Showing papers on "Spatial analysis published in 2022"
TL;DR: Squid as discussed by the authors is a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data.
Abstract: Spatial omics data are advancing the study of tissue organization and cellular communication at an unprecedented scale. Flexible tools are required to store, integrate and visualize the large diversity of spatial omics data. Here, we present Squidpy, a Python framework that brings together tools from omics and image analysis to enable scalable description of spatial molecular data, such as transcriptome or multivariate proteins. Squidpy provides efficient infrastructure and numerous analysis methods that allow to efficiently store, manipulate and interactively visualize spatial omics data. Squidpy is extensible and can be interfaced with a variety of already existing libraries for the scalable analysis of spatial omics data.
186 citations
TL;DR: SpatialDecon as mentioned in this paper is an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies, using log-normal regression and modeling background.
Abstract: Abstract Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.
67 citations
TL;DR: SpatialDecon as discussed by the authors is an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies, using log-normal regression and modeling background.
Abstract: Abstract Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.
55 citations
TL;DR: The count of open source software packages hosted by the Comprehensive R Archive Network (CRAN) using key spatial data handling packages has now passed 1,000 as mentioned in this paper , and providing a comprehensive review of these packages is beyond the scope of an article.
Abstract: The count of open source software packages hosted by the Comprehensive R Archive Network (CRAN) using key spatial data handling packages has now passed 1,000. Providing a comprehensive review of these packages is beyond the scope of an article. Consequently, this review takes the form of a comparative case study, reproducing some of the approach and workflow of a spatial analysis of a data set including almost all the census tracts in the coterminous United States. The case study moves from visualization and the construction of a spatial weights matrix, to exploratory spatial data analysis and spatial regression. For comparison, implementations of the same steps in PySAL and GeoDa are interwoven, and points of convergence and divergence noted and discussed. Conclusions are drawn about the usefulness of open source software, the significance of sharing contributions both in software implementation but also more broadly in reproducible research, and in opportunities for exchanging ideas and solutions with other research domains.
44 citations
TL;DR: Li et al. as discussed by the authors used the Back Propagation neural network model to predict the carbon neutral capacity of China's provinces, which provided theoretical and data support for China to introduce corresponding zero-carbon solutions based on its understanding of CNC.
44 citations
TL;DR: Zhang et al. as discussed by the authors developed a graph attention auto-encoder framework to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles.
Abstract: Recent advances in spatially resolved transcriptomics have enabled comprehensive measurements of gene expression patterns while retaining the spatial context of the tissue microenvironment. Deciphering the spatial context of spots in a tissue needs to use their spatial information carefully. To this end, we develop a graph attention auto-encoder framework STAGATE to accurately identify spatial domains by learning low-dimensional latent embeddings via integrating spatial information and gene expression profiles. To better characterize the spatial similarity at the boundary of spatial domains, STAGATE adopts an attention mechanism to adaptively learn the similarity of neighboring spots, and an optional cell type-aware module through integrating the pre-clustering of gene expressions. We validate STAGATE on diverse spatial transcriptomics datasets generated by different platforms with different spatial resolutions. STAGATE could substantially improve the identification accuracy of spatial domains, and denoise the data while preserving spatial expression patterns. Importantly, STAGATE could be extended to multiple consecutive sections to reduce batch effects between sections and extracting three-dimensional (3D) expression domains from the reconstructed 3D tissue effectively.
44 citations
TL;DR: A Laplacian pyramid pansharpening network architecture for accurately fusing a high spatial resolution panchromatic image and a low spatial resolution multispectral image, which outperforms state-of-the-art panshARPening methods.
41 citations
TL;DR: In this paper , a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers, is presented, which builds multiple views focusing on different spatial or functional contexts to dissect different effects.
Abstract: The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy's results to clinical features.
38 citations
TL;DR: In this article, the authors explored approaches to refine calibration data, integrate novel predictors, and optimize classifier implementation for land cover mapping, and implemented a regionalized approach for optimizing training data selection and model-building.
34 citations
TL;DR: In this article , a framework for the assessment of regional spatial patterns in land use/cover changes (LUCC) and ecosystem service value (ESV) was established; the framework relies on the application of a classical spatial autocorrelation model.
34 citations
TL;DR: In this paper , the spatial effects of urbanization level (UL) on ecosystem services (ESs) were identified with an integrated spatial panel approach by decomposing the spatial autocorrelation and spatial spillover effects at multiscales into direct, indirect, and total effects in the Middle Reaches of the Yangtze River Urban Agglomerations (MRYRUA) of China.
TL;DR: An efficient variant named the fuzzy clustering algorithm with variable multi-pixel fitting spatial information (FCM-VMF) is presented, which has extremely high efficiency and has a better prospect of application.
TL;DR: Wang et al. as discussed by the authors analyzed the spatiotemporal evolution of urban land and airborne concentrations of fine particulate matter (PM2.5) in China through global autocorrelation, local spatial auto-correlation, and multiscale geo-weighted regression analysis, thus revealing the relationship between them.
Abstract: This study analyzed the spatiotemporal evolution of urban land and airborne concentrations of fine particulate matter (PM2.5) in China through global autocorrelation, local spatial autocorrelation, and multiscale geo-weighted regression analysis, thus revealing the relationship between them. The results were as follows:1. Built-up, construction, and residential areas in China showed significant spatial clustering from 2006 to 2018. The built-up and residential areas were mainly characterized by high-high agglomeration and high-low agglomeration, with high-high agglomeration characteristics varying more over time and high-low agglomeration characteristics varying less over time. The construction area was mainly characterized by high-high agglomeration, high-low agglomeration, and low-high agglomeration, with all three agglomeration characteristics showing significant changes over time. 2. Between 2006 and 2018, PM2.5 concentrations in China exhibited significant spatial clustering, which were mainly characterized by high-high clustering and low-low clustering. High-high spatial agglomeration varied more over time, evolving gradually from two agglomerations (one in the east and one in the west) to one agglomeration in the east. Meanwhile, the low-low spatial agglomeration showed relatively little change over time, with only slight changes in the southwest and northeast. 3. Between 2006 and 2018, spatial heterogeneity was significantly correlated between the urban land area and PM2.5 concentration; the effect of change in the built-up area on PM2.5 concentration was more stable, and the two showed a significant correlation. However, the influence of two variables (construction area and residential area) on the concentration of PM2.5, was not sufficient, and the correlation between the two variables and PM2.5 concentration gradually changed from insignificant to significant.
TL;DR: In this paper , a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers, is presented, which builds multiple views focusing on different spatial or functional contexts to dissect different effects.
Abstract: The advancement of highly multiplexed spatial technologies requires scalable methods that can leverage spatial information. We present MISTy, a flexible, scalable, and explainable machine learning framework for extracting relationships from any spatial omics data, from dozens to thousands of measured markers. MISTy builds multiple views focusing on different spatial or functional contexts to dissect different effects. We evaluated MISTy on in silico and breast cancer datasets measured by imaging mass cytometry and spatial transcriptomics. We estimated structural and functional interactions coming from different spatial contexts in breast cancer and demonstrated how to relate MISTy's results to clinical features.
TL;DR: The technological progress of spatial omics and how advanced computation methods boost multi‐modal spatial data analysis are reviewed and a transfer of spatial ecological principles to cancer biology in spatial data interpretation is proposed.
Abstract: Abstract The idea that tumour microenvironment (TME) is organised in a spatial manner will not surprise many cancer biologists; however, systematically capturing spatial architecture of TME is still not possible until recent decade. The past five years have witnessed a boom in the research of high‐throughput spatial techniques and algorithms to delineate TME at an unprecedented level. Here, we review the technological progress of spatial omics and how advanced computation methods boost multi‐modal spatial data analysis. Then, we discussed the potential clinical translations of spatial omics research in precision oncology, and proposed a transfer of spatial ecological principles to cancer biology in spatial data interpretation. So far, spatial omics is placing us in the golden age of spatial cancer research. Further development and application of spatial omics may lead to a comprehensive decoding of the TME ecosystem and bring the current spatiotemporal molecular medical research into an entirely new paradigm.
TL;DR: In this paper , the authors synthesize and review the key problems in analysis of spatial transcriptomics data and methods that are currently applied, while also expanding on open questions and areas of future development.
Abstract: Abstract The rapid development of spatial transcriptomics (ST) techniques has allowed the measurement of transcriptional levels across many genes together with the spatial positions of cells. This has led to an explosion of interest in computational methods and techniques for harnessing both spatial and transcriptional information in analysis of ST datasets. The wide diversity of approaches in aim, methodology and technology for ST provides great challenges in dissecting cellular functions in spatial contexts. Here, we synthesize and review the key problems in analysis of ST data and methods that are currently applied, while also expanding on open questions and areas of future development.
TL;DR: In this article , a spatial-aware dimension reduction method, SpatialPCA, is proposed to extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserve spatial correlation structure.
Abstract: Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex.
TL;DR: In this paper , Wu et al. investigated the spatiotemporal pattern and drivers of industrial green productivity (IGP) by means of the Dagum Gini coefficient, spatial autocorrelation model, Markov transition probability matrix, and spatial econometric model.
TL;DR: In this article , a method, called CellDART, is proposed to decompose the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, which is translated to decomposition the cell types in each spatial barcoded region.
Abstract: Deciphering the cellular composition in genome-wide spatially resolved transcriptomic data is a critical task to clarify the spatial context of cells in a tissue. In this study, we developed a method, CellDART, which estimates the spatial distribution of cells defined by single-cell level data using domain adaptation of neural networks and applied it to the spatial mapping of human lung tissue. The neural network that predicts the cell proportion in a pseudospot, a virtual mixture of cells from single-cell data, is translated to decompose the cell types in each spatial barcoded region. First, CellDART was applied to a mouse brain and a human dorsolateral prefrontal cortex tissue to identify cell types with a layer-specific spatial distribution. Overall, the proposed approach showed more stable and higher accuracy with short execution time compared to other computational methods to predict the spatial location of excitatory neurons. CellDART was capable of decomposing cellular proportion in mouse hippocampus Slide-seq data. Furthermore, CellDART elucidated the cell type predominance defined by the human lung cell atlas across the lung tissue compartments and it corresponded to the known prevalent cell types. CellDART is expected to help to elucidate the spatial heterogeneity of cells and their close interactions in various tissues.
TL;DR: In this article, Wu et al. investigated the spatiotemporal pattern and drivers of industrial green productivity (IGP) by means of the Dagum Gini coefficient, spatial autocorrelation model, Markov transition probability matrix, and spatial econometric model.
TL;DR: Zhang et al. as mentioned in this paper analyzed the spatiotemporal evolution and influencing factors of urban green innovation in China by standard deviation ellipses, spatial autocorrelation, and Geodetector.
TL;DR: In this article , the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern was analyzed. And the results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid19 case numbers.
TL;DR: In this article , the spectral-spatial feature extraction of hyperspectral imagery (HSI) is analyzed, and two principles for spectral-space feature extraction are built, including the foundation of pixel-level HSI classification and the definition of spatial information.
Abstract: In this article, the intrinsic properties of hyperspectral imagery (HSI) are analyzed, and two principles for spectral-spatial feature extraction of HSI are built, including the foundation of pixel-level HSI classification and the definition of spatial information. Based on the two principles, scaled dot-product central attention (SDPCA) tailored for HSI is designed to extract spectral-spatial information from a central pixel (i.e., a query pixel to be classified) and pixels that are similar to the central pixel on an HSI patch. Then, employed with the HSI-tailored SDPCA module, a central attention network (CAN) is proposed by combining HSI-tailored dense connections of the features of the hidden layers and the spectral information of the query pixel. MiniCAN as a simplified version of CAN is also investigated. Superior classification performance of CAN and miniCAN on three datasets of different scenarios demonstrates their effectiveness and benefits compared with state-of-the-art methods.
TL;DR: In this article, the spatial association of the Covid-19 outbreak in Turkey between February 8 and May 28, 2021 and reveals its spatiotemporal pattern was analyzed and the results of the spatial regression model showed that population density and elderly dependency ratio are very important in explaining the model of Covid19 case numbers.
01 Aug 2022
TL;DR: In this article , a cascaded monitoring network (MoniNet) method is proposed to develop the monitoring model with concurrent analytics of temporal and spatial information, which can effectively detect process anomalies.
Abstract: Modern industrial plants generally consist of multiple manufacturing units, and the local correlation within each unit can be used to effectively alleviate the effect of spurious correlation and meticulously reflect the operation status of the process system. Therefore, the local correlation, which is called spatial information here, should also be taken into consideration when developing the monitoring model. In this study, a cascaded monitoring network (MoniNet) method is proposed to develop the monitoring model with concurrent analytics of temporal and spatial information. By implementing convolutional operation to each variable, the temporal information that reveals dynamic correlation of process data and spatial information that reflects local characteristics within individual operation unit can be extracted simultaneously. For each convolutional feature, a submodel is developed and then all the submodels are integrated to generate a final monitoring model. Based on the developed model, the operation status of the newly collected sample can be identified by comparing the calculated statistics with their corresponding control limits. Similar to the convolutional neural network (CNN), the MoniNet can also expand its receptive field and capture deeper information by adding more convolutional layers. Besides, the filter selection and submodel development in MoniNet can be replaced to generalize the proposed network to many existing monitoring strategies. The performance of the proposed method is validated using two real industrial processes. The illustration results show that the proposed method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.
TL;DR: Wang et al. as discussed by the authors employed spatial lag model (SLM) and multiscale geographically weighted regression (MGWR) model to reveal the global and local varying impacts of different factors on urban vibrancy.
TL;DR: In this article , a collective optimal transport method is developed to handle complex molecular interactions and spatial constraints in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells.
Abstract: Abstract Spatial transcriptomic technologies and spatially annotated single-cell RNA sequencing datasets provide unprecedented opportunities to dissect cell–cell communication (CCC). However, incorporation of the spatial information and complex biochemical processes required in the reconstruction of CCC remains a major challenge. Here, we present COMMOT (COMMunication analysis by Optimal Transport) to infer CCC in spatial transcriptomics, which accounts for the competition between different ligand and receptor species as well as spatial distances between cells. A collective optimal transport method is developed to handle complex molecular interactions and spatial constraints. Furthermore, we introduce downstream analysis tools to infer spatial signaling directionality and genes regulated by signaling using machine learning models. We apply COMMOT to simulation data and eight spatial datasets acquired with five different technologies to show its effectiveness and robustness in identifying spatial CCC in data with varying spatial resolutions and gene coverages. Finally, COMMOT identifies new CCCs during skin morphogenesis in a case study of human epidermal development.
TL;DR: In this paper , the authors analyzed the spatial distribution and spread of COVID-19 in 143 cities in China and found that the risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial auto-correlation differs significantly between the two.
Abstract: The study of the spatial differentiation of COVID-19 in cities and its driving mechanism is helpful to reveal the spatial distribution pattern, transmission mechanism and diffusion model, and evolution mechanism of the epidemic and can lay the foundation for constructing the spatial dynamics model of the epidemic and provide theoretical basis for the policy design, spatial planning and implementation of epidemic prevention and control and social governance. Geodetector (Origin version, Beijing, China) is a great tool for analysis of spatial differentiation and its influencing factors, and it provides decision support for differentiated policy design and its implementation in executing the city-specific policies. Using factor detection and interaction analysis of Geodetector, 15 indicators of economic, social, ecological, and environmental dimensions were integrated, and 143 cities were selected for the empirical research in China. The research shows that, first of all, risks of both infection and death show positive spatial autocorrelation, but the geographical distribution of local spatial autocorrelation differs significantly between the two. Secondly, the inequalities in urban economic, social, and residential environments interact with COVID-19 spatial heterogeneity, with stronger explanatory power especially when multidimensional inequalities are superimposed. Thirdly, the spatial distribution and spread of COVID-19 are highly spatially heterogeneous and correlated due to the complex influence of multiple factors, with factors such as Area of Urban Construction Land, GDP, Industrial Smoke and Dust Emission, and Expenditure having the strongest influence, the factors such as Area of Green, Number of Hospital Beds and Parks, and Industrial NOx Emissions having unignorable influence, while the factors such as Number of Free Parks and Industrial Enterprises, Per-GDP, and Population Density play an indirect role mainly by means of interaction. Fourthly, the factor interaction effect from the infected person’s perspective mainly shows a nonlinear enhancement effect, that is, the joint influence of the two factors is greater than the sum of their direct influences; but from the perspective of the dead, it mainly shows a two-factor enhancement effect, that is, the joint influence of the two factors is greater than the maximum of their direct influences but less than their sum. Fifthly, some suggestions are put forward from the perspectives of building a healthy, resilient, safe, and smart city, providing valuable reference and decision basis for city governments to carry out differentiated policy design.
TL;DR: For better energy assessment and management in the buildings industry, a spatial regression method is presented to quantitively measure the impacts of green certification programs on energy consumption and Greenhouse Gas (GHG) emissions in buildings as discussed by the authors .