Detecting and aligning peaks in mass spectrometry data with applications to MALDI

Yu, Weichuan; Wu, Baolin; Lin, Ning; Stone, Kathy; Williams, K. R.; Zhao, Hongyu

doi:10.1016/j.compbiolchem.2005.10.006

Cited by 44 publications

(30 citation statements)

References 11 publications

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“…The default value of Min window size was set to 3, as suggested in [13], [25], [34]. The peak probability is the probability that a peak occurs in a time window of some size w, i.e., it is the probability that the sum of the intensity values within the time window exceeds the threshold fðwÞ associated with w. The peak probability is reversely proportional to the threshold-the smaller the peak probability, the larger the threshold is.…”

Section: Experimental Results On Synthetic Datamentioning

confidence: 99%

“…The first group is concerned with 3D peak detection in mass spectrometry data. Most 3D peak detection algorithms are based on either statistical distributions or a variety of smoothing functions [13], [34]. To reduce the number of false positives (i.e., those that are nonpeaks but are detected as peaks), these algorithms often assume a minimum peak width.…”

Section: Related Workmentioning

confidence: 99%

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Fast Elastic Peak Detection for Mass Spectrometry Data Mining

Zhang

Shasha

Song

et al. 2012

IEEE Trans. Knowl. Data Eng.

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Abstract-We study a data mining problem concerning the elastic peak detection in 2D liquid chromatography-mass spectrometry (LC-MS) data. These data can be modeled as time series, in which the X-axis represents time points and the Y -axis represents intensity values. A peak occurs in a set of 2D LC-MS data when the sum of the intensity values in a sliding time window exceeds a user-determined threshold. The elastic peak detection problem is to locate all peaks across multiple window sizes of interest in the data set. We propose a new data structure, called a Shifted Aggregation Tree or AggTree for short, and use the data structure to find the different peaks. Our method, called PeakID, solves the elastic peak detection problem in 2D LC-MS data yielding neither false positives nor false negatives. The method works by first constructing an AggTree in a bottom-up manner from the given data set, and then searching the AggTree for the peaks in a top-down manner. We describe a state-space algorithm for finding the topology and structure of an efficient AggTree to be used by PeakID. Our experimental results demonstrate the superiority of the proposed method over other methods on both synthetic and real-world data.Index Terms-Knowledge discovery from LC-MS data, time series data mining, bioinformatics, computational proteomics, algorithms and data structures.

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Section: Experimental Results On Synthetic Datamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Fast Elastic Peak Detection for Mass Spectrometry Data Mining

Zhang

Shasha

Song

et al. 2012

IEEE Trans. Knowl. Data Eng.

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show abstract

“…The number of features is further reduced to 5 709 by requiring that a peak must be observed in at least two samples to avoid noise peaks. Note that other spectra alignment algorithms are also good candidate for this task (Yu et al, 2006;Wong et al, 2005).…”

Section: Mass Spectra Peak Alignmentmentioning

confidence: 99%

Link test—A statistical method for finding prostate cancer biomarkers

Deng

Geng

Bastola

et al. 2006

Computational Biology and Chemistry

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We present a new method, link-test, to select prostate cancer biomarkers from SELDI mass spectrometry and microarray data sets. Biomarkers selected by link-test are supported by data sets from both mRNA and protein levels, and therefore results in improved robustness. Link-test determines the level of significance of the association between a microarray marker and a specific mass spectrum marker by constructing background mass spectra distributions estimated by all human protein sequences in the SWISS-PROT database. The data set consist of both microarray and mass spectrometry data from prostate cancer patients and healthy controls. A list of statistically justified prostate cancer biomarkers is reported by link-test. Cross-validation results show high prediction accuracy using the identified biomarker panel. We also employ text mining approach with OMIM database to validate the cancer biomarkers. The study with link-test represents one of the first crossplatform studies of cancer biomarkers.

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“…Previous studies have shown that the variation of peak location in different data sets is nonlinear [21], [10]. The example in [28] shows that this variation still exists even when we use technical replicates. The reasons that underlie data variation are extremely complicated.…”

mentioning

confidence: 93%

Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach

Liu

et al. 2006

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-In this paper, we address the multiple peak alignment problem in sequential data analysis with an approach based on the Gaussian scale-space theory. We assume that multiple sets of detected peaks are the observed samples of a set of common peaks. We also assume that the locations of the observed peaks follow unimodal distributions (e.g., normal distribution) with their means equal to the corresponding locations of the common peaks and variances reflecting the extension of their variations. Under these assumptions, we convert the problem of estimating locations of the unknown number of common peaks from multiple sets of detected peaks into a much simpler problem of searching for local maxima in the scale-space representation. The optimization of the scale parameter is achieved using an energy minimization approach. We compare our approach with a hierarchical clustering method using both simulated data and real mass spectrometry data. We also demonstrate the merit of extending the binary peak detection method (i.e., a candidate is considered either as a peak or as a nonpeak) with a quantitative scoring measure-based approach (i.e., we assign to each candidate a possibility of being a peak).

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Detecting and aligning peaks in mass spectrometry data with applications to MALDI

Cited by 44 publications

References 11 publications

Fast Elastic Peak Detection for Mass Spectrometry Data Mining

Fast Elastic Peak Detection for Mass Spectrometry Data Mining

Link test—A statistical method for finding prostate cancer biomarkers

Multiple Peak Alignment in Sequential Data Analysis: A Scale-Space-Based Approach

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