More Like this

1. SEraster: a rasterization preprocessing framework for scalable spatial omics data analysis

   https://doi.org/10.1093/bioinformatics/btae412  

   Aihara, Gohta; Clifton, Kalen; Chen, Mayling; Li, Zhuoyan; Atta, Lyla; Miller, Brendan F; Satija, Rahul; Hickey, John W; Fan, Jean (July 2024, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Abstract MotivationSpatial omics data demand computational analysis but many analysis tools have computational resource requirements that increase with the number of cells analyzed. This presents scalability challenges as researchers use spatial omics technologies to profile millions of cells. ResultsTo enhance the scalability of spatial omics data analysis, we developed a rasterization preprocessing framework called SEraster that aggregates cellular information into spatial pixels. We apply SEraster to both real and simulated spatial omics data prior to spatial variable gene expression analysis to demonstrate that such preprocessing can reduce computational resource requirements while maintaining high performance, including as compared to other down-sampling approaches. We further integrate SEraster with existing analysis tools to characterize cell-type spatial co-enrichment across length scales. Finally, we apply SEraster to enable analysis of a mouse pup spatial omics dataset with over a million cells to identify tissue-level and cell-type-specific spatially variable genes as well as spatially co-enriched cell types that recapitulate expected organ structures. Availability and implementationSEraster is implemented as an R package on GitHub (https://github.com/JEFworks-Lab/SEraster) with additional tutorials at https://JEF.works/SEraster. 

   more » « less
2. Systematic benchmarking of computational methods to identify spatially variable genes

   https://doi.org/10.1186/s13059-025-03731-2  

   Li, Zhijian; MPatel, Zain; Song, Dongyuan; Yasa, Sai Nirmayi; Cannoodt, Robrecht; Yan, Guanao; Li, Jingyi Jessica; Pinello, Luca (December 2025, Genome Biology)

   Abstract BackgroundSpatially 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. ResultsHere, 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. ConclusionsOur benchmarking provides a detailed comparison of SVG detection methods and serves as a reference for both users and method developers. 

   more » « less
3. BISON: bi-clustering of spatial omics data with feature selection

   https://doi.org/10.1093/bioinformatics/btaf495  

   Zhu, Bencong; Cassese, Alberto; Vannucci, Marina; Guindani, Michele; Li, Qiwei (September 2025, Bioinformatics)

   Kendziorski, Christina (Ed.)

   Abstract MotivationThe advent of next-generation sequencing-based spatially resolved transcriptomics (SRT) techniques has reshaped genomic studies by enabling high-throughput gene expression profiling while preserving spatial and morphological context. Understanding gene functions and interactions in different spatial domains is crucial, as it can enhance our comprehension of biological mechanisms, such as cancer-immune interactions and cell differentiation in various regions. It is necessary to cluster tissue regions into distinct spatial domains and identify discriminating genes (DGs) that elucidate the clustering result, referred to as spatial domain-specific DGs. Existing methods for identifying these genes typically rely on a two-stage approach, which can lead to the phenomenon known as double-dipping. ResultsTo address the challenge, we propose a unified Bayesian latent block model that simultaneously detects a list of DGs contributing to spatial domain identification while clustering these DGs and spatial locations. The efficacy of our proposed method is validated through a series of simulation experiments, and its capability to identify DGs is demonstrated through applications to benchmark SRT datasets. Availability and implementationThe R/C++ implementation of BISON is available at https://github.com/new-zbc/BISON. 

   more » « less
4. SPASCER: spatial transcriptomics annotation at single-cell resolution

   https://doi.org/10.1093/nar/gkac889  

   Fan, Zhiwei; Luo, Yangyang; Lu, Huifen; Wang, Tiangang; Feng, YuZhou; Zhao, Weiling; Kim, Pora; Zhou, Xiaobo (October 2022, Nucleic Acids Research)

   Abstract In recent years, the explosive growth of spatial technologies has enabled the characterization of spatial heterogeneity of tissue architectures. Compared to traditional sequencing, spatial transcriptomics reserves the spatial information of each captured location and provides novel insights into diverse spatially related biological contexts. Even though two spatial transcriptomics databases exist, they provide limited analytical information. Information such as spatial heterogeneity of genes and cells, cell-cell communication activities in space, and the cell type compositions in the microenvironment are critical clues to unveil the mechanism of tumorigenesis and embryo differentiation. Therefore, we constructed a new spatial transcriptomics database, named SPASCER (https://ccsm.uth.edu/SPASCER), designed to help understand the heterogeneity of tissue organizations, region-specific microenvironment, and intercellular interactions across tissue architectures at multiple levels. SPASCER contains datasets from 43 studies, including 1082 sub-datasets from 16 organ types across four species. scRNA-seq was integrated to deconvolve/map spatial transcriptomics, and processed with spatial cell-cell interaction, gene pattern and pathway enrichment analysis. Cell–cell interactions and gene regulation network of scRNA-seq from matched spatial transcriptomics were performed as well. The application of SPASCER will provide new insights into tissue architecture and a solid foundation for the mechanistic understanding of many biological processes in healthy and diseased tissues. 

   more » « less
5. BioModME for building and simulating dynamic computational models of complex biological systems

   https://doi.org/10.1093/bioadv/vbae023  

   Womack, Justin A; Shah, Viren; Audi, Said H; Terhune, Scott S; Dash, Ranjan K (January 2024, Bioinformatics Advances)

   Lengauer, Thomas (Ed.)

   Abstract SummaryMolecular mechanisms of biological functions and disease processes are exceptionally complex, and our ability to interrogate and understand relationships is becoming increasingly dependent on the use of computational modeling. We have developed “BioModME,” a standalone R-based web application package, providing an intuitive and comprehensive graphical user interface to help investigators build, solve, visualize, and analyze computational models of complex biological systems. Some important features of the application package include multi-region system modeling, custom reaction rate laws and equations, unit conversion, model parameter estimation utilizing experimental data, and import and export of model information in the Systems Biology Matkup Language format. The users can also export models to MATLAB, R, and Python languages and the equations to LaTeX and Mathematical Markup Language formats. Other important features include an online model development platform, multi-modality visualization tool, and efficient numerical solvers for differential-algebraic equations and optimization. Availability and implementationAll relevant software information including documentation and tutorials can be found at https://mcw.marquette.edu/biomedical-engineering/computational-systems-biology-lab/biomodme.php. Deployed software can be accessed at https://biomodme.ctsi.mcw.edu/. Source code is freely available for download at https://github.com/MCWComputationalBiologyLab/BioModME. 

   more » « less