Zormpas, E., Queen, R., Comber, A. orcid.org/0000-0002-3652-7846 et al. (1 more author) (2023) Mapping the transcriptome: Realizing the full potential of spatial data analysis. Cell, 186 (26). pp. 5677-5689. ISSN 0092-8674
Abstract
RNA sequencing in situ allows for whole-transcriptome characterization at high resolution, while retaining spatial information. These data present an analytical challenge for bioinformatics—how to leverage spatial information effectively? Properties of data with a spatial dimension require special handling, which necessitate a different set of statistical and inferential considerations when compared to non-spatial data. The geographical sciences primarily use spatial data and have developed methods to analye them. Here we discuss the challenges associated with spatial analysis and examine how we can take advantage of practice from the geographical sciences to realize the full potential of spatial information in transcriptomic datasets.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
Keywords: | spatial data; spatial analysis; omics; spatial transcriptomics; geography; spatial autocorrelation; spatial heterogeneity; modifiable areal unit problem |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 30 Nov 2023 15:50 |
Last Modified: | 26 Jan 2024 16:49 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.cell.2023.11.003 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:205737 |