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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: Jul 1, 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

Deep learning in spatially resolved transcriptfomics: a comprehensive technical view

Roxana Zahedi¹, Reza Ghamsari¹, Ahmadreza Argha^2,3

¹UNSW BioMedical Machine Learning Lab (BML), The Graduate School of Biomedical Engineering, UNSW Sydney, 2052, NSW, Australia.

Briefings in Bioinformatics

|March 14, 2024

Summary

This summary is machine-generated.

Spatially resolved transcriptomics (SRT) analysis requires advanced methods beyond traditional machine learning. Deep learning shows promise but needs refinement for biological nuances and data challenges, with new resources available for researchers.

Keywords:

Spatially resolved transcriptomics deep learning gene expression histology images multimodal analysis

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Last Updated: Jul 1, 2025

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) offers single-cell resolution of gene expression within morphological contexts.
SRT data complexity challenges conventional analytical methods, necessitating advanced approaches.
Deep learning is increasingly adopted for SRT tasks like spatial clustering and gene identification.

Purpose of the Study:

To critically evaluate deep learning methodologies for SRT data analysis.
To identify limitations and areas for improvement in current deep learning models for SRT.
To provide resources for the SRT research community.

Main Methods:

Critique of existing deep learning algorithms applied to SRT.
Analysis of challenges including batch effects, normalization, and count distribution.
Compilation of accessible SRT databases.

Main Results:

Deep learning models show promise for SRT but require enhancement for biological complexity.
Key challenges include incorporating biological nuances and addressing data artifacts.
A curated directory of SRT databases is provided.

Conclusions:

Further development of deep learning is crucial for advancing SRT.
Models need to integrate biological intricacies and robustly handle data imperfections.
The provided database directory aims to facilitate future SRT research.