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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 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

Computational methods for alignment and integration of spatially resolved transcriptomics data.

Yuyao Liu¹, Can Yang²

¹Department of Automation, School of Information Science and Technology, Tsinghua University, Beijing, China.

Computational and Structural Biotechnology Journal

|March 18, 2024

Summary

This summary is machine-generated.

Spatially resolved transcriptomics (SRT) methods enable analysis of gene expression within tissues. This review compares SRT slice alignment and integration techniques for enhanced biological insights.

Keywords:

Batch effects Data integration Slices alignment Spatially resolved transcriptomics

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Last Updated: Jun 30, 2025

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Published on: July 6, 2022

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Complex biological regulation occurs in 3D, necessitating analysis of spatial context alongside molecular information.
Spatially resolved transcriptomics (SRT) technologies generate datasets linking gene expression to spatial arrangement within biological samples.
Analyzing multiple 2D slices requires slice alignment and data integration to correlate information and improve downstream analyses.

Purpose of the Study:

To review and elucidate representative methods for spatially resolved transcriptomics slice alignment and data integration.
To assess the performance of these methods on various SRT datasets and downstream tasks.
To provide insights into the strengths, weaknesses, and underlying reasons for method performance.

Main Methods:

Detailed explanation of principles behind representative SRT slice alignment and data integration methods.
Empirical testing of selected methods using diverse SRT datasets.
Evaluation of method performance in representative downstream biological analyses.

Main Results:

Comparative analysis of multiple SRT alignment and integration methods.
Performance assessment across various datasets and downstream tasks, highlighting strengths and weaknesses.
Discussion of factors influencing method efficacy in leveraging spatial transcriptomic data.

Conclusions:

The review provides a comprehensive overview and comparative analysis of current SRT slice alignment and integration techniques.
Understanding method performance is crucial for effectively utilizing spatial context in biological research.
Future directions for developing and improving SRT data analysis methods are discussed.