Navigating the landscapes of spatial transcriptomics: How computational methods guide the way

Abstract

Spatially resolved transcriptomics has been dramatically transforming biological and medical research in various fields. It enables transcriptome profiling at single‐cell, multi‐cellular, or sub‐cellular resolution, while retaining the information of geometric localizations of cells in complex tissues. The coupling of cell spatial information and its molecular characteristics generates a novel multi‐modal high‐throughput data source, which poses new challenges for the development of analytical methods for data‐mining. Spatial transcriptomic data are often highly complex, noisy, and biased, presenting a series of difficulties, many unresolved, for data analysis and generation of biological insights. In addition, to keep pace with the ever‐evolving spatial transcriptomic experimental technologies, the existing analytical theories and tools need to be updated and reformed accordingly. In this review, we provide an overview and discussion of the current computational approaches for mining of spatial transcriptomics data. Future directions and perspectives of methodology design are proposed to stimulate further discussions and advances in new analytical models and algorithms.This article is categorized under: RNA Methods > RNA Analyses in Cells RNA Evolution and Genomics > Computational Analyses of RNA RNA Export and Localization > RNA Localization

Uncovering periodontitis-associated markers through the aggregation of transcriptomics information from diverse sources

“…Li et al point out that although a variety of algorithms have been designed to integrate spatial and single-cell transcriptome data, there are significant differences in how these algorithms work and their scope of application ( Li et al, 2022 ). Spatial transcriptome data are highly non-ideal, including features such as complex data structure, low signal-to-noise ratio, high sparsity, and uneven coverage, which pose challenges for in-depth analysis of the data and parsing of biological information ( Li et al, 2024 ). Therefore, efficient algorithms are necessary for identification and analysis.…”

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“…Li et al point out that although a variety of algorithms have been designed to integrate spatial and single-cell transcriptome data, there are significant differences in how these algorithms work and their scope of application (Li et al, 2022). Spatial transcriptome data are highly non-ideal, including features such as complex data structure, low signal-to-noise ratio, high sparsity, and uneven coverage, which pose challenges for in-depth analysis of the data and parsing of biological information (Li et al, 2024). Therefore, efficient algorithms are necessary for identification and analysis.…”