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

Area of Science:

• Molecular Biology
• Bioinformatics
• Genomics

Background:

• Spatially resolved transcriptomics provides single-cell to sub-cellular resolution of gene expression while preserving spatial localization within tissues.
• This generates complex, multi-modal high-throughput data, integrating molecular and geometric information.
• Analyzing this data presents significant challenges due to its complexity, noise, and biases.

Purpose of the Study:

• To provide a comprehensive overview of current computational approaches for analyzing spatial transcriptomic data.
• To discuss the challenges and limitations of existing analytical methods.
• To propose future directions and perspectives for developing new analytical models and algorithms.

Main Methods:

• Review of existing literature on computational methods for spatial transcriptomics data analysis.
• Discussion of data mining techniques applicable to spatial transcriptomic datasets.
• Exploration of challenges in handling high-throughput, multi-modal spatial data.

Main Results:

• Identified key computational strategies currently employed for spatial transcriptomics data mining.
• Highlighted unresolved difficulties in analyzing complex, noisy, and biased spatial transcriptomic data.
• Emphasized the need for continuous updates and reforms in analytical theories and tools.

Conclusions:

• Spatially resolved transcriptomics is a transformative technology requiring advanced computational methods.
• Further development of analytical models and algorithms is crucial for unlocking biological insights from spatial transcriptomic data.
• This review aims to stimulate progress in the field of computational spatial transcriptomics analysis.