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Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

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Spatial Transcriptomics Brings New Challenges and Opportunities for Trajectory Inference.

Matthieu Heitz¹, Yujia Ma¹, Sharvaj Kubal¹

¹Department of Mathematics, University of British Columbia, Vancouver, British Columbia, Canada;

Annual Review of Biomedical Data Science

|November 14, 2024

Summary

Spatial transcriptomics (ST) analysis faces challenges in trajectory inference (TI) due to spatial batch effects and measurement limitations. This review examines current methods and future directions for ST trajectory inference.

Keywords:

batch effects common coordinate framework spatial transcriptomics spatiotemporal analysis trajectory inference

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Trajectory Data Analyses for Pedestrian Space-time Activity Study

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatial transcriptomics (ST) integrates spatial information with single-cell gene expression data.
Traditional single-cell analysis methods often require adaptation for ST data.
Trajectory inference (TI) methods are particularly susceptible to challenges in ST data analysis.

Purpose of the Study:

To review the challenges encountered when applying trajectory inference to spatial transcriptomics data.
To examine current state-of-the-art methods for spatial transcriptomics trajectory inference (STTI).
To identify opportunities for future method development in STTI.

Main Methods:

Review of existing literature on spatial transcriptomics and trajectory inference.
Analysis of challenges including spatial batch effects, tissue deformation, measurement granularity, and slicing bias.
Exploration of current STTI methodologies addressing these challenges.

Main Results:

Spatial batch effects introduce significant noise and complexity into ST data for TI.
Existing STTI methods primarily focus on physical arrangement batch effects.
Measurement granularity and slicing biases present additional, often unaddressed, challenges.

Conclusions:

Addressing diverse sources of noise and bias is crucial for accurate STTI.
Further methodological development is needed to overcome current limitations in STTI.
Future research should focus on robust methods for spatial transcriptomics trajectory inference.