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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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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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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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Chen C, Kim HJ, Yang P. Evaluating spatially variable gene detection methods for spatial transcriptomics data. Genome Biol 2024; 25:18. [PMID: 38225676 PMCID: PMC10789051 DOI: 10.1186/s13059-023-03145-y] [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: 12/08/2022] [Accepted: 12/14/2023] [Indexed: 01/17/2024] Open
Abstract
BACKGROUND The identification of genes that vary across spatial domains in tissues and cells is an essential step for spatial transcriptomics data analysis. Given the critical role it serves for downstream data interpretations, various methods for detecting spatially variable genes (SVGs) have been proposed. However, the lack of benchmarking complicates the selection of a suitable method. RESULTS Here we systematically evaluate a panel of popular SVG detection methods on a large collection of spatial transcriptomics datasets, covering various tissue types, biotechnologies, and spatial resolutions. We address questions including whether different methods select a similar set of SVGs, how reliable is the reported statistical significance from each method, how accurate and robust is each method in terms of SVG detection, and how well the selected SVGs perform in downstream applications such as clustering of spatial domains. Besides these, practical considerations such as computational time and memory usage are also crucial for deciding which method to use. CONCLUSIONS Our study evaluates the performance of each method from multiple aspects and highlights the discrepancy among different methods when calling statistically significant SVGs across diverse datasets. Overall, our work provides useful considerations for choosing methods for identifying SVGs and serves as a key reference for the future development of related methods.
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Affiliation(s)
- Carissa Chen
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
| | - Pengyi Yang
- Computational Systems Biology Group, Faculty of Medicine and Health, Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
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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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Li Z, Chen X, Zhang X, Jiang R, Chen S. Latent feature extraction with a prior-based self-attention framework for spatial transcriptomics. Genome Res 2023; 33:1757-1773. [PMID: 37903634 PMCID: PMC10691543 DOI: 10.1101/gr.277891.123] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 09/19/2023] [Indexed: 11/01/2023]
Abstract
Rapid advances in spatial transcriptomics (ST) have revolutionized the interrogation of spatial heterogeneity and increase the demand for comprehensive methods to effectively characterize spatial domains. As a prerequisite for ST data analysis, spatial domain characterization is a crucial step for downstream analyses and biological implications. Here we propose a prior-based self-attention framework for spatial transcriptomics (PAST), a variational graph convolutional autoencoder for ST, which effectively integrates prior information via a Bayesian neural network, captures spatial patterns via a self-attention mechanism, and enables scalable application via a ripple walk sampler strategy. Through comprehensive experiments on data sets generated by different technologies, we show that PAST can effectively characterize spatial domains and facilitate various downstream analyses, including ST visualization, spatial trajectory inference and pseudotime analysis. Also, we highlight the advantages of PAST for multislice joint embedding and automatic annotation of spatial domains in newly sequenced ST data. Compared with existing methods, PAST is the first ST method that integrates reference data to analyze ST data. We anticipate that PAST will open up new avenues for researchers to decipher ST data with customized reference data, which expands the applicability of ST technology.
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Affiliation(s)
- Zhen Li
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xiaoyang Chen
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Xuegong Zhang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Rui Jiang
- Ministry of Education Key Laboratory of Bioinformatics, Bioinformatics Division at the Beijing National Research Center for Information Science and Technology, Center for Synthetic and Systems Biology, Department of Automation, Tsinghua University, Beijing 100084, China
| | - Shengquan Chen
- School of Mathematical Sciences and LPMC, Nankai University, Tianjin 300071, China
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