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Das Adhikari S, Yang J, Wang J, Cui Y. Recent advances in spatially variable gene detection in spatial transcriptomics. Comput Struct Biotechnol J 2024; 23:883-891. [PMID: 38370977 PMCID: PMC10869304 DOI: 10.1016/j.csbj.2024.01.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2023] [Revised: 01/22/2024] [Accepted: 01/22/2024] [Indexed: 02/20/2024] Open
Abstract
With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.
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Affiliation(s)
- Sikta Das Adhikari
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
| | - Jiaxin Yang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
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2
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Yuan X, Ma Y, Gao R, Cui S, Wang Y, Fa B, Ma S, Wei T, Ma S, Yu Z. HEARTSVG: a fast and accurate method for identifying spatially variable genes in large-scale spatial transcriptomics. Nat Commun 2024; 15:5700. [PMID: 38972896 DOI: 10.1038/s41467-024-49846-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 06/19/2024] [Indexed: 07/09/2024] Open
Abstract
Identifying spatially variable genes (SVGs) is crucial for understanding the spatiotemporal characteristics of diseases and tissue structures, posing a distinctive challenge in spatial transcriptomics research. We propose HEARTSVG, a distribution-free, test-based method for fast and accurately identifying spatially variable genes in large-scale spatial transcriptomic data. Extensive simulations demonstrate that HEARTSVG outperforms state-of-the-art methods with higherF 1 scores (averageF 1 Score=0.948), improved computational efficiency, scalability, and reduced false positives (FPs). Through analysis of twelve real datasets from various spatial transcriptomic technologies, HEARTSVG identifies a greater number of biologically significant SVGs (average AUC = 0.792) than other comparative methods without prespecifying spatial patterns. Furthermore, by clustering SVGs, we uncover two distinct tumor spatial domains characterized by unique spatial expression patterns, spatial-temporal locations, and biological functions in human colorectal cancer data, unraveling the complexity of tumors.
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Affiliation(s)
- Xin Yuan
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China
| | - Yanran Ma
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Ruitian Gao
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Shuya Cui
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China
| | - Yifan Wang
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Botao Fa
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Xi'an Jiaotong University, Xi'an, Shanxi, China
| | - Shiyang Ma
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ting Wei
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Shuangge Ma
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.
- Department of Biostatistics, Yale University, New Haven, USA.
| | - Zhangsheng Yu
- Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
- SJTU-Yale Joint Center for Biostatistics and Data Science Organization, Shanghai Jiao Tong University, Shanghai, China.
- Clinical Research Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
- Center for Biomedical Data Science, Translational Science Institute, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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3
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Ali M, Kuijs M, Hediyeh-Zadeh S, Treis T, Hrovatin K, Palla G, Schaar AC, Theis FJ. GraphCompass: spatial metrics for differential analyses of cell organization across conditions. Bioinformatics 2024; 40:i548-i557. [PMID: 38940138 DOI: 10.1093/bioinformatics/btae242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/29/2024] Open
Abstract
SUMMARY Spatial omics technologies are increasingly leveraged to characterize how disease disrupts tissue organization and cellular niches. While multiple methods to analyze spatial variation within a sample have been published, statistical and computational approaches to compare cell spatial organization across samples or conditions are mostly lacking. We present GraphCompass, a comprehensive set of omics-adapted graph analysis methods to quantitatively evaluate and compare the spatial arrangement of cells in samples representing diverse biological conditions. GraphCompass builds upon the Squidpy spatial omics toolbox and encompasses various statistical approaches to perform cross-condition analyses at the level of individual cell types, niches, and samples. Additionally, GraphCompass provides custom visualization functions that enable effective communication of results. We demonstrate how GraphCompass can be used to address key biological questions, such as how cellular organization and tissue architecture differ across various disease states and which spatial patterns correlate with a given pathological condition. GraphCompass can be applied to various popular omics techniques, including, but not limited to, spatial proteomics (e.g. MIBI-TOF), spot-based transcriptomics (e.g. 10× Genomics Visium), and single-cell resolved transcriptomics (e.g. Stereo-seq). In this work, we showcase the capabilities of GraphCompass through its application to three different studies that may also serve as benchmark datasets for further method development. With its easy-to-use implementation, extensive documentation, and comprehensive tutorials, GraphCompass is accessible to biologists with varying levels of computational expertise. By facilitating comparative analyses of cell spatial organization, GraphCompass promises to be a valuable asset in advancing our understanding of tissue function in health and disease. .
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Affiliation(s)
- Mayar Ali
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- Institute for Tissue Engineering and Regenerative Medicine, Helmholtz Munich, Neuherberg, 85764, Germany
- Graduate School of Systemic Neurosciences, Ludwig Maximilian University of Munich, Planegg-Martinsried, 82152, Germany
| | - Merel Kuijs
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, 80333, Germany
| | - Soroor Hediyeh-Zadeh
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany
| | - Tim Treis
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany
| | - Karin Hrovatin
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany
| | - Giovanni Palla
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany
| | - Anna C Schaar
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, 80333, Germany
- Munich Center for Machine Learning, Technical University of Munich, Munich, 80333, Germany
| | - Fabian J Theis
- Institute of Computational Biology, Helmholtz Munich, Neuherberg, 85764, Germany
- Department of Mathematics, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, 80333, Germany
- TUM School of Life Sciences, Technical University of Munich, Freising, 85354, Germany
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4
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Yan G, Hua SH, Li JJ. Categorization of 31 computational methods to detect spatially variable genes from spatially resolved transcriptomics data. ARXIV 2024:arXiv:2405.18779v1. [PMID: 38855546 PMCID: PMC11160866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
In the analysis of spatially resolved transcriptomics data, detecting spatially variable genes (SVGs) is crucial. Numerous computational methods exist, but varying SVG definitions and methodologies lead to incomparable results. We review 31 state-of-the-art methods, categorizing SVGs into three types: overall, cell-type-specific, and spatial-domain-marker SVGs. Our review explains the intuitions underlying these methods, summarizes their applications, and categorizes the hypothesis tests they use in the trade-off between generality and specificity for SVG detection. We discuss challenges in SVG detection and propose future directions for improvement. Our review offers insights for method developers and users, advocating for category-specific benchmarking.
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Affiliation(s)
- Guanao Yan
- Department of Statistics, University of California, Los Angeles, CA 90095-1554
| | - Shuo Harper Hua
- Department of Biomedical Data Science, Stanford University, Stanford, CA 94305
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA 90095-1554
- Department of Human Genetics, University of California, Los Angeles, CA 90095-7088
- Department of Computational Medicine, University of California, Los Angeles, CA 90095-1766
- Department of Biostatistics, University of California, Los Angeles, CA 90095-1772
- Radcliffe Institute for Advanced Study, Harvard University, Cambridge, MA 02138
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Yu S, Li WV. spVC for the detection and interpretation of spatial gene expression variation. Genome Biol 2024; 25:103. [PMID: 38641849 PMCID: PMC11027374 DOI: 10.1186/s13059-024-03245-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Accepted: 04/10/2024] [Indexed: 04/21/2024] Open
Abstract
Spatially resolved transcriptomics technologies have opened new avenues for understanding gene expression heterogeneity in spatial contexts. However, existing methods for identifying spatially variable genes often focus solely on statistical significance, limiting their ability to capture continuous expression patterns and integrate spot-level covariates. To address these challenges, we introduce spVC, a statistical method based on a generalized Poisson model. spVC seamlessly integrates constant and spatially varying effects of covariates, facilitating comprehensive exploration of gene expression variability and enhancing interpretability. Simulation and real data applications confirm spVC's accuracy in these tasks, highlighting its versatility in spatial transcriptomics analysis.
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Affiliation(s)
- Shan Yu
- Department of Statistics, Unversity of Virginia, Charlottesville, 22903, VA, USA.
| | - Wei Vivian Li
- Department of Statistics, University of California, Riverside, 92521, CA, USA.
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Li R, Chen X, Yang X. Navigating the landscapes of spatial transcriptomics: How computational methods guide the way. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1839. [PMID: 38527900 DOI: 10.1002/wrna.1839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 02/24/2024] [Accepted: 03/04/2024] [Indexed: 03/27/2024]
Abstract
Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single-cell, multi-cellular, or sub-cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi-modal high-throughput data source, which poses new challenges for the development of analytical methods for data-mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever-evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms. This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization.
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Affiliation(s)
- Runze Li
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xu Chen
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
| | - Xuerui Yang
- MOE Key Laboratory of Bioinformatics, Center for Synthetic & Systems Biology, School of Life Sciences, Tsinghua University, Beijing, China
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7
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Zhang C, Gao J, Chen HY, Kong L, Cao G, Guo X, Liu W, Ren B, Wei DQ. STGIC: A graph and image convolution-based method for spatial transcriptomic clustering. PLoS Comput Biol 2024; 20:e1011935. [PMID: 38416785 PMCID: PMC10927115 DOI: 10.1371/journal.pcbi.1011935] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 03/11/2024] [Accepted: 02/20/2024] [Indexed: 03/01/2024] Open
Abstract
Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) is designed for techniques with regular lattices on chips. It utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by Kullback-Leibler (KL) divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of 10x Visium human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.
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Affiliation(s)
- Chen Zhang
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Junhui Gao
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Hong-Yu Chen
- College of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Lingxin Kong
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Guangshuo Cao
- State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang
| | - Xiangyu Guo
- Smart-Health Initiative, King Abdullah University of Science and Technology, Jeddah, Saudi Arabia
| | - Wei Liu
- Marine Science and Technology College, Zhejiang Ocean University, Zhoushan, China
| | - Bin Ren
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
| | - Dong-Qing Wei
- School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China
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8
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Zahedi R, Ghamsari R, Argha A, Macphillamy C, Beheshti A, Alizadehsani R, Lovell NH, Lotfollahi M, Alinejad-Rokny H. Deep learning in spatially resolved transcriptfomics: a comprehensive technical view. Brief Bioinform 2024; 25:bbae082. [PMID: 38483255 PMCID: PMC10939360 DOI: 10.1093/bib/bbae082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 12/22/2024] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Spatially resolved transcriptomics (SRT) is a pioneering method for simultaneously studying morphological contexts and gene expression at single-cell precision. Data emerging from SRT are multifaceted, presenting researchers with intricate gene expression matrices, precise spatial details and comprehensive histology visuals. Such rich and intricate datasets, unfortunately, render many conventional methods like traditional machine learning and statistical models ineffective. The unique challenges posed by the specialized nature of SRT data have led the scientific community to explore more sophisticated analytical avenues. Recent trends indicate an increasing reliance on deep learning algorithms, especially in areas such as spatial clustering, identification of spatially variable genes and data alignment tasks. In this manuscript, we provide a rigorous critique of these advanced deep learning methodologies, probing into their merits, limitations and avenues for further refinement. Our in-depth analysis underscores that while the recent innovations in deep learning tailored for SRT have been promising, there remains a substantial potential for enhancement. A crucial area that demands attention is the development of models that can incorporate intricate biological nuances, such as phylogeny-aware processing or in-depth analysis of minuscule histology image segments. Furthermore, addressing challenges like the elimination of batch effects, perfecting data normalization techniques and countering the overdispersion and zero inflation patterns seen in gene expression is pivotal. To support the broader scientific community in their SRT endeavors, we have meticulously assembled a comprehensive directory of readily accessible SRT databases, hoping to serve as a foundation for future research initiatives.
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Affiliation(s)
- Roxana Zahedi
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Reza Ghamsari
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
| | - Ahmadreza Argha
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Callum Macphillamy
- School of Animal and Veterinary Sciences, University of Adelaide, Roseworthy, 5371, Australia
| | - Amin Beheshti
- School of Computing, Macquarie University, Sydney, 2109, Australia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Waurn Ponds, Melbourne, VIC, 3216, Australia
| | - Nigel H Lovell
- The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
| | - Mohammad Lotfollahi
- Computational Health Center, Helmholtz Munich, Germany
- Wellcome Sanger Institute, Cambridge, UK
| | - Hamid Alinejad-Rokny
- UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia
- Tyree Institute of Health Engineering (IHealthE), UNSW Sydney, 2052, NSW, Australia
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Liang Y, Shi G, Cai R, Yuan Y, Xie Z, Yu L, Huang Y, Shi Q, Wang L, Li J, Tang Z. PROST: quantitative identification of spatially variable genes and domain detection in spatial transcriptomics. Nat Commun 2024; 15:600. [PMID: 38238417 PMCID: PMC10796707 DOI: 10.1038/s41467-024-44835-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 12/19/2023] [Indexed: 01/22/2024] Open
Abstract
Computational methods have been proposed to leverage spatially resolved transcriptomic data, pinpointing genes with spatial expression patterns and delineating tissue domains. However, existing approaches fall short in uniformly quantifying spatially variable genes (SVGs). Moreover, from a methodological viewpoint, while SVGs are naturally associated with depicting spatial domains, they are technically dissociated in most methods. Here, we present a framework (PROST) for the quantitative recognition of spatial transcriptomic patterns, consisting of (i) quantitatively characterizing spatial variations in gene expression patterns through the PROST Index; and (ii) unsupervised clustering of spatial domains via a self-attention mechanism. We demonstrate that PROST performs superior SVG identification and domain segmentation with various spatial resolutions, from multicellular to cellular levels. Importantly, PROST Index can be applied to prioritize spatial expression variations, facilitating the exploration of biological insights. Together, our study provides a flexible and robust framework for analyzing diverse spatial transcriptomic data.
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Affiliation(s)
- Yuchen Liang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Guowei Shi
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Runlin Cai
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Yuchen Yuan
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Ziying Xie
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Long Yu
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Yingjian Huang
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Qian Shi
- School of Geography and Planning, Sun Yat-sen University, Guangzhou, 510275, China
| | - Lizhe Wang
- School of Computer Science, China University of Geosciences, Wuhan, 430078, China
| | - Jun Li
- School of Computer Science, China University of Geosciences, Wuhan, 430078, China.
| | - Zhonghui Tang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
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10
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Li Z, Patel ZM, Song D, Yan G, Li JJ, Pinello L. Benchmarking computational methods to identify spatially variable genes and peaks. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.02.569717. [PMID: 38076922 PMCID: PMC10705556 DOI: 10.1101/2023.12.02.569717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/17/2023]
Abstract
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. 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. Here, we present a systematic evaluation of 14 methods using 60 simulated datasets generated by four different simulation strategies, 12 real-world transcriptomics, and three spatial ATAC-seq datasets. We find that spatialDE2 consistently outperforms the other benchmarked methods, and Moran's I achieves competitive performance in different experimental settings. Moreover, our results reveal that more specialized algorithms are needed to identify spatially variable peaks.
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Affiliation(s)
- Zhijian Li
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Zain M. Patel
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
| | - Dongyuan Song
- Interdepartmental Program of Bioinformatics, University of California, Los Angeles, CA, USA
| | - Guanao Yan
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Jingyi Jessica Li
- Department of Statistics and Data Science, University of California, Los Angeles, CA, USA
| | - Luca Pinello
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
- Department of Pathology, Harvard Medical School, Boston, MA, USA
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11
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Adhikari SD, Yang J, Wang J, Cui Y. A SELECTIVE REVIEW OF RECENT DEVELOPMENTS IN SPATIALLY VARIABLE GENE DETECTION FOR SPATIAL TRANSCRIPTOMICS. ARXIV 2023:arXiv:2311.13801v1. [PMID: 38045476 PMCID: PMC10690303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
With the emergence of advanced spatial transcriptomic technologies, there has been a surge in research papers dedicated to analyzing spatial transcriptomics data, resulting in significant contributions to our understanding of biology. The initial stage of downstream analysis of spatial transcriptomic data has centered on identifying spatially variable genes (SVGs) or genes expressed with specific spatial patterns across the tissue. SVG detection is an important task since many downstream analyses depend on these selected SVGs. Over the past few years, a plethora of new methods have been proposed for the detection of SVGs, accompanied by numerous innovative concepts and discussions. This article provides a selective review of methods and their practical implementations, offering valuable insights into the current literature in this field.
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Affiliation(s)
- Sikta Das Adhikari
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Jiaxin Yang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Jianrong Wang
- Department of Computational Mathematics, Science and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Yuehua Cui
- Department of Statistics and Probability, Michigan State University, East Lansing, MI 48824, USA
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12
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Seal S, Bitler BG, Ghosh D. SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data. PLoS Genet 2023; 19:e1010983. [PMID: 37862362 PMCID: PMC10619839 DOI: 10.1371/journal.pgen.1010983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 11/01/2023] [Accepted: 09/19/2023] [Indexed: 10/22/2023] Open
Abstract
In high-throughput spatial transcriptomics (ST) studies, it is of great interest to identify the genes whose level of expression in a tissue covaries with the spatial location of cells/spots. Such genes, also known as spatially variable genes (SVGs), can be crucial to the biological understanding of both structural and functional characteristics of complex tissues. Existing methods for detecting SVGs either suffer from huge computational demand or significantly lack statistical power. We propose a non-parametric method termed SMASH that achieves a balance between the above two problems. We compare SMASH with other existing methods in varying simulation scenarios demonstrating its superior statistical power and robustness. We apply the method to four ST datasets from different platforms uncovering interesting biological insights.
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Affiliation(s)
- Souvik Seal
- Department of Public Health Sciences, School of Medicine, Medical University of South Carolina, Charleston, South Carolina, United States of America
| | - Benjamin G. Bitler
- Department of Obstetrics and Gynecology, School of Medicine, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
| | - Debashis Ghosh
- Department of Biostatistics and Informatics, Colorado School of Public Health, University of Colorado Denver Anschutz Medical Campus, Aurora, Colorado, United States of America
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Jones DC, Danaher P, Kim Y, Beechem JM, Gottardo R, Newell EW. An information theoretic approach to detecting spatially varying genes. CELL REPORTS METHODS 2023; 3:100507. [PMID: 37426750 PMCID: PMC10326450 DOI: 10.1016/j.crmeth.2023.100507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 04/03/2023] [Accepted: 05/25/2023] [Indexed: 07/11/2023]
Abstract
A key step in spatial transcriptomics is identifying genes with spatially varying expression patterns. We adopt an information theoretic perspective to this problem by equating the degree of spatial coherence with the Jensen-Shannon divergence between pairs of nearby cells and pairs of distant cells. To avoid the notoriously difficult problem of estimating information theoretic divergences, we use modern approximation techniques to implement a computationally efficient algorithm designed to scale with in situ spatial transcriptomics technologies. In addition to being highly scalable, we show that our method, which we call maximization of spatial information (Maxspin), improves accuracy across several spatial transcriptomics platforms and a variety of simulations when compared with a variety of state-of-the-art methods. To further demonstrate the method, we generated in situ spatial transcriptomics data in a renal cell carcinoma sample using the CosMx Spatial Molecular Imager and used Maxspin to reveal novel spatial patterns of tumor cell gene expression.
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Affiliation(s)
| | | | - Youngmi Kim
- NanoString Technologies, Inc., Seattle, WA, USA
| | | | - Raphael Gottardo
- Fred Hutchinson Cancer Center, Seattle, WA, USA
- Biomedical Data Science Center, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
- Swiss Institute of Bioinformatics, Lausanne, Switzerland
- Ludwig Institute for Cancer Research, Lausanne Branch, Lausanne, Switzerland
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Meng-Lin K, Ung CY, Zhang C, Weiskittel TM, Wisniewski P, Zhang Z, Tan SH, Yeo KS, Zhu S, Correia C, Li H. SPIN-AI: A Deep Learning Model That Identifies Spatially Predictive Genes. Biomolecules 2023; 13:895. [PMID: 37371475 PMCID: PMC10296445 DOI: 10.3390/biom13060895] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 05/23/2023] [Accepted: 05/23/2023] [Indexed: 06/29/2023] Open
Abstract
Spatially resolved sequencing technologies help us dissect how cells are organized in space. Several available computational approaches focus on the identification of spatially variable genes (SVGs), genes whose expression patterns vary in space. The detection of SVGs is analogous to the identification of differentially expressed genes and permits us to understand how genes and associated molecular processes are spatially distributed within cellular niches. However, the expression activities of SVGs fail to encode all information inherent in the spatial distribution of cells. Here, we devised a deep learning model, Spatially Informed Artificial Intelligence (SPIN-AI), to identify spatially predictive genes (SPGs), whose expression can predict how cells are organized in space. We used SPIN-AI on spatial transcriptomic data from squamous cell carcinoma (SCC) as a proof of concept. Our results demonstrate that SPGs not only recapitulate the biology of SCC but also identify genes distinct from SVGs. Moreover, we found a substantial number of ribosomal genes that were SPGs but not SVGs. Since SPGs possess the capability to predict spatial cellular organization, we reason that SPGs capture more biologically relevant information for a given cellular niche than SVGs. Thus, SPIN-AI has broad applications for detecting SPGs and uncovering which biological processes play important roles in governing cellular organization.
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Affiliation(s)
- Kevin Meng-Lin
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Choong-Yong Ung
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Cheng Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Taylor M. Weiskittel
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Philip Wisniewski
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Zhuofei Zhang
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Shyang-Hong Tan
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Kok-Siong Yeo
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (K.-S.Y.); (S.Z.)
| | - Shizhen Zhu
- Department of Biochemistry and Molecular Biology, Mayo Clinic College of Medicine and Science, Rochester, MN 55905, USA; (K.-S.Y.); (S.Z.)
| | - Cristina Correia
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
| | - Hu Li
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN 55905, USA; (K.M.-L.); (C.-Y.U.); (C.Z.); (T.M.W.); (P.W.); (Z.Z.); (S.-H.T.)
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Lee RY, Ng CW, Rajapakse MP, Ang N, Yeong JPS, Lau MC. The promise and challenge of spatial omics in dissecting tumour microenvironment and the role of AI. Front Oncol 2023; 13:1172314. [PMID: 37197415 PMCID: PMC10183599 DOI: 10.3389/fonc.2023.1172314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/18/2023] [Indexed: 05/19/2023] Open
Abstract
Growing evidence supports the critical role of tumour microenvironment (TME) in tumour progression, metastases, and treatment response. However, the in-situ interplay among various TME components, particularly between immune and tumour cells, are largely unknown, hindering our understanding of how tumour progresses and responds to treatment. While mainstream single-cell omics techniques allow deep, single-cell phenotyping, they lack crucial spatial information for in-situ cell-cell interaction analysis. On the other hand, tissue-based approaches such as hematoxylin and eosin and chromogenic immunohistochemistry staining can preserve the spatial information of TME components but are limited by their low-content staining. High-content spatial profiling technologies, termed spatial omics, have greatly advanced in the past decades to overcome these limitations. These technologies continue to emerge to include more molecular features (RNAs and/or proteins) and to enhance spatial resolution, opening new opportunities for discovering novel biological knowledge, biomarkers, and therapeutic targets. These advancements also spur the need for novel computational methods to mine useful TME insights from the increasing data complexity confounded by high molecular features and spatial resolution. In this review, we present state-of-the-art spatial omics technologies, their applications, major strengths, and limitations as well as the role of artificial intelligence (AI) in TME studies.
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Affiliation(s)
- Ren Yuan Lee
- Singapore Thong Chai Medical Institution, Singapore, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Chan Way Ng
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | | | - Nicholas Ang
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Joe Poh Sheng Yeong
- Department of Anatomical Pathology, Singapore General Hospital, Singapore, Singapore
- Cancer Science Institute of Singapore, National University of Singapore, Singapore, Singapore
- *Correspondence: Joe Poh Sheng Yeong, ; Mai Chan Lau,
| | - Mai Chan Lau
- Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- Bioinformatics Institute (BII), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
- *Correspondence: Joe Poh Sheng Yeong, ; Mai Chan Lau,
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