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Cited by 502Single-cell and spatial transcriptomicsGene expression and cancer classificationSpatial and Panel Data Analysis

Statistical analysis of spatial expression patterns for spatially resolved transcriptomic studies

Open access
Sun, Shiquan, Zhu, Jiaqiang, Zhou, Xiang·2020·Nature Methods
DOI 10.1038/s41592-019-0701-7PMC PMC7233129

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Abstract

Identifying genes that display spatial expression patterns in spatially resolved transcriptomic studies is an important first step toward characterizing the spatial transcriptomic landscape of complex tissues. Here we present a statistical method, SPARK, for identifying spatial expression patterns of genes in data generated from various spatially resolved transcriptomic techniques. SPARK directly models spatial count data through generalized linear spatial models. It relies on recently developed statistical formulas for hypothesis testing, providing effective control of type I errors and yielding high statistical power. With a computationally efficient algorithm, which is based on penalized quasi-likelihood, SPARK is also scalable to datasets with tens of thousands of genes measured on tens of thousands of samples. Analyzing four published spatially resolved transcriptomic datasets using SPARK, we show it can be up to ten times more powerful than existing methods and disclose biological discoveries that otherwise cannot be revealed by existing approaches.

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  • The unsupervised iterative clustering algorithm has been previously utilized for scRNA-seq analysis and showed superior performance over Louvain's method.

    “This unsupervised iterative clustering algorithm has been previously utilized for scRNA-seq analysis and showed superior performance over Louvain's method 25,26 .”

    From SpaGCN: Integrating gene expression, spatial location and histology to identify spatial domains and spatially variable genes by graph convolutional network (2021) · cites this paper as [25]

    The Results (Simulations; page unavailable) compare methods for detecting spatially expressed genes, not clustering algorithms. Searches for iterative clustering, scRNA-seq clustering, and Louvain found no passage in our copy addressing the claimed prior use or performance comparison. (LLM, conf=0.74)

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Research works printed but not linked: 31

  1. [1]Chen KH, Boettiger AN, Moffitt JR, Wang S & Zhuang X RNA imaging. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015). PMC4662681 25858977
  2. [2]Lubeck E, Coskun AF, Zhiyentayev T, Ahmad M & Cai L Single-cell in situ RNA profiling by sequential hybridization. Nat Methods 11, 360– 361 (2014). 24681720 PMC4085791
  3. [3]Femino AM, Fogarty K, Lifshitz LM, Carrington W & Singer RH Visualization of single molecules of mRNA in situ. Method Enzymol 361, 245– 304 (2003). 12624916
  4. [5]Simone NL, Bonner RF, Gillespie JW, Emmert-Buck MR & Liotta LA Laser-capture microdissection: opening the microscopic frontier to molecular analysis. Trends In Genetics 14, 272– 276 (1998). 9676529
  5. [6]Junker JP Genome-wide RNA Tomography in the Zebrafish Embryo. Cell 159, 662– 675 (2014). 25417113
  6. [9]Lee JH Highly Multiplexed Subcellular RNA Sequencing in Situ. Science 343, 1360– 1363 (2014). 24578530 PMC4140943
  7. [11]Fan X Spatial transcriptomic survey of human embryonic cerebral cortex by single-cell RNA-seq analysis. Cell Res 28, 730– 745 (2018). 29867213 PMC6028726
  8. [13]Edsgard D, Johnsson P & Sandberg R Identification of spatial expression trends in single-cell gene expression data. Nat Methods 15, 339– 342 (2018). 29553578 PMC6314435
  9. [14]Lea AJ, Alberts SC, Tung J & Zhou X A flexible, efficient binomial mixed model for identifying differential DNA methylation in bisulfite sequencing data. Plos Genet 11, e1005650 (2015). 26599596 PMC4657956
  10. [15]Sun SQ Differential expression analysis for RNAseq using Poisson mixed models. Nucleic Acids Res 45, e106 (2017). 28369632 PMC5499851
  11. [16]Lun A Overcoming systematic errors caused by log-transformation of normalized single-cell RNA sequencing data. BioRxiv, 404962 (2019).
  12. [17]Li Y, Tang HC & Lin XH Spatial Linear Mixed Models with Covariate Measurement Errors. Stat Sinica 19, 1077– 1093 (2009). PMC2695401 20046975
  13. [20]Sun SQ Heritability estimation and differential analysis of count data with generalized linear mixed models in genomic sequencing studies. Bioinformatics 35, 487– 496 (2019). 30020412 PMC6361238
  14. [21]Liu YW ACAT: A Fast and Powerful p Value Combination Method for Rare-Variant Analysis in Sequencing Studies. Am J Hum Genet 104, 410– 421 (2019). 30849328 PMC6407498
  15. [24]Rouillard AD The harmonizome: a collection of processed datasets gathered to serve and mine knowledge about genes and proteins. Database-Oxford 2016, baw100 (2016). 27374120 PMC4930834
  16. [26]Lever J, Zhao EY, Grewal J, Jones MR & Jones SJM CancerMine: a literature-mined resource for drivers, oncogenes and tumor suppressors in cancer. Nat Methods 16, 505– 507 (2019). 31110280
  17. [27]Moffitt JR Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018). PMC6482113 30385464
  18. [28]Fabio K Synthesis and evaluation of potent and selective human V1a receptor antagonists as potential ligands for PET or SPECT imaging. Bioorgan Med Chem 20, 1337– 1345 (2012). PMC3449305 22249122
  19. [29]Ozturk A, DeKosky ST & Kamboh MI Genetic variation in the choline acetyltransferase (CHAT) gene may be associated with the risk of Alzheimer’s disease. Neurobiol Aging 27, 1440– 1444 (2006). 16223550 PMC1609962
  20. [30]Kiaris H, Schally AV & Kalofoutis A Extrapituitary effects of the growth hormone-releasing hormone. Vitam Horm 70, 1– 24 (2005). 15727800
  21. [31]Shah S, Lubeck E, Zhou W & Cai L In Situ Transcription Profiling of Single Cells Reveals Spatial Organization of Cells in the Mouse Hippocampus. Neuron 92, 342– 357 (2016). 27764670 PMC5087994
  22. [33]Li XH, Polter A & Yang S FoxO transcription factors - Regulation in brain and behavioral manifestation. Biol Psychiat 63, 150– 159 (2008). PMC2630515 18823877
  23. [34]Hoekman MFM, Jacobs FMJ, Smidt MP & Burbach JPH Spatial and temporal expression of FoxO transcription factors in the developing and adult murine brain. Gene Expr Patterns 6, 134– 140 (2006). 16326148
  24. [36]Shrestha BR Sensory Neuron Diversity in the Inner Ear Is Shaped by Activity. Cell 174, 1229– 1246 (2018). 30078709 PMC6150604
  25. [37]Sun YF A central role for Islet1 in sensory neuron development linking sensory and spinal gene regulatory programs. Nat Neurosci 11, 1283– 1293 (2008). 18849985 PMC2605652
  26. [40]Lark RM, Heuvelink GBM & Bishop TFA Burgess TM & Webster R. 1980. Optimal interpolation and isarithmic mapping of soil properties. I. The semi-variogram and punctual kriging. Journal of Soil Science, 31, 315–331. Eur J Soil Sci 70, 7– 10 (2019).
  27. [43]Rodriques SG Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463– 1467 (2019). 30923225 PMC6927209
  28. [45]Christensen OF & Waagepetersen R Bayesian prediction of spatial count data using generalized linear mixed models. Biometrics 58, 280– 286 (2002). 12071400
  29. [47]Vanhatalo J, Pietilainen V & Vehtari A Approximate inference for disease mapping with sparse Gaussian processes. Statistics in medicine 29, 1580– 1607 (2010). 20552572
  30. [51]Yu GC, Wang LG, Han YY & He QY clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. Omics 16, 284– 287 (2012). 22455463 PMC3339379
  31. [52]Moffitt JR in Dryad Digital Repository, Vol. 362 (ed. Repository DD) eaau5324 (2018).

Cited by

Sorted by citations

Authors
Sun, S., Zhu, J., & Zhou, X.
Affiliations
  • Northwestern Polytechnical University (CN)
  • University of Michigan (US)
Corresponding author
Zhou, Xiang
ORCID
Venue
Nature Methods
Publisher
Springer Science and Business Media LLC
Published
2020-01-27
Volume
17
Issue
2
Pages
193-200
ISSN
  • 1548-7091 (ISSN-L)
  • 1548-7105
DOI
10.1038/s41592-019-0701-7
PMC
PMC7233129
OpenAlex
W3001833132
Cited by
502
References
13
Citations by year
2026: 76 · 2025: 94 · 2024: 90 · 2023: 69 · 2022: 79 · 2021: 80 · 2020: 12 · 2019: 2
FWCI
20.12
Type
journal article
Language
en
Open access
Licence
Locations
  • Nature Methods (publishedVersion)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (R01HG009124)
  • U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01GM126553)
  • National Science Foundation (DMS1712933)
  • U.S. Department of Health & Human Services | NIH | Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD088558)
  • U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (U01HL137182)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
  • Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Spatial and Panel Data Analysis
Keywords
spatial expression patterns, spatial transcriptomics, Spark, spatial count data, hypothesis testing, type I error control, statistical power, penalized quasi-likelihood
MeSH terms
Algorithms, Gene Expression Regulation, Humans, Likelihood Functions, Transcriptome

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