Litcius/Paper detail

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

Eleftherios Zormpas, Rachel Queen, Alexis Comber, Simon Cockell

2023Cell89 citationsDOIOpen Access PDF

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.

Topics & Concepts

Spatial analysisLeverage (statistics)TranscriptomeBiologyComputational biologyData setSet (abstract data type)Data scienceDimension (graph theory)Data miningComputer scienceGeneticsMachine learningArtificial intelligenceGeneGene expressionPure mathematicsGeologyMathematicsProgramming languageRemote sensingSingle-cell and spatial transcriptomicsGene expression and cancer classification