Browsing by Author "Raina, Mauminah"

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    Relation equivariant graph neural networks to explore the mosaic-like tissue architecture of kidney diseases on spatially resolved transcriptomics
    (Oxford University Press, 2025) Raina, Mauminah; Cheng, Hao; Ferreira, Ricardo Melo; Stansfield, Treyden; Modak, Chandrima; Cheng, Ying-Hua; Suryadevara, Hari Naga Sai Kiran; Xu, Dong; Eadon, Michael T.; Ma, Qin; Wang, Juexin; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Motivation: Chronic kidney disease (CKD) and acute kidney injury (AKI) are prominent public health concerns affecting more than 15% of the global population. The ongoing development of spatially resolved transcriptomics (SRT) technologies presents a promising approach for discovering the spatial distribution patterns of gene expression within diseased tissues. However, existing computational tools are predominantly calibrated and designed on the ribbon-like structure of the brain cortex, presenting considerable computational obstacles in discerning highly heterogeneous mosaic-like tissue architectures in the kidney. Consequently, timely and cost-effective acquisition of annotation and interpretation in the kidney remains a challenge in exploring the cellular and morphological changes within renal tubules and their interstitial niches. Results: We present an empowered graph deep learning framework, REGNN (Relation Equivariant Graph Neural Networks), designed for SRT data analyses on heterogeneous tissue structures. To increase expressive power in the SRT lattice using graph modeling, REGNN integrates equivariance to handle n-dimensional symmetries of the spatial area, while additionally leveraging Positional Encoding to strengthen relative spatial relations of the nodes uniformly distributed in the lattice. Given the limited availability of well-labeled spatial data, this framework implements both graph autoencoder and graph self-supervised learning strategies. On heterogeneous samples from different kidney conditions, REGNN outperforms existing computational tools in identifying tissue architectures within the 10× Visium platform. This framework offers a powerful graph deep learning tool for investigating tissues within highly heterogeneous expression patterns and paves the way to pinpoint underlying pathological mechanisms that contribute to the progression of complex diseases. Availability and implementation: REGNN is publicly available at https://github.com/Mraina99/REGNN.
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    scBSP: A fast and accurate tool for identifying spatially variable features from high-resolution spatial omics data
    (bioRxiv, 2025-02-07) Li, Jinpu; Raina, Mauminah; Wang, Yiqing; Xu, Chunhui; Su, Li; Guo, Qi; Ferreira, Ricardo Melo; Eadon, Michael T.; Ma, Qin; Wang, Juexin; Xu, Dong; Biomedical Engineering and Informatics, Luddy School of Informatics, Computing, and Engineering
    Emerging spatial omics technologies empower comprehensive exploration of biological systems from multi-omics perspectives in their native tissue location in two and three-dimensional space. However, sparse sequencing capacity and growing spatial resolution in spatial omics present significant computational challenges in identifying biologically meaningful molecules that exhibit variable spatial distributions across different omics. We introduce scBSP, an open-source, versatile, and user-friendly package for identifying spatially variable features in high-resolution spatial omics data. scBSP leverages sparse matrix operation to significantly increase computational efficiency in both computational time and memory usage. In diverse spatial sequencing data and simulations, scBSP consistently and rapidly identifies spatially variable genes and spatially variable peaks across various sequencing techniques and spatial resolutions, handling two- and three-dimensional data with up to millions of cells. It can process high-definition spatial transcriptomics data for 19,950 genes across 181,367 spots within 10 seconds on a typical desktop computer, making it the fastest tool available for handling such high-resolution, sparse spatial omics data while maintaining high accuracy. In a case study of kidney disease using 10x Xenium data, scBSP identified spatially variable genes representative of critical pathological mechanisms associated with histology.