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

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

Journal: Cell 2024;186(26):5677-5689

PMID: 38065099

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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.

Address: Biosciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK.; 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.; School of Geography and Leeds Institute for Data Analytics, University of Leeds, Leeds LS2 9NL, UK.; 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: [email protected].

DOI: 10.1016/j.cell.2023.11.003

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MeSH Terms: Computational Biology, Data Analysis, Gene Expression Profiling, Spatial Analysis, Transcriptome