A Bayesian multivariate mixture model for high throughput spatial transcriptomics-Reference-Cited by-同舟云学术

A Bayesian multivariate mixture model for high throughput spatial transcriptomics

Published:2022-08-10 Issue: Volume: Page:
ISSN:0006-341X
Container-title:Biometrics
language:en
Short-container-title:Biometrics

Author:

Allen Carter¹²,Chang Yuzhou¹²,Neelon Brian³^ORCID,Chang Won⁴^ORCID,Kim Hang J.⁴,Li Zihai²,Ma Qin¹²^ORCID,Chung Dongjun¹²^ORCID

Affiliation:

1. Department of Biomedical Informatics The Ohio State University Columbus Ohio USA

2. The Pelotonia Institute for Immuno‐Oncology The Ohio State University Comprehensive Cancer Center Columbus Ohio USA

3. Department of Public Health Sciences Medical University of South Carolina Charleston South Carolina USA

4. Division of Statistics and Data Science University of Cincinnati Cincinnati Ohio USA

Funder

National Institute on Aging

National Institute on Drug Abuse

National Human Genome Research Institute

National Institute of General Medical Sciences

National Cancer Institute

Publisher

Wiley

Subject

Applied Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,General Medicine,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/biom.13727

Reference44 articles.

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4. 10X Genomics(2019d)Mouse brain serial section 2 (sagittal‐posterior): spatial gene expression dataset by Space Ranger 1.0.0.https://www.10xgenomics.com/resources/datasets/mouse‐brain‐serial‐section‐2‐sagittal‐posterior‐1‐standard‐1‐1‐0. Accessed 15 March 2022.

5. 10X Genomics(2020a)Human breast cancer (block a section 1): spatial gene expression dataset by Space Ranger 1.1.0.https://support.10xgenomics.com/spatial‐gene‐expression/datasets/1.0.0/V1_Breast_Cancer_Block_A_Section_1. Accessed 15 March 2022.

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