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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Updated: Jun 30, 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

Benchmarking spatial clustering methods with spatially resolved transcriptomics data.

Zhiyuan Yuan^1,2, Fangyuan Zhao^3,4, Senlin Lin^3,4

¹Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Fudan University, Shanghai, China. zhiyuan@fudan.edu.cn.

|March 16, 2024

Summary

This summary is machine-generated.

This study benchmarks 13 computational methods for spatial clustering using spatially resolved transcriptomics (SRT) data. It reveals complementary method performance but highlights limitations in handling complex spatial domains and large-scale tasks.

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Area of Science:

Computational biology
Genomics
Tissue physiology

Background:

Spatially resolved transcriptomics (SRT) enables structure-centroid analysis in tissue physiology.
Computational methods for spatial clustering have rapidly advanced.
A comprehensive benchmark of these methods is currently lacking.

Purpose of the Study:

To benchmark 13 computational methods for spatial clustering using SRT data.
To evaluate method performance based on accuracy, spatial continuity, marker gene detection, scalability, and robustness.
To provide guidance for selecting appropriate spatial clustering methods.

Main Methods:

Evaluation of 13 computational methods on 34 SRT datasets (7 datasets).
Performance assessment across accuracy, spatial continuity, marker gene detection, scalability, and robustness.
Testing on 22 additional challenging datasets and 145 simulated datasets to assess robustness and preprocessing impacts.

Main Results:

Existing spatial clustering methods show complementary performance and functionality.
Identified challenges in detecting noncontinuous spatial domains and limitations in large-scale tasks.
Assessed method robustness against various factors and the impact of preprocessing.

Conclusions:

The study provides a comprehensive evaluation of current spatial clustering methods for SRT data.
Highlights the need for improved methods to handle complex spatial structures and large datasets.
Offers guidance for method selection and identifies areas for future research in spatial transcriptomics analysis.