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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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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

scRGCL: a cell type annotation method for single-cell RNA-seq data using residual graph convolutional neural network

Lin Yuan^1,2,3, Shengguo Sun^1,2,3, Yufeng Jiang^1,2,3

¹Key Laboratory of Computing Power Network and Information Security, Ministry of Education, Shandong Computer Science Center, Qilu University of Technology (Shandong Academy of Sciences), 3501 Daxue Road, 250353, Shandong, China.

Briefings in Bioinformatics

|December 21, 2024

Summary

This study introduces scRGCL, a novel deep learning model for cell type annotation in single-cell RNA sequencing data, outperforming existing methods by effectively utilizing differential and high-order gene expression features.

Keywords:

cell type annotation contrastive learning residual graph neural network scRNA-seq weight freezing

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Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Isolation of Adult Spinal Cord Nuclei for Massively Parallel Single-nucleus RNA Sequencing

Published on: October 12, 2018

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Cell type annotation is crucial for single-cell RNA sequencing (scRNA-seq) data analysis.
Existing deep learning (DL) methods have limitations, including underutilization of cell-to-cell differential features, reliance on shallow features, and potential overfitting due to low-dimensional gene expression data.

Purpose of the Study:

To develop a novel deep learning-based model, scRGCL, to overcome the limitations of existing cell type annotation methods for scRNA-seq data.
To enhance the extraction of complex, high-order, and differential gene expression features for more accurate cell type identification.

Main Methods:

Proposed scRGCL model integrating residual graph convolutional neural network (RGCN) and contrastive learning.
RGCN is employed to extract complex high-order features from scRNA-seq data.
Contrastive learning is utilized to learn meaningful cell-to-cell differential features, while weight freezing prevents overfitting and highlights gene expression impacts.

Main Results:

scRGCL demonstrated superior performance compared to six other methods, including shallow learning algorithms and state-of-the-art DL approaches.
The model was validated on eight diverse single-cell benchmark datasets from human and mouse species.
Experimental results confirmed the effectiveness and generalizability of scRGCL for cell type annotation.

Conclusions:

scRGCL effectively addresses limitations in current DL-based cell type annotation methods for scRNA-seq data.
The model's architecture, combining RGCN and contrastive learning, enables robust extraction of critical gene expression features.
scRGCL offers a powerful and generalizable solution for accurate cell type annotation in single-cell genomics research.