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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 4, 2025

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Author Spotlight: Integrating Organoid Models with Single-Cell and Spatial Transcriptomics Technologies

Published on: March 29, 2024

STCGAN: a novel cycle-consistent generative adversarial network for spatial transcriptomics cellular deconvolution.

Bo Wang¹, Yahui Long², Yuting Bai¹

¹College of Computer Science and Electronic Engineering, Hunan University, Changsha, 410083, China.

Briefings in Bioinformatics

|December 23, 2024

Summary

This summary is machine-generated.

Spatial transcriptomics (ST) enables gene expression mapping in tissues. We developed STCGAN, a novel method using cycle-consistent generative adversarial networks, to accurately deconvolute cell types and reconstruct their spatial distribution from ST data.

Keywords:

cellular deconvolution cycle adversarial network graph convolutional network spatial transcriptomics

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Last Updated: Jun 4, 2025

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

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

Published on: March 12, 2021

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Spatial transcriptomics (ST) offers insights into tissue architecture and cellular heterogeneity.
Accurate cell-type deconvolution from sparse ST data is crucial but challenging.
Existing methods often fail to capture tissue complexity at the single-cell level.

Purpose of the Study:

To develop a novel computational method for accurate cell-type deconvolution in spatial transcriptomics.
To improve the reconstruction of cell spatial distribution within tissues.
To address limitations of current methods in capturing single-cell level tissue complexity.

Main Methods:

Propose STCGAN, a cycle-consistent generative adversarial network (CGAN) for spatial transcriptomic data.
Utilize CGAN pre-training for robust latent representations and consistent data mapping.
Integrate single-cell RNA sequencing (scRNA-seq) with ST data using a trainable cell-to-spot mapping matrix.
Incorporate spatial-aware regularization to enhance cellular distribution reconstruction.

Main Results:

STCGAN accurately estimates cellular composition within spatial transcriptomic spots.
The method effectively reconstructs the spatial distribution of cells across tissues.
Benchmarking demonstrates superior cell-type deconvolution performance compared to seven state-of-the-art methods on diverse datasets.

Conclusions:

STCGAN provides a significant advancement in spatial transcriptomic data analysis.
The method enhances the understanding of tissue architecture and cellular heterogeneity.
STCGAN offers a robust solution for single-cell level deconvolution and spatial reconstruction.