IDEAS home Printed from https://ideas.repec.org/a/plo/pgen00/1010983.html
   My bibliography  Save this article

SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data

Author

Listed:
  • Souvik Seal
  • Benjamin G Bitler
  • Debashis Ghosh

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.Author summary: In recent years, spatial transcriptomics (ST) has become increasingly popular to study the expression profile of genes across different spatial locations of a tissue. Many of the genes exhibit spatially varying expression patterns making them immensely valuable for understanding the structural and functional properties of the tissue. The proposed method termed SMASH enables powerful and scalable detection of such genes in high-dimensional ST datasets.

Suggested Citation

  • Souvik Seal & Benjamin G Bitler & Debashis Ghosh, 2023. "SMASH: Scalable Method for Analyzing Spatial Heterogeneity of genes in spatial transcriptomics data," PLOS Genetics, Public Library of Science, vol. 19(10), pages 1-25, October.
    • Handle: RePEc:plo:pgen00:1010983
    DOI: 10.1371/journal.pgen.1010983
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosgenetics/article?id=10.1371/journal.pgen.1010983
    Download Restriction: no
    File URL: https://journals.plos.org/plosgenetics/article/file?id=10.1371/journal.pgen.1010983&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pgen.1010983?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Souvik Seal & Abhirup Datta & Saonli Basu, 2022. "Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies," PLOS Genetics, Public Library of Science, vol. 18(4), pages 1-19, April.
    2. Miranda V. Hunter & Reuben Moncada & Joshua M. Weiss & Itai Yanai & Richard M. White, 2021. "Spatially resolved transcriptomics reveals the architecture of the tumor-microenvironment interface," Nature Communications, Nature, vol. 12(1), pages 1-16, December.
    3. Dawei Liu & Xihong Lin & Debashis Ghosh, 2007. "Semiparametric Regression of Multidimensional Genetic Pathway Data: Least-Squares Kernel Machines and Linear Mixed Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1079-1088, December.
    4. Lyla Atta & Jean Fan, 2021. "Computational challenges and opportunities in spatially resolved transcriptomic data analysis," Nature Communications, Nature, vol. 12(1), pages 1-5, December.
    5. Emelie Berglund & Jonas Maaskola & Niklas Schultz & Stefanie Friedrich & Maja Marklund & Joseph Bergenstråhle & Firas Tarish & Anna Tanoglidi & Sanja Vickovic & Ludvig Larsson & Fredrik Salmén & Chri, 2018. "Spatial maps of prostate cancer transcriptomes reveal an unexplored landscape of heterogeneity," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    6. Lukas M. Weber & Arkajyoti Saha & Abhirup Datta & Kasper D. Hansen & Stephanie C. Hicks, 2023. "nnSVG for the scalable identification of spatially variable genes using nearest-neighbor Gaussian processes," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Haohao Su & Yuesong Wu & Bin Chen & Yuehua Cui, 2025. "STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
    2. S. Vickovic & B. Lötstedt & J. Klughammer & S. Mages & Å Segerstolpe & O. Rozenblatt-Rosen & A. Regev, 2022. "SM-Omics is an automated platform for high-throughput spatial multi-omics," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    3. Aditi Sahu & Kivanc Kose & Lukas Kraehenbuehl & Candice Byers & Aliya Holland & Teguru Tembo & Anthony Santella & Anabel Alfonso & Madison Li & Miguel Cordova & Melissa Gill & Christi Fox & Salvador G, 2022. "In vivo tumor immune microenvironment phenotypes correlate with inflammation and vasculature to predict immunotherapy response," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    4. Zaili Fang & Inyoung Kim & Jeesun Jung, 2018. "Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 129-152, March.
    5. Arnab Maity & Xihong Lin, 2011. "Powerful Tests for Detecting a Gene Effect in the Presence of Possible Gene–Gene Interactions Using Garrote Kernel Machines," Biometrics, The International Biometric Society, vol. 67(4), pages 1271-1284, December.
    6. Chenglong Sun & Anqiang Wang & Yanhe Zhou & Panpan Chen & Xiangyi Wang & Jianpeng Huang & Jiamin Gao & Xiao Wang & Liebo Shu & Jiawei Lu & Wentao Dai & Zhaode Bu & Jiafu Ji & Jiuming He, 2023. "Spatially resolved multi-omics highlights cell-specific metabolic remodeling and interactions in gastric cancer," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Long Qu & Tobias Guennel & Scott L. Marshall, 2013. "Linear Score Tests for Variance Components in Linear Mixed Models and Applications to Genetic Association Studies," Biometrics, The International Biometric Society, vol. 69(4), pages 883-892, December.
    8. Haoyang Li & Juexiao Zhou & Zhongxiao Li & Siyuan Chen & Xingyu Liao & Bin Zhang & Ruochi Zhang & Yu Wang & Shiwei Sun & Xin Gao, 2023. "A comprehensive benchmarking with practical guidelines for cellular deconvolution of spatial transcriptomics," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    9. Teran Hidalgo, Sebastian J. & Wu, Michael C. & Engel, Stephanie M. & Kosorok, Michael R., 2018. "Goodness-of-fit test for nonparametric regression models: Smoothing spline ANOVA models as example," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 135-155.
    10. Lin Zhang & Inyoung Kim, 2021. "Finite mixtures of semiparametric Bayesian survival kernel machine regressions: Application to breast cancer gene pathway subgroup analysis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(2), pages 251-269, March.
    11. Chakraborty, Sounak, 2009. "Bayesian binary kernel probit model for microarray based cancer classification and gene selection," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4198-4209, October.
    12. Yunxuan Jiang & Karen N. Conneely & Michael P. Epstein, 2018. "Robust Rare-Variant Association Tests for Quantitative Traits in General Pedigrees," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(3), pages 491-505, December.
    13. Maja Marklund & Niklas Schultz & Stefanie Friedrich & Emelie Berglund & Firas Tarish & Anna Tanoglidi & Yao Liu & Ludvig Bergenstråhle & Andrew Erickson & Thomas Helleday & Alastair D. Lamb & Erik Son, 2022. "Spatio-temporal analysis of prostate tumors in situ suggests pre-existence of treatment-resistant clones," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    14. Glen McGee & Ander Wilson & Thomas F. Webster & Brent A. Coull, 2023. "Bayesian multiple index models for environmental mixtures," Biometrics, The International Biometric Society, vol. 79(1), pages 462-474, March.
    15. Lulu Shang & Peijun Wu & Xiang Zhou, 2025. "Statistical identification of cell type-specific spatially variable genes in spatial transcriptomics," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
    16. Ziyang Tang & Zuotian Li & Tieying Hou & Tonglin Zhang & Baijian Yang & Jing Su & Qianqian Song, 2023. "SiGra: single-cell spatial elucidation through an image-augmented graph transformer," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    17. Pluta, Dustin & Yu, Zhaoxia & Shen, Tong & Chen, Chuansheng & Xue, Gui & Ombao, Hernando, 2018. "Statistical methods and challenges in connectome genetics," Statistics & Probability Letters, Elsevier, vol. 136(C), pages 83-86.
    18. Linhua Wang & Mirjana Maletic-Savatic & Zhandong Liu, 2022. "Region-specific denoising identifies spatial co-expression patterns and intra-tissue heterogeneity in spatially resolved transcriptomics data," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    19. Zhiyuan Yuan & Yisi Li & Minglei Shi & Fan Yang & Juntao Gao & Jianhua Yao & Michael Q. Zhang, 2022. "SOTIP is a versatile method for microenvironment modeling with spatial omics data," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    20. Guidantonio Malagoli Tagliazucchi & Anna J. Wiecek & Eloise Withnell & Maria Secrier, 2023. "Genomic and microenvironmental heterogeneity shaping epithelial-to-mesenchymal trajectories in cancer," Nature Communications, Nature, vol. 14(1), pages 1-20, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pgen00:1010983. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosgenetics (email available below). General contact details of provider: https://journals.plos.org/plosgenetics/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.