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Related Experiment Video

Updated: Jul 28, 2025

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

Consensus label propagation with graph convolutional networks for single-cell RNA sequencing cell type annotation.

Daniel P Lewinsohn^1,2, Katinka A Vigh-Conrad¹, Donald F Conrad¹

¹Division of Genetics, Oregon National Primate Research Center, Oregon Health and Science University, Beaverton, OR 97006, United States.

Bioinformatics (Oxford, England)

|June 2, 2023

Summary

This summary is machine-generated.

We developed a Graph Convolutional Network (GCN) to automate cell type annotation for single-cell RNA sequencing (scRNA-seq) data. This method enhances accuracy and provides feature interpretation for cell classification.

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Last Updated: Jul 28, 2025

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

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Published on: May 22, 2017

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Single-cell RNA Sequencing of Fluorescently Labeled Mouse Neurons Using Manual Sorting and Double In Vitro Transcription with Absolute Counts Sequencing DIVA-Seq

Published on: October 26, 2018

Area of Science:

Genomics
Bioinformatics
Computational Biology

Background:

Single-cell RNA sequencing (scRNA-seq) enables gene expression profiling at the cellular level.
Manual cell type annotation is labor-intensive and prone to subjectivity.

Purpose of the Study:

To develop an automated and interpretable method for cell type annotation of scRNA-seq data.
To improve the accuracy and efficiency of cell type classification.

Main Methods:

Utilized a Graph Convolutional Network (GCN) for semi-supervised learning.
Integrated consensus labeling from state-of-the-art tools to identify confident cell labels.
Applied the GCN to spread confident labels across the cell graph.

Main Results:

The GCN-based approach demonstrated improved accuracy compared to consensus algorithms and individual tools.
Achieved comparable results to a nonparametric neighbor majority approach.
Enabled feature interpretation by identifying key genes for cell type classification.

Conclusions:

The developed GCN pipeline provides an end-to-end solution for automated and interpretable scRNA-seq data classification.
This method offers a robust and efficient alternative to manual annotation.