements of peak detection efficiency obtained by using Gaussian mixture modeling. We also show applications of the elaborated algorithm to real proteomic datasets of low and high resolution.

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Reversible jump MCMC approach for peak identification for stroke SELDI mass spectrometry using mixture model

Cited by 17 publications

References 25 publications

Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs

Inference of Unresolved Point Sources at High Galactic Latitudes Using Probabilistic Catalogs

Bayesian modeling and computation for analyte quantification in complex mixtures using Raman spectroscopy

Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry

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