ArticlePloS one2015
Signal Partitioning Algorithm for Highly Efficient Gaussian Mixture Modeling in Mass Spectrometry.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- DiviK: divisive intelligent K-means for hands-free unsupervised clustering in big biological data.BMC bioinformatics · 2022Article
- Prognostic Value of Molecular Intratumor Heterogeneity in Primary Oral Cancer and Its Lymph Node Metastases Assessed by Mass Spectrometry Imaging.Molecules (Basel, Switzerland) · 2022Article
- Intra-Tumor Heterogeneity Revealed by Mass Spectrometry Imaging Is Associated with the Prognosis of Breast Cancer.Cancers · 2021Article
- Algorithmic decomposition for efficient multiple nuclear spin detection in diamond.Scientific reports · 2020Article
- Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach.International journal of molecular sciences · 2020Article
- Molecular Heterogeneity of Papillary Thyroid Cancer: Comparison of Primary Tumors and Synchronous Metastases in Regional Lymph Nodes by Mass Spectrometry Imaging.Endocrine pathology · 2019Article
- BatchI: Batch effect Identification in high-throughput screening data using a dynamic programming algorithm.Bioinformatics (Oxford, England) · 2019Article
- Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids.Journal of molecular histology · 2019Article
- Article
- Evaluation of semi-automatic image analysis tools for cerebrospinal fluid electrophoresis of IgG oligoclonal bands.Practical laboratory medicine · 2018Article
- Article
- Elastic K-means using posterior probability.PloS one · 2017Article
- Article
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5 authors.
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Abstract
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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