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

Polański, Andrzej; Marczyk, Michał; Pietrowska, Monika; Widłak, Piotr; Polańska, Joanna

doi:10.1371/journal.pone.0134256

Cited by 34 publications

(26 citation statements)

References 44 publications

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“…The MSI spectra dataset was preprocessed by performing mass channels unification, baseline subtraction [ 35 ], outlying spectra identification according to TIC (total ion current) value using criterion for skewed and heavy-tailed distributions [ 36 ], fast Fourier transform-based peak alignment to reference average spectrum [ 37 ], and TIC normalization. Gaussian mixture modeling (GMM) of the average spectrum was applied for peak detection as described in detail elsewhere [ 38 , 39 ]. GMM components of low amplitude and high variance were removed from initial GMM spectra representation; GMM components modeling the same spectrum peak were merged by summing their estimated abundance and setting the location of a dominant component as mass/charge value of a peptide ion.…”

Section: Methodsmentioning

confidence: 99%

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

Kurczyk

Gawin

Chekan

et al. 2020

IJMS

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The primary diagnosis of thyroid tumors based on histopathological patterns can be ambiguous in some cases, so proper classification of thyroid diseases might be improved if molecular biomarkers support cytological and histological assessment. In this work, tissue microarrays representative for major types of thyroid malignancies—papillary thyroid cancer (classical and follicular variant), follicular thyroid cancer, anaplastic thyroid cancer, and medullary thyroid cancer—and benign thyroid follicular adenoma and normal thyroid were analyzed by mass spectrometry imaging (MSI), and then different computation approaches were implemented to test the suitability of the registered profiles of tryptic peptides for tumor classification. Molecular similarity among all seven types of thyroid specimens was estimated, and multicomponent classifiers were built for sample classification using individual MSI spectra that corresponded to small clusters of cells. Moreover, MSI components showing the most significant differences in abundance between the compared types of tissues detected and their putative identity were established by annotation with fragments of proteins identified by liquid chromatography-tandem mass spectrometry in corresponding tissue lysates. In general, high accuracy of sample classification was associated with low inter-tissue similarity index and a high number of components with significant differences in abundance between the tissues. Particularly, high molecular similarity was noted between three types of tumors with follicular morphology (adenoma, follicular cancer, and follicular variant of papillary cancer), whose differentiation represented the major classification problem in our dataset. However, low level of the intra-tissue heterogeneity increased the accuracy of classification despite high inter-tissue similarity (which was exemplified by normal thyroid and benign adenoma). We compared classifiers based on all detected MSI components (n = 1536) and the subset of the most abundant components (n = 147). Despite relatively higher contribution of components with significantly different abundance and lower overall inter-tissue similarity in the latter case, the precision of classification was generally higher using all MSI components. Moreover, the classification model based on individual spectra (a single-pixel approach) outperformed the model based on mean spectra of tissue cores. Our result confirmed the high feasibility of MSI-based approaches to multi-class detection of cancer types and proved the good performance of sample classification based on individual spectra (molecular image pixels) that overcame problems related to small amounts of heterogeneous material, which limit the applicability of classical proteomics.

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Section: Methodsmentioning

confidence: 99%

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

Kurczyk

Gawin

Chekan

et al. 2020

IJMS

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“…The basic preprocessing steps included: spectrum resampling, adaptive baseline correction (Bednarczyk et al 2017 ), identification of the outlying spectra (as those with too big or too small TIC) with the use of Bruffaerts’ criterion for extremely skewed distributions (Bruffaerts et al 2014 ), spectra alignment to the average spectrum based on Fast Fourier Transformation (Wong et al 2005 ), and TIC normalization. Gaussian mixture model (GMM) approach (Polanski et al 2015 ) was used for the average spectrum modeling and peak detection. GMM components of high variance and/or low amplitude were filtered out reducing the data dimensionality.…”

Section: Methodsmentioning

confidence: 99%

Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids

et al. 2018

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Identification of biomarkers for molecular classification of cancer and for differentiation between cancerous and normal epithelium remains a vital issue in the field of head and neck cancer. Here we aimed to compare the ability of proteome and lipidome components to discriminate oral cancer from normal mucosa. Tissue specimens including squamous cell cancer and normal epithelium were analyzed by MALDI mass spectrometry imaging. Two molecular domains of tissue components were imaged in serial sections—peptides (resulting from trypsin-processed proteins) and lipids (primarily zwitterionic phospholipids), then regions of interest corresponding to cancer and normal epithelium were compared. Heterogeneity of cancer regions was higher than the heterogeneity of normal epithelium, and the distribution of peptide components was more heterogeneous than the distribution of lipid components. Moreover, there were more peptide components than lipid components that showed significantly different abundance between cancer and normal epithelium (median of the Cohen’s effect was 0.49 and 0.31 in case of peptide and lipid components, respectively). Multicomponent cancer classifier was tested (vs. normal epithelium) using tissue specimens from three patients and then validated with a tissue specimen from the fourth patient. Peptide-based signature and lipid-based signature allowed cancer classification with a weighted accuracy of 0.85 and 0.69, respectively. Nevertheless, both classifiers had very high precision (0.98 and 0.94, respectively). We concluded that though molecular differences between cancerous and normal mucosa were higher in the proteome domain than in the analyzed lipidome subdomain, imaging of lipidome components also enabled discrimination of oral cancer and normal epithelium. Therefore, both cancer proteome and lipidome are promising sources of biomarkers of oral malignancies.Electronic supplementary materialThe online version of this article (10.1007/s10735-018-9802-3) contains supplementary material, which is available to authorized users.

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“…Standard spectrum preprocessing sequenced steps were applied as follows: (i) resampling to common mass channels, (ii) adaptive baseline detection and correction [24], (iii) outlying spectra identification according to TIC value using Bruffaerts' criterion [25], (iv) fast Fourier transform-based spectral alignment [26], and (v) TIC normalization. The Gaussian mixture model (GMM) approach described in detail elsewhere [27,28] was used for the average spectrum modeling and peak detection. Peptide abundance was estimated by pairwise convolution of the GMM components and individual spectra.…”

Section: Spectra Processing and Identification Of Spectral Componentsmentioning

confidence: 99%

Molecular Heterogeneity of Papillary Thyroid Cancer: Comparison of Primary Tumors and Synchronous Metastases in Regional Lymph Nodes by Mass Spectrometry Imaging

et al. 2019

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Intra-tumor heterogeneity results from both genetic heterogeneity of cancer (sub)clones and phenotypic plasticity of cancer cells that could be induced by different local microenvironments. Here, we used mass spectrometry imaging (MSI) to compare molecular profiles of primary tumors located in the thyroid gland and their synchronous metastases in regional lymph nodes to analyze phenotypic heterogeneity in papillary thyroid cancer. Two types of cancerous (primary tumor and metastasis) and two types of not cancerous (thyroid gland and lymph node) regions of interest (ROIs) were delineated in postoperative material from 11 patients, then the distribution of tryptic peptides (spectral components) was analyzed by MSI in all tissue regions. Moreover, tryptic peptides identified by shotgun proteomics in corresponding tissue lysates were matched to components detected by MSI to enable their hypothetical protein annotation. Unsupervised segmentation of all cancer ROIs revealed that different clusters dominated in tumor ROIs and metastasis ROIs. The intra-patient similarity between thyroid and tumor ROIs was higher than the intra-patient similarity between tumor and metastasis ROIs. Moreover, the similarity between tumor and its metastasis from the same patients was lower than similarities among tumors and among metastases from different patients (inter-patient similarity was higher for metastasis ROIs than for tumor ROIs). Components differentiating between tumor and its metastases were annotated as proteins involved in the organization of the cytoskeleton and chromatin, as well as proteins involved in immunity-related functions. We concluded that phenotypical heterogeneity between primary tumor and lymph node metastases from the same patient was higher than inter-tumor heterogeneity between primary tumors from different patients.

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

Cited by 34 publications

References 44 publications

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

Classification of Thyroid Tumors Based on Mass Spectrometry Imaging of Tissue Microarrays; a Single-Pixel Approach

Discrimination of normal oral mucosa from oral cancer by mass spectrometry imaging of proteins and lipids

Molecular Heterogeneity of Papillary Thyroid Cancer: Comparison of Primary Tumors and Synchronous Metastases in Regional Lymph Nodes by Mass Spectrometry Imaging

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