BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Zhao, Edward; Stone, Matthew R.; Ren, Xing; Pulliam, Thomas H.; Nghiem, Paul; Bielas, Jason H.; Gottardo, Raphaël

doi:10.1101/2020.09.04.283812

Cited by 18 publications

(22 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, using the customized website spatial.libd.org/spatialLIBD, researchers can perform spatial registration of their scRNA-seq clusters. This data can also be used for developing new analytical methods (Biancalani et al, 2020;Zhao et al, 2020).…”

Section: Resultsmentioning

confidence: 99%

“…However, Loupe and Giotto currently do not support visualizing more than one tissue section at a time, which is useful for comparing replicates and annotating observed spots on the Visium array across samples. Furthermore, while unsupervised clustering methods are widely developed (Zhao et al, 2020;Biancalani et al, 2020), manual annotation of spots using known marker genes is important, as well as cross-sample dimension reduction techniques such as UMAP and t-SNE.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

Pardo

Spangler

Weber

et al. 2021

Preprint

View full text Add to dashboard Cite

Motivation: Spatially-resolved transcriptomics has now enabled the quantification of high-throughput and transcriptome-wide gene expression in intact tissue while also retaining the spatial coordinates. Incorporating the precise spatial mapping of gene activity advances our understanding of intact tissue-specific biological processes. In order to interpret these novel spatial data types, interactive visualization tools are necessary. Results: We describe spatialLIBD, an R/Bioconductor package to interactively explore spatially-resolved transcriptomics data generated with the 10x Genomics Visium platform. The package contains functions to interactively access, visualize, and inspect the observed spatial gene expression data and data-driven clusters identified with supervised or unsupervised analyses, either on the user's computer or through a web application. Availability: spatialLIBD is available at http://bioconductor.org/packages/spatialLIBD.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

Pardo

Spangler

Weber

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Further integration of spatial datasets with transcriptomics and other single-cell data modalities will uncover an unprecedented level of understanding of the composition and function of the organism (92). The rapid proliferation of single-cell omics has greatly improved the understanding of heterogeneity within cell populations, and any serious approach that hopes to advance this knowledge must leverage all the tools available, including new statistical methods that can efficiently leverage diverse sources of information (93).…”

Section: Reasoning For Multimodal Approachesmentioning

confidence: 99%

Single-Cell Analysis for Whole-Organism Datasets

Pisco

Tojo

McGeever

2021

Annu. Rev. Biomed. Data Sci.

View full text Add to dashboard Cite

Cell atlases are essential companions to the genome as they elucidate how genes are used in a cell type–specific manner or how the usage of genes changes over the lifetime of an organism. This review explores recent advances in whole-organism single-cell atlases, which enable understanding of cell heterogeneity and tissue and cell fate, both in health and disease. Here we provide an overview of recent efforts to build cell atlases across species and discuss the challenges that the field is currently facing. Moreover, we propose the concept of having a knowledgebase that can scale with the number of experiments and computational approaches and a new feedback loop for development and benchmarking of computational methods that includes contributions from the users. These two aspects are key for community efforts in single-cell biology that will help produce a comprehensive annotated map of cell types and states with unparalleled resolution. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

show abstract

“…In the presence of spatial coordinates for each transcriptome profiled spot, spatial clustering methods achieve better classification accuracy. For example, Zhao et al [2020] showed that BayesSpace improves resolution and achieves better classification accuracy in manually-annotated human brain samples. However, these existing spatial clustering methods have certain limitations.…”

Section: Introductionmentioning

confidence: 99%

“…To address all these limitations, we propose a Spatial Clustering using the hidden Markov random field based on Empirical Bayes (SC-MEB) to model a low-dimensional representation of the gene expression matrix incorporating the spatial coordinates for each measurement. Compared with existing methods [Dries et al, 2019, Zhao et al, 2020, SC-MEB is not only computationally e cient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. We derive an e cient expectation-maximization (EM) algorithm based on iterative conditional mode (ICM) and further choose the number of clusters for SC-MEB with the Bayesian information criterion (BIC) [Claeskens et al, 2008].…”

Section: Introductionmentioning

confidence: 99%

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

Yang

Shi

Liu

et al. 2021

Preprint

View full text Add to dashboard Cite

Spatial transcriptomics (ST) has been emerging as a powerful technique for resolving gene expression profiles while retaining the tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex mulitcellular systems under different microenvironments. To answer scientific questions with spatial transcriptomics, the first step is to identify cell clusters by integrating available spatial information which would expand the understanding of how cell types and states are regulated by tissues. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using hidden Markov random field. We also derive an efficient expectation-maximization (EM) algorithm based on iterative conditional mode (ICM) for SC-MEB. Compared with BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to the sample size increment, but also is capable of choosing the smoothness parameter and the number of clusters as well. We then performed comprehensive simulation studies to demonstrate the effectiveness of SC-MEB over some existing methods. We first applied SC-MEB to perform clustering analysis in the spatial transcriptomics dataset from human dorsolateral prefrontal cortex tissues that were manually annotated. Our analysis results show that SC-MEB can achieve similar clustering performance with BayesSpace that uses the true number of clusters and fixed smoothness parameter. We then applied SC-MEB to analyze the dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap for identified signature genes shows that the identified clusters from SC-MEB is more separable than those from BayesSpace. By using pathway analysis, we identified three immune-related clusters and further compared mean expressions of COVID-19 signature genes in immune regions with those in non-immune ones. As such, SC-MEB provides a valuable computational tool for investigating structural organizations of tissues from spatial transcriptomic data.

show abstract

BayesSpace enables the robust characterization of spatial gene expression architecture in tissue sections at increased resolution

Cited by 18 publications

References 35 publications

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data

Single-Cell Analysis for Whole-Organism Datasets

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes

Contact Info

Product

Resources

About