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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 8, 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

SpaDiT: diffusion transformer for spatial gene expression prediction using scRNA-seq.

Xiaoyu Li¹, Fangfang Zhu², Wenwen Min¹

¹School of Information Science and Engineering, Yunnan University, 650500, Kunming, Yunnan, China.

Briefings in Bioinformatics

|November 7, 2024

Summary

This summary is machine-generated.

SpaDiT, a deep learning framework, enhances spatial transcriptomics by predicting gene expression using single-cell RNA sequencing data. This method improves the analysis of tissue structures and gene activity in spatial datasets.

Keywords:

diffusion model gene expression prediction scRNA-seq data spatial transcriptomics data transformer

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Last Updated: Jun 8, 2025

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

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Published on: July 6, 2022

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq

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Published on: March 12, 2021

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Targeted RNA Sequencing Assay to Characterize Gene Expression and Genomic Alterations

Published on: August 4, 2016

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatially resolved transcriptomics (SRT) offers insights into tissue architecture but captures limited gene expression.
Existing SRT methods struggle with high-throughput gene detection, limiting their application value.
A significant portion of gene expression remains undetected in current spatial transcriptomics (ST) datasets.

Purpose of the Study:

To develop a deep learning framework, SpaDiT, for accurate spatial reconstruction and gene expression prediction in ST data.
To leverage single-cell RNA sequencing (scRNA-seq) data as a prior to enhance ST data analysis.
To overcome the limitations of gene detection in current SRT technologies.

Main Methods:

SpaDiT utilizes scRNA-seq data as a priori information.
Shared genes between ST and scRNA-seq data are employed as latent representations for input construction.
A deep learning framework is applied for spatial reconstruction and gene expression prediction.

Main Results:

SpaDiT significantly enhances the accuracy of spatial gene expression predictions.
The framework demonstrates effectiveness across various seq-based and image-based ST datasets.
SpaDiT achieved an 8%-12% performance improvement compared to eight baseline methods.

Conclusions:

SpaDiT effectively addresses the challenge of limited gene detection in ST data.
The deep learning framework provides a valuable tool for advancing spatial transcriptomics research.
SpaDiT offers enhanced accuracy and applicability for analyzing complex biological tissues.