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

Eleftherios Zormpas; Rachel Queen; Alexis Comber; Simon J Cockell

doi:10.1016/j.cell.2023.11.003

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

Cell. 2023 Dec 21;186(26):5677-5689. doi: 10.1016/j.cell.2023.11.003. Epub 2023 Dec 7.

Authors

Eleftherios Zormpas¹, Rachel Queen², Alexis Comber³, Simon J Cockell⁴

Affiliations

¹ Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK.
² Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; Bioinformatics Support Unit, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK.
³ School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9NL, UK.
⁴ Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK; School of Biomedical, Nutritional and Sport Sciences, Faculty of Medical Sciences, Newcastle upon Tyne NE2 4HH, UK. Electronic address: simon.cockell@newcastle.ac.uk.

PMID: 38065099
DOI: 10.1016/j.cell.2023.11.003

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.

Keywords: geography; modifiable areal unit problem; omics; spatial analysis; spatial autocorrelation; spatial data; spatial heterogeneity; spatial transcriptomics.

Publication types

Review
Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology
Data Analysis*
Gene Expression Profiling
Spatial Analysis*
Transcriptome* / genetics