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Current clustering analysis of spatial transcriptomics data primarily relies on molecular information and fails to fully exploit the morphological features present in histology images, leading to compromised accuracy and interpretability. To overcome these limitations, we have developed a multi-stage statistical method called iIMPACT. It identifies and defines histology-based spatial domains based on AI-reconstructed histology images and spatial context of gene expression measurements, and detects domain-specific differentially expressed genes. Through multiple case studies, we demonstrate iIMPACT outperforms existing methods in accuracy and interpretability and provides insights into the cellular spatial organization and landscape of functional genes within spatial transcriptomics data.

Spatially resolved transcriptomics (SRT) for characterizing spatial cellular heterogeneities in tissue environments requires systematic analytical approaches to elucidate gene expression variations within their physiological context. Here, we introduce SpaSEG, an unsupervised deep learning model utilizing convolutional neural networks for multiple SRT analysis tasks. Extensive evaluations across diverse SRT datasets generated by various platforms demonstrate SpaSEG’s superior robustness and efficiency compared to existing methods. In the application analysis of invasive ductal carcinoma, SpaSEG successfully unravels intratumoral heterogeneity and delivers insights into immunoregulatory mechanisms. These results highlight SpaSEG’s substantial potential for exploring tissue architectures and pathological biology.

Testing the significance of predictors in a regression model is one of the most important topics in statistics. This problem is especially difficult without any parametric assumptions on the data. This paper aims to test the null hypothesis that given confounding variables Z, X does not significantly contribute to the prediction of Y under the model-free setting, where X and Z are possibly high dimensional. We propose a general framework that first fits nonparametric machine learning regression algorithms on Y|Z and Y|(X, Z), then compares the prediction power of the two models. The proposed method allows us to leverage the strength of the most powerful regression algorithms developed in the modern machine learning community. The P value for the test can be easily obtained by permutation. In simulations, we find that the proposed method is more powerful compared to existing methods. The proposed method allows us to draw biologically meaningful conclusions from two gene expression data analyses without strong distributional assumptions: 1) testing the prediction power of sequencing RNA for the proteins in cellular indexing of transcriptomes and epitopes by sequencing data and 2) identification of spatially variable genes in spatially resolved transcriptomics data.

Background Spatially resolved transcriptomics offers unprecedented insight by enabling the profiling of gene expression within the intact spatial context of cells, effectively adding a new and essential dimension to data interpretation. To efficiently detect spatial structure of interest, an essential step in analyzing such data involves identifying spatially variable genes (SVGs). Despite researchers having developed several computational methods to accomplish this task, the lack of a comprehensive benchmark evaluating their performance remains a considerable gap in the field. Results Here, we systematically evaluate 14 methods using 96 spatial datasets and 6 metrics. We compare the methods regarding gene ranking and classification based on real spatial variation, statistical calibration, and computation scalability and investigate the impact of identified SVGs on downstream applications such as spatial domain detection. Finally, we explore the applicability of the methods to spatial ATAC-seq data to examine their effectiveness in identifying spatially variable peaks (SVPs). Overall, SPARK-X outperforms other benchmarked methods and Moran’s I achieves a competitive performance, representing a strong baseline for future method development. Moreover, our results reveal that most methods are poorly calibrated, and more specialized algorithms are needed to identify spatially variable peaks. Conclusions Our benchmarking provides a detailed comparison of SVG detection methods and serves as a reference for both users and method developers.

Background Understanding cellular heterogeneity within tissues hinges on knowledge of their spatial context. However, it is still challenging to accurately map cells to their spatial coordinates. Results We present SC2Spa, a deep learning-based approach that learns intricate spatial relationships from spatial transcriptomics (ST) data. Benchmarking tests show that SC2Spa outperformed other predictors and accurately detected tissue architecture from transcriptome. SC2Spa successfully mapped single cell RNA sequencing (scRNA-seq) to Visium assay, providing an approach to enhance the resolution for low resolution ST data. Our test showed that SC2Spa performs well for various ST technologies and robust to spatial resolution. In addition, SC2Spa can suggest spatially variable genes that cannot be identified from previous approaches. Conclusions SC2Spa is a robust and accurate approach to provide single cells with their spatial location and identify spatially meaningful genes.

Spatial transcriptomics combines gene expression data with spatial coordinates to allow for the discovery of detailed RNA localization, study development, investigating the tumor microenvironment, and creating a tissue atlas. A large range of spatial transcriptomics software is available, with little information on which may be better suited for particular datasets or computing environments. A review was conducted to detail the useful metrics when choosing appropriate software for spatial transcriptomics analysis. Specifically, the results from benchmarking studies that compared software across four key areas of spatial transcriptomics analysis (tissue architecture identification, spatially variable gene discovery, cell–cell communication analysis, and deconvolution) were assimilated into a single review that can serve as guidance when choosing potential spatial transcriptomics analysis software.

Recent advancements in spatial transcriptomics (ST) technologies allow researchers to simultaneously measure RNA expression levels for hundreds to thousands of genes while preserving spatial information within tissues, providing critical insights into spatial gene expression patterns, tissue organization, and gene functionality. However, existing methods for clustering spatially variable genes (SVGs) into co-expression modules often fail to detect rare or unique spatial expression patterns. To address this, we present spatial transcriptomics iterative hierarchical clustering (stIHC), a novel method for clustering SVGs into co-expression modules, representing groups of genes with shared spatial expression patterns. Through three simulations and applications to ST datasets from technologies such as 10x Visium, 10x Xenium, and Spatial Transcriptomics, stIHC outperforms clustering approaches used by popular SVG detection methods, including SPARK, SPARK-X, MERINGUE, and SpatialDE. Gene ontology enrichment analysis confirms that genes within each module share consistent biological functions, supporting the functional relevance of spatial co-expression. Robust across technologies with varying gene numbers and spatial resolution, stIHC provides a powerful tool for decoding the spatial organization of gene expression and the functional structure of complex tissues.

Recent advances in spatial transcriptomics have revolutionized the understanding of tissue organization. The identification of spatially variable genes (SVGs) is an essential step for downstream spatial domain characterization. Although several methods have been proposed for identifying SVGs, inadequate ability to decipher spatial domains, poor efficiency, and insufficient interoperability with existing standard analysis workflows still impede the applications of these methods. Here we propose SINFONIA, a scalable method for identifying spatially variable genes via ensemble strategies. Implemented in Python, SINFONIA can be seamlessly integrated into existing analysis workflows. Using 15 spatial transcriptomic datasets generated with different protocols and with different sizes, dimensions and qualities, we show the advantage of SINFONIA over three baseline methods and two variants via systematic evaluation of spatial clustering, domain resolution, latent representation, spatial visualization, and computational efficiency with 21 quantitative metrics. Additionally, SINFONIA is robust relative to the choice of the number of SVGs. We anticipate SINFONIA will facilitate the analysis of spatial transcriptomics.

Spatial epigenomic technologies enable simultaneous capture of spatial location and chromatin accessibility of cells within tissue slices. Identifying peaks that display spatial variation and cellular heterogeneity is the key analytic task for characterizing the spatial chromatin accessibility landscape of complex tissues. Here, we propose an efficient and iterative model, Descart, for spatially variable peaks identification based on the graph of inter-cellular correlations. Through the comprehensive benchmarking, we demonstrate the superiority of Descart in revealing cellular heterogeneity and capturing tissue structure. Utilizing the graph of inter-cellular correlations, Descart shows its potential to denoise data, identify peak modules, and detect gene-peak interactions.