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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 22, 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

Comprehensive single-cell RNA-seq analysis using deep interpretable generative modeling guided by biological

Hegang Chen¹, Yuyin Lu¹, Zhiming Dai¹

¹School of Computer Science and Engineering, Sun Yat-sen University, 132 Waihuan East Road, Guangzhou University Town, 510006, Guangzhou, China.

Briefings in Bioinformatics

|July 3, 2024

Summary

This summary is machine-generated.

We introduce d-scIGM, a deep interpretable generative model for single-cell transcriptomic data. It enhances biological explanation and analysis of cellular heterogeneity, outperforming existing methods.

Keywords:

combining hierarchical prior knowledge deep generative model deep learning for single-cell data interpretable neural networks

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Last Updated: Jun 22, 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:

Computational Biology
Genomics
Machine Learning

Background:

Single-cell technologies enable exploration of cellular heterogeneity.
Deep learning, particularly generative models, has advanced transcriptomic data analysis.
Existing generative models often lack interpretability and depth, limiting biological insights.

Purpose of the Study:

To develop a deep interpretable generative model (d-scIGM) for enhanced single-cell data analysis.
To improve the biological interpretability and analytical capabilities beyond existing shallow models.
To apply d-scIGM to diverse tasks like clustering, visualization, and drug response analysis.

Main Methods:

Developed d-scIGM, a deep generative framework using sawtooth connectivity and residual networks.
Incorporated hierarchical biological domain prior knowledge for enhanced interpretability.
Evaluated performance on clustering, visualization, pseudo-temporal inference, and drug response data.

Main Results:

d-scIGM demonstrated superior performance in clustering, visualization, and pseudo-temporal inference.
Learned topics from d-scIGM were significantly enriched for biologically meaningful pathways.
Successfully captured drug response patterns and identified key genes/pathways in a melanoma dataset.

Conclusions:

d-scIGM offers a powerful and interpretable approach for single-cell data analysis.
The model facilitates deeper understanding of cellular heterogeneity and biological mechanisms.
d-scIGM shows promise for drug development and disease mechanism elucidation.