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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: May 14, 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

STCC enhances spatial domain detection through consensus clustering of spatial transcriptomics data.

Congcong Hu¹, Nana Wei^2,3, Jiyuan Yang¹

¹Center for Single-Cell Omics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China.

Genome Research

|May 12, 2025

Summary

This summary is machine-generated.

Spatial domain detection in spatial transcriptomics is crucial for biological insights. Our STCC framework uses consensus clustering to improve accuracy and stability by integrating multiple analysis tools, outperforming individual methods.

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Published on: March 14, 2022

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Spatially resolved transcriptomics generates complex data requiring robust analysis.
Spatial domain detection (clustering) is a fundamental step for biological interpretation.
Existing tools for spatial domain detection show variable performance across datasets and platforms.

Purpose of the Study:

To develop a novel consensus clustering framework (STCC) for spatial transcriptomics data.
To improve the accuracy and stability of spatial domain detection by aggregating results from multiple tools.
To evaluate the performance of consensus clustering strategies on diverse datasets.

Main Methods:

Developed STCC, a consensus clustering framework for spatial transcriptomics.
Integrated state-of-the-art tools using various consensus strategies (Onehot-based, average-based, hypergraph-based, wNMF-based).
Performed comprehensive assessments on simulated and real-world data from different experimental platforms.

Main Results:

Consensus clustering significantly enhances clustering accuracy compared to individual methods across various parameters.
STCC demonstrated improved results for normal tissues with layered structures when integrating multiple methods.
For tumor samples with scattered patterns, integrating a single baseline method provided satisfactory performance.
Average-based and hypergraph-based consensus strategies showed optimal precision and stability.

Conclusions:

STCC offers a scalable and practical solution for spatial domain detection in spatial transcriptomics.
Consensus clustering improves biological insight derivation from spatial transcriptomics data.
The framework provides a foundation for future research and applications in the field.