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Mapping the transcriptome: Realizing the full potential of spatial data analysis

Lookup NU author(s): Lefteris Zormpas, Dr Rachel Queen, Dr Simon CockellORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

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.

Publication metadata

Author(s): Zormpas E, Queen R, Comber A, Cockell SJ

Publication type: Article

Publication status: Published

Journal: Cell

Year: 2023

Volume: 186

Issue: 26

Pages: 5677 - 5689

Print publication date: 21/12/2023

Online publication date: 07/12/2023

Acceptance date: 02/11/2023

Date deposited: 13/02/2024

ISSN (print): 0092-8674

ISSN (electronic): 1097-4172

Publisher: Cell Press

URL: https://doi.org/10.1016/j.cell.2023.11.003

DOI: 10.1016/j.cell.2023.11.003

Data Access Statement: The data used to produce the figures come from the human dorsolateral prefrontal cortex (DLPFC) 10X Visium dataset65 and specifically tissue section 151673. The code and processed data that produced the figures is available via Zenodo at: https://doi.org/10.5281/zenodo.8333525

PubMed id: 38065099

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