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Updated: Sep 29, 2025

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

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

Published on: July 6, 2022

Statistical and machine learning methods for spatially resolved transcriptomics data analysis.

Zexian Zeng^1,2,3, Yawei Li⁴, Yiming Li⁴

¹Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, 100084, China.

|March 26, 2022

Summary

This summary is machine-generated.

Advancements in spatial transcriptomics require new analytical methods. This review covers statistical and machine learning approaches for analyzing complex spatial transcriptomics data.

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Published on: January 10, 2019

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Spatial transcriptomics enables simultaneous measurement of gene expression and spatial coordinates within tissues.
Technological improvements increase data capacity and efficiency, necessitating advanced analytical tools.
Evolving sequencing protocols challenge existing analytical method assumptions.

Purpose of the Study:

To review recent statistical and machine learning methods for spatial transcriptomics data analysis.
To summarize available resources for researchers in the field.
To identify future challenges and opportunities in spatial transcriptomics analysis.

Main Methods:

Literature review of statistical and machine learning methodologies.
Synthesis of current analytical approaches for spatial transcriptomics.
Identification of trends and gaps in the field.

Main Results:

Overview of diverse analytical methods, including those for cell type identification, spatial pattern detection, and trajectory inference.
Compilation of software tools and computational resources.
Discussion on the need for scalable and robust methods to handle high-dimensional spatial transcriptomics data.

Conclusions:

The rapid progress in spatial transcriptomics necessitates continuous development of sophisticated analytical techniques.
Machine learning and statistical modeling are crucial for extracting biological insights from complex spatial data.
Future research should focus on integrating multi-modal data and developing interpretable models.