24 papers · ranked by Valyu relevance
Haoyu Wang, Changqing Song, Jinfeng Wang, Peichao Gao
Spatial clustering is an essential method for the comprehensive understanding of a region. Spatial clustering divides all spatial units into different clusters. The attributes of each cluster of the spatial units are similar, and simultaneously, they are as continuous as spatially possible. In spatial clustering, the…
Rajitha Senanayake, Pratheepa Jeganathan
Identifying spatially contiguous clusters and repeated spatial patterns (RSP) characterized by similar underlying distributions that are spatially apart is a key challenge in modern spatial statistics. Existing constrained clustering methods enforce spatial contiguity but are limited in their ability to identify RSP.…
Lee Mason, Blánaid Hicks, Jonas S. Almeida, Nazarudin Safian
Spatial cluster analysis is crucial for understanding localized patterns in geospatial data, with wide-ranging applications for scientific discovery and decision-making. However, the dynamic nature of spatial clusters and the diverse range of clustering methods available can make analysis and interpretation…
Jongwon Kim, Jeongho Cho
In spatial data with complexity, different clusters can be very contiguous, and the density of each cluster can be arbitrary and uneven. In addition, background noise that does not belong to any clusters in the data, or chain noise that connects multiple clusters may be included. This makes it difficult to separate…
Yinqiao Yan, Xiangnan Feng, Xiangyu Luo
The spatial transcriptomics (ST) clustering plays a crucial role in elucidating the tissue spatial heterogeneity, which is referred to as region segmentation for spot level ST data and spatial cell typing for single-cell ST data. An accurate ST clustering result can greatly benefit downstream biological analyses. As…
Lee Mason, Blánaid Hicks, Jonas S. Almeida
of Spatial Clusters Over Time Authors: Lee Mason, Blánaid Hicks, Jonas S. Almeida Title: ClusterRadar: an Interactive Web-Tool for the Multi-Method Exploration of Spatial Clusters Over Time Authors: Lee Mason, Blánaid Hicks, Jonas S. Almeida Content: # 1 INTRODUCTION Space plays a critical role in many real-world…
Vipul Singhal, Nigel Chou, Joseph Lee, Jinyue Liu + 7 more
Each cell type in a solid tissue has a characteristic transcriptome and spatial arrangement, both of which are observable using modern spatial omics assays. However, the common practice is still to ignore spatial information when clustering cells to identify cell types. In fact, spatial location is typically considered…
Justin Lessler, Henrik Salje, M. Kate Grabowski, Derek A. T. Cummings + 1 more
'Derek A. T. Cummings' 'Michiel van Boven'] Global spatial clustering is the tendency of points, here cases of infectious disease, to occur closer together than expected by chance. The extent of global clustering can provide a window into the spatial scale of disease transmission, thereby providing insights into the…
Tianbo Chen, Ying Sun, Mehdi Maadooliat
In this paper, we develop a method for estimating and clustering two-dimensional spectral density functions (2D-SDFs) for spatial data from multiple subregions. We use a common set of adaptive basis functions to explain the similarities among the 2D-SDFs in a low-dimensional space and estimate the basis coefficients by…
Rebeca Ramis, Diana Gómez-Barroso, Ibon Tamayo, Javier García-Pérez + 4 more
For our two objectives, overall spatial clustering analysis and cluster detection, we used two different methodologies. The first, for overall spatial clustering detection, was the differences of K functions from the spatial point patterns perspective proposed by Diggle and Chetwynd . The second, for cluster detection…
Pratibha Panwar, Boyi Guo, Haowen Zhou, Stephanie C. Hicks + 1 more
The increased uptake of high-resolution spatially-resolved transcriptomics (SRT) technologies demands the development of unsupervised methods to extract cell types and their spatial distribution from biological tissues. However, unsupervised clustering is challenging due to the sparsity of the data and the differences…
Ben Moews, Antonia Gieschen
Spatio-temporal clustering occupies an established role in various fields dealing with geospatial analysis, spanning from healthcare analysis to environmental science. One major challenge are applications in which cluster assignments are dependent on local densities, meaning that higher-density areas should be treated…
Huimin Li, Xi Jiang, Lei Guo, Yang Xie + 2 more
Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many…
Caterina Morelli, Paolo Maranzano, Philipp Otto
In this study, we propose a novel application of spatiotemporal clustering in the environmental sciences, with a particular focus on regionalised time series of greenhouse gases (GHGs) emissions from a range of economic sectors. Utilising a hierarchical spatiotemporal clustering methodology, we analyse yearly time…
Mary Pitman, David Hahn, Gary Tresadern, David Mobley
Drug discovery is accelerated with computational methods such as alchemical simulations to estimate ligand affinities. In particular, relative binding free energy (RBFE) simulations are beneficial for lead optimization. To use RBFE simulations to compare prospective ligands in silico, researchers first plan the…
Joao C. Marques, Michael B. Orger
How to partition a data set into a set of distinct clusters is a ubiquitous and challenging problem. The fact that data sets vary widely in features such as cluster shape, cluster number, density distribution, background noise, outliers and degree of overlap, makes it difficult to find a single algorithm that can be…
Mridula Prasad, Geert Postma, Pietro Franceschi, Lutgarde M. C. Buydens + 1 more
'Lutgarde M. C. Buydens' 'Jeroen J. Jansen'] For the extraction of spatially important regions from mass spectrometry imaging (MSI) data, different clustering methods have been proposed. These clustering methods are based on certain assumptions and use different criteria to assign pixels into different classes. For…
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We present a gridless framework for computing high-dimensional conformational free energy surfaces (FES) of flexible molecules using enhanced sampling trajectories. By combining concurrent well-tempered metadynamics with Density Peaks Advanced (DPA) clustering, our approach bypasses the dimensionality limitations of…
Maximilian Woollard, Pratibha Panwar, Luke T.G. Harland, Jeremy Mo + 5 more
With the dramatic take-up of spatially resolved transcriptomics biotechnologies, performing spatially-aware analysis of the resulting data is crucial to maximise advances in biological understanding. Dimensionality reduction is a first step in almost any analysis of spatial transcriptomics data, regardless of whether…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Evangelos Karatzas, Maria Gkonta, Joana Hotova, Fotis A. Baltoumas + 4 more
Clustering is the process of grouping together different data objects based on similar properties. Clustering has applications in various case studies from several fields such as graph theory, image analysis, pattern recognition, statistics and others. Nowadays, there are numerous algorithms and tools able to generate…
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This work investigates different formulations of internal Coordinates for molecular dynamics (MD) simulations. The goal is to assess their advantages and limitations. Furthermore, a method is presented that evaluates the quality of the partitioning of molecular structural data into clusters based on statistical…
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Recent advances in artificial intelligence have significantly improved spectral data analysis. In this study, we used unsupervised machine learning to classify chemical compounds based on infrared (IR) spectral images, without relying on prior chemical knowledge. The potential of machine learning for chemical…
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The screening of chemical libraries is an essential starting point in the drug discovery process. While some researchers desire a more thorough screening of drug targets against a narrower scope of molecules, it is not uncommon for diverse screening sets to be favored during early stages of drug discovery. However, a…