27 papers · ranked by Valyu relevance
Fang Liu, Rui He, Thomas Sheeley, David A. Scheiblin + 6 more
Background Advancements in spatially resolved single-cell technologies are transforming our understanding of tissue architecture and disease microenvironments. However, analyzing the resulting high-dimensional, gigabyte-scale datasets remains challenging due to fragmented workflows, intensive computational…
Cindy Ogolla Jean-Baptiste
Highlights What are the main findings?1. Geospatial epidemiology uncovers spatially patterned vulnerabilities driven by ecological, structural, and built-environment determinants. 2. The proposed architecture translates multilevel data into localized risk signatures for precision prevention strategies. What are the…
Ke Hu, Chaojie Li, Xingjin Yang, Shuiping Ou + 3 more
Spatial epidemiology, as an important branch of epidemiology, has undergone a significant paradigm shift from infectious disease prevention and control to chronic disease management. This paper systematically reviews the application progress of spatial epidemiology in the study of infectious diseases (e.g., malaria…
Samuel Gunz, Helena L. Crowell, Mark D. Robinson
Spatial omics technologies enable high-resolution, large-scale quantification of molecular features while preserving the spatial context within tissues. Existing analysis methods largely focus on spatial arrangements of single cells, whereas biological function often emerges from multicellular arrangements. Here, we…
Ke Hu, Xingjin Yang, Shuiping Ou, Chaojie Li + 3 more
Introduction Dysentery remains a significant notifiable Class B infectious disease in China, exhibiting distinct spatial variations in incidence patterns. This persistent geographical heterogeneity necessitates a systematic investigation into the underlying influencing factors to inform targeted prevention and control…
Adnane Nemri, Ovidiu Radulescu, Antoine Claessens, Thomas D. Otto
Despite the advent of spatial data science, including spatial biology, there exist few methods that study the distribution of points e.g. cells or individuals, accounting for both their own characteristics and environmental factors. We propose a new spatial entropy measure, termed the Regional Co-occurrence Entropy…
Helena L. Crowell, Yixing Dong, Ilaria Billato, Peiying Cai + 30 more
Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows…
Rosalin A Cooper, Emily Thomas, Muhammad Dawood, Hosuk Ryou + 12 more
The bone marrow (BM) is the main site of haematopoiesis in adult life. Our understanding of the pathogenesis of BM-derived blood cancers is limited by lack of spatial contextualisation. While emerging spatial transcriptomic (ST) platforms offer unprecedented opportunities for spatially-resolved cellular phenotyping, we…
Mohammad Faiz Iqbal Faiz, Elliot Jokl, Rachel Jennings, Karen Piper Hanley + 3 more
Spatial transcriptomics is rapidly advancing toward single cell level resolution, revealing complex tissue architectures organized across continuous anatomical gradients. However, accurate identification of spatial domains remains a central computational challenge, as many existing clustering approaches blur anatomical…
Helena L. Crowell, Yixing Dong, Ilaria Billato, Peiying Cai + 30 more
Spatial transcriptomics technologies provide spatially-resolved measurements of gene expression through assays that can either target selected genes or capture transcriptome-wide expression profiles. The complexity and variability of these technologies and their associated data necessitate multi-step workflows…
Zhen-Hao Guo, De-Shuang Huang, Shihua Zhang
The rapid advancement of spatial omics technology has revolutionized biomedical research, unveiling unprecedented insights into the molecular and cellular architecture of biological systems. Despite this progress, a critical limitation arises from the widespread use of disparate analytical tools within a single…
Chelsea R. Baker, Ivan Barilar, Leonardo S. de Araujo, Daniel M. Parker + 9 more
Objectives Our objective was to identify potential ‘hotspots’ of tuberculosis (TB) transmission by integrating pathogen genomic and geospatial data, including activity spaces where transmission may occur (e.g. community locations such as work, school, or social venues) in addition to residential locations. Methods We…
Christoffer M. Langseth, Bastien Hervé, Hanna P. Piechaczyk, Yuk Kit Lor + 2 more
Spatial omics technologies enable high-resolution mapping of molecular and cellular organization within tissues, yet interactive exploration of these data remains challenging due to computational bottlenecks, and reliance on proprietary software infrastructures. We present KaroSpace, a framework for cell-centric…
Ke Hu, Xingjin Yang, Yu Cai, Chaojie Li + 3 more
Introduction Gonorrhea is a major sexually transmitted infection in China, showing distinct regional clustering and spatial heterogeneity. Understanding its geographical distribution and influencing factors is crucial for targeted prevention. Methods We analyzed 2022 gonorrhea incidence across 31 Chinese provinces…
Jinpu Li, Mauminah Raina, Yiqing Wang, Shuai Zeng + 20 more
Emerging spatial multi-omics technologies enable the profiling of molecular variation within its tissue context, yet existing methods for identifying spatially variable features lack principled approaches to experimental design and cross-sample inference. Here, we present STORM, a principled Statistical TOol for…
Levi John Wolf, Wei Kang
The Moran statistic, and its accompanying local statistics, are one of the most extensively used exploratory spatial data analysis tools for assessing global and local spatial autocorrelation. The paired visualizations for these statistics, the Moran Scatterplot and LISA map, are likewise central to spatial analysis.…
Jingwen Deng, Shujie Ma, Sergio J. Rey, Guanyu Hu
Income inequality is a major contributor to health disparities, yet its effects often vary by geography and are commonly represented as compositional distributions (e.g., proportions of households across income brackets). Existing spatial regression methods struggle in this setting: they typically assume smooth spatial…
Debjoy Thakur, Lingyuan Zhao, Soutir Bandyopadhyay
A major public health concern in the United States (US) is gunrelated deaths. The number of gun injuries largely varies spatially because of county-wise heterogeneity of race, sex, age, and income distributions. But still, a major challenge is to locally identify the influential socio-economic factors behind these…
Isqeel Ogunsola, Olatunji Johnson
Spatial interference and spatial confounding are two major issues inhibiting precise causal estimates when dealing with observational spatial data. Moreover, the definition and interpretation of spatial confounding remain arguable in the literature. In this paper, our goal is to provide clarity in a novel way on…
Shuqi Lin, Joshua L. Warren
Although spatial models for areal data are widely used in multilevel settings, the conditions under which spatial and nonspatial random effects yield equivalent posterior inference for regression coefficients have never been formally characterized. We address this question within a hierarchical Bayesian framework for…
Perttu Saarela, Klaus Nordhausen, Jaakko Pere, Anne M. Ruiz
Stationary subspace analysis (SSA) is a blind source separation framework that decomposes linearly mixed multivariate data into stationary and nonstationary components. We extend SSA to spatially indexed data by introducing spatial stationary subspace analysis (spSSA), which explicitly accounts for spatial dependence.…
Isabella Habereder, Thomas Kneib, Isao Echizen, Timo Spinde
Data with spatial-temporal attributes are prevalent across many research fields, and statistical models for analyzing spatio-temporal relationships are widely used. Existing reviews focus either on specific domains or model types, creating a gap in comprehensive, cross-disciplinary overviews. To address this, we…
Authors not listed
Biochar, produced via biomass pyrolysis, is an emerging method for carbon removal in agriculture. However, its uncertain environmental outcomes hinder large-scale adoption. We address this using physics-informed machine learning (ML) to integrate kinetics-based pyrolysis modeling and global datasets of field…
Authors not listed
In the United States, people of color are disproportionately and unjustly exposed to air pollution. Historically, environmental policy has emphasized aggregate emission reductions; yet major emission reduction scenarios do not sufficiently mitigate relative exposure disparities. Here, we show that without focusing on…
Authors not listed
Understanding how plants respond to dynamic and spatially variable stimuli is a key goal in plant sciences. Traditional imaging methods often involve a trade-off between environmental control and spatial resolution, limiting their ability to capture real-time responses in high resolution. Microfluidic technology…
Authors not listed
People of color in the United States are disproportionately and unfairly exposed to air pollution. Equity-oriented scientific evaluations quantifying these disparities often use population-average exposure metrics to capture the overall inequality within a system. Utilizing these metrics involves choices about the…
Julius Rauscher, Frederik L. Dennig, Udo Schlegel, Daniel A. Keim + 1 more
The analysis of spatiotemporal data is essential in domains such as epidemiology and environmental monitoring, where understanding the interplay between spatially distributed phenomena and their temporal evolution is critical. Dense pixel visualizations offer a compact, effective overview of spatiotemporal dynamics.…