Finding regions of significance in SELDI measurements for identifying protein biomarkers

Tan, Chuen Seng; Ploner, Alexander; Quandt, Andreas; Lehtiö, Janne; Pawitan, Yudi

doi:10.1093/bioinformatics/btl106

Cited by 34 publications

(12 citation statements)

References 16 publications

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“…Data analysis is aimed at extracting relevant information in an unbiased way through the discovery and identification of biomarkers that allow the classification and prediction of clinical conditions. Several approaches to the analysis of proteomic mass spectrometry data have been used [84][85][86][87][88]. We have developed a novel computational approach that overcomes some of the obstacles and limitations of previous methods.…”

Section: Analysis Of Mass Spectrometry Datamentioning

confidence: 99%

Proteomic analysis of amniotic fluid to identify women with preterm labor and intra-amniotic inflammation/infection: The use of a novel computational method to analyze mass spectrometric profiling

Romero

Espinoza

Rogers³

et al. 2008

The Journal of Maternal-Fetal & Neonatal Medicine

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Objective. Examination of the amniotic fluid proteome has been used to identify biomarkers for intra-amniotic inflammation as well as those that may be useful in predicting the outcome of preterm labor. The purpose of this study was to combine a novel computational method of pattern discovery with mass spectrometric proteomic profiling of amniotic fluid to discover biomarkers of intra-amniotic infection/inflammation (IAI). Methods. This cross-sectional study included patients with spontaneous preterm labor and intact membranes who delivered at term (n ¼ 59) and those who delivered preterm with IAI (n ¼ 60). Proteomic profiling was performed using surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. A proteomic profile was acquired through multiple simultaneous SELDI conditions, which were combined in a single proteomic 'fingerprint' using a novel computational approach. Classification of patients based on their associated surface-enhanced laser desorption/ionizationtime of flight (SELDI-TOF) mass spectra as belonging to either the class of individuals with preterm delivery with IAI or term delivery was accomplished by constructing an empirical model. The first phase in the construction of this empirical model involved the selection of adjustable parameters utilizing a training/testing subset of data. The second phase tested the generalization of the model by utilizing a blinded validation set of patients who were not employed in parameter selection.Results. Gestational age at amniocentesis was not significantly different between the groups. Thirty-nine unique mass spectrometric peaks discriminated patients with preterm labor/delivery with IAI from those with preterm labor and term delivery. In the testing/training dataset, the classification accuracies (averaged over 100 random draws) were: 91.4% (40.2/ 44) for patients with preterm delivery with IAI, and 91.2% (40.1/44) for term delivery. The overall accuracy of the classification of patients in the validation dataset was 90.3% (28/31).Conclusions. Proteomic analysis of amniotic fluid allowed the identification of mass spectrometry features, which can distinguish patients with preterm labor with IAI from those with preterm labor without inflammation or infection who subsequently delivered at term.

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Section: Analysis Of Mass Spectrometry Datamentioning

confidence: 99%

Proteomic analysis of amniotic fluid to identify women with preterm labor and intra-amniotic inflammation/infection: The use of a novel computational method to analyze mass spectrometric profiling

Romero

Espinoza

Rogers³

et al. 2008

The Journal of Maternal-Fetal & Neonatal Medicine

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“…Normally, denoising is performed based on some denoising threshold (Coombes et al , 2005b; Tan et al , 2006). If the threshold is too high, some small peaks are erased along together with noise.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2008

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Mass spectrometry (MS) has shown great potential in detecting disease-related biomarkers for early diagnosis of stroke. To discover potential biomarkers from large volume of noisy MS data, peak detection must be performed first. This article proposes a novel automatic peak detection method for the stroke MS data. In this method, a mixture model is proposed to model the spectrum. Bayesian approach is used to estimate parameters of the mixture model, and Markov chain Monte Carlo method is employed to perform Bayesian inference. By introducing a reversible jump method, we can automatically estimate the number of peaks in the model. Instead of separating peak detection into substeps, the proposed peak detection method can do baseline correction, denoising and peak identification simultaneously. Therefore, it minimizes the risk of introducing irrecoverable bias and errors from each substep. In addition, this peak detection method does not require a manually selected denoising threshold. Experimental results on both simulated dataset and stroke MS dataset show that the proposed peak detection method not only has the ability to detect small signal-to-noise ratio peaks, but also greatly reduces false detection rate while maintaining the same sensitivity.Contact: XZhou@tmhs.org

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“…Spectra are generally normalized in order to equalize or minimize differential effect due to external variation [59, 115, 116, 118, 119]. The widely used total ion current (TIC) gives a clear indication of the impacts of technical variables such as laser and detector performances, matrix application, and sample amounts.…”

Section: Discussionmentioning

confidence: 99%

Challenges for Biomarker Discovery in Body Fluids Using SELDI-TOF-MS

Bock

Seny

Meuwis

et al. 2010

Journal of Biomedicine and Biotechnology

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Protein profiling using SELDI-TOF-MS has gained over the past few years an increasing interest in the field of biomarker discovery. The technology presents great potential if some parameters, such as sample handling, SELDI settings, and data analysis, are strictly controlled. Practical considerations to set up a robust and sensitive strategy for biomarker discovery are presented. This paper also reviews biological fluids generally available including a description of their peculiar properties and the preanalytical challenges inherent to sample collection and storage. Finally, some new insights for biomarker identification and validation challenges are provided.

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Finding regions of significance in SELDI measurements for identifying protein biomarkers

Abstract: We show that RS has better operating characteristics than several existing methods and demonstrate routine applications on a number of large datasets.

Cited by 34 publications

References 16 publications

Proteomic analysis of amniotic fluid to identify women with preterm labor and intra-amniotic inflammation/infection: The use of a novel computational method to analyze mass spectrometric profiling

Proteomic analysis of amniotic fluid to identify women with preterm labor and intra-amniotic inflammation/infection: The use of a novel computational method to analyze mass spectrometric profiling

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

Challenges for Biomarker Discovery in Body Fluids Using SELDI-TOF-MS

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