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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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Related Experiment Video

Updated: Jun 17, 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 clustering, alignment, and integration methods for spatial transcriptomics.

Yunfei Hu¹, Manfei Xie², Yikang Li²

¹Department of Computer Science, Vanderbilt University, 37235, Nashville, USA.

|August 9, 2024

Summary

This summary is machine-generated.

This study benchmarks spatial transcriptomics (ST) algorithms for clustering and integration. Our comprehensive analysis provides recommendations to guide tool selection and future development in ST data analysis.

Keywords:

3D reconstruction Alignment Batch correction Benchmarking Clustering Integration Spatial transcriptomics

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Last Updated: Jun 17, 2025

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

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Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Isolation and Profiling of Human Primary Mesenteric Arterial Endothelial Cells at the Transcriptome Level

Published on: March 14, 2022

Area of Science:

Spatial transcriptomics (ST) is a rapidly evolving field.
Understanding complex biological tissues requires advanced analytical methods.

Background:

Spatial transcriptomics (ST) enables detailed tissue analysis.
Challenges exist in clustering, alignment, and integration of ST data.
Lack of benchmark studies hinders method selection and development.

Purpose of the Study:

To systematically benchmark state-of-the-art spatial transcriptomics algorithms.
To evaluate algorithm performance across diverse datasets and metrics.
To provide guidance for tool selection and future method development.

Main Methods:

Benchmarking of various ST clustering, alignment, and integration algorithms.
Utilized real and simulated datasets of varying sizes, technologies, species, and complexity.
Employed quantitative and qualitative metrics including clustering accuracy, contiguity, alignment accuracy, and 3D reconstruction.

Main Results:

Identified strengths and weaknesses of different ST methods.
Evaluated performance using metrics for spatial clustering, visualization, and alignment.
Assessed method performance and data quality through diverse analyses.

Conclusions:

Provided comprehensive recommendations for selecting optimal ST tools.
Aimed to guide future development of spatial transcriptomics methods.
Facilitated reproducibility with available code, tutorials, and documentation.