Dataset Information

Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding.

ABSTRACT: Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions, providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive, as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentation approach, which combines multivariate clustering and univariate thresholding to generate comprehensive spatial segmentation maps of the MSI data. This approach combines matrix factorization and manifold learning to enable high-quality image segmentation without an extensive hyperparameter search. In parallel, some ion images inadequately represented in the multivariate analysis were treated using univariate thresholding to generate complementary spatial segments. The final spatial segmentation map was assembled from segment candidates that were generated using both techniques. We demonstrate the performance and robustness of this approach for two MSI data sets of mouse uterine and kidney tissue sections that were acquired with different spatial resolutions. The resulting segmentation maps are easy to interpret and project onto the known anatomical regions of the tissue.

SUBMITTER: Hu H

PROVIDER: S-EPMC7904669 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding.

Hu Hang H Yin Ruichuan R Brown Hilary M HM Laskin Julia J

Analytical chemistry 20210211 7

Spatial segmentation partitions mass spectrometry imaging (MSI) data into distinct regions, providing a concise visualization of the vast amount of data and identifying regions of interest (ROIs) for downstream statistical analysis. Unsupervised approaches are particularly attractive, as they may be used to discover the underlying subpopulations present in the high-dimensional MSI data without prior knowledge of the properties of the sample. Herein, we introduce an unsupervised spatial segmentat ...[more]

PMID: 33570915

Similar Datasets

Project description:Recent developments in psychoradiological researches have highlighted the disrupted organization of large-scale functional brain networks in obsessive-compulsive disorder (OCD). However, whether abnormal activation of localized brain areas would affect network dysfunction remains to be fully characterized. We applied both univariate analysis and multivariate pattern analysis (MVPA) approaches to investigate the abnormalities of regional homogeneity (ReHo), an index to measure the localized connectivity, in 88 medication-free patients with OCD and 88 healthy control subjects (HCS). Resting-state functional magnetic resonance imaging (RS-fMRI) data of all the participants were acquired in a 3.0-T scanner. First, we adopted a traditional univariate analysis to explore ReHo alterations between the patient group and the control group. Subsequently, we utilized a support vector machine (SVM) to examine whether ReHo could be further used to differentiate patients with OCD from HCS at the individual level. Relative to HCS, OCD patients showed lower ReHo in the bilateral cerebellum and higher ReHo in the bilateral superior frontal gyri (SFG), right inferior parietal gyrus (IPG), and precuneus [P < 0.05, family-wise error (FWE) correction]. ReHo value in the left SFG positively correlated with Yale-Brown Obsessive Compulsive Scale (Y-BOCS) total score (r = 0 0.241, P = 0.024) and obsessive subscale (r = 0.224, P = 0.036). The SVM classification regarding ReHo yielded an accuracy of 78.98% (sensitivity = 78.41%, specificity = 79.55%) with P < 0.001 after permutation testing. The most discriminative regions contributing to the SVM classification were mainly located in the frontal, temporal, and parietal regions as well as in the cerebellum while the right orbital frontal cortex was identified with the highest discriminative power. Our findings not only suggested that the localized activation disequilibrium between the prefrontal cortex (PFC) and the cerebellum appeared to be associated with the pathophysiology of OCD but also indicated the translational role of the localized connectivity as a potential discriminative pattern to detect OCD at the individual level.

Dataset Information

Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding.

Publications

Spatial Segmentation of Mass Spectrometry Imaging Data by Combining Multivariate Clustering and Univariate Thresholding.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets