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RNA-seq03:21

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 microarray-based...

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Related Experiment Video

Updated: May 27, 2026

Multiplexed Analysis of Retinal Gene Expression and Chromatin Accessibility Using scRNA-Seq and scATAC-Seq
06:24

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

Published on: March 12, 2021

scE2EGAE: enhancing single-cell RNA-Seq data analysis through an end-to-end cell-graph-learnable graph autoencoder

Shuo Wang1,2, Yuanning Liu3,4, Hao Zhang1,2

  • 1College of Computer Science and Technology, Jilin University, 2699 Qianjin Street, Changchun, 130012, Jilin, China.

Biology Direct
|May 28, 2025
PubMed
Summary

scE2EGAE dynamically learns cell graphs for single-cell RNA sequencing (scRNA-Seq) data analysis, improving denoising and downstream tasks. This graph neural network (GNN) approach enhances biological insights from scRNA-Seq data.

Keywords:
AutoencoderBioinformaticsDeep learningEnd-to-endGraph neural networksSingle-cell RNA-Seq

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Last Updated: May 27, 2026

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06:24

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Published on: March 12, 2021

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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
11:26

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

Published on: May 22, 2017

Area of Science:

  • Computational Biology
  • Genomics
  • Bioinformatics

Background:

  • Single-cell RNA sequencing (scRNA-Seq) provides high-resolution genomic insights but requires advanced computational analysis.
  • Graph neural networks (GNNs) are increasingly used for scRNA-Seq data analysis, yet fixed graph construction methods can lead to information loss.

Purpose of the Study:

  • To introduce scE2EGAE, a novel method that learns cell graphs dynamically during training to overcome limitations of fixed graph construction in scRNA-Seq analysis.
  • To enhance the accuracy and biological relevance of scRNA-Seq data analysis, particularly for denoising and downstream tasks.

Main Methods:

  • scE2EGAE utilizes a deep count autoencoder (DCA) to process scRNA-Seq data.
  • Cell-to-cell graph edges are generated using a straight-through estimator (STE) with top-k sampling and Gumbel-Softmax, enabling dynamic graph learning.
  • The learned graph and original data are input into GNNs for downstream tasks, exemplified here by a graph autoencoder for denoising.

Main Results:

  • scE2EGAE was evaluated on eight scRNA-Seq datasets, demonstrating superior denoising performance compared to seven existing methods.
  • Performance was assessed using metrics for denoising (MAE, PCC, cosine similarity), clustering (ARI, NMI, silhouette score), and trajectory inference (pseudo-temporal ordering score).
  • The results confirm scE2EGAE's ability to construct informative cell-to-cell graphs, capturing true inter-cellular relationships.

Conclusions:

  • The scE2EGAE method effectively learns inter-cellular relationships and constructs dynamic cell-to-cell graphs for scRNA-Seq data.
  • This dynamic graph learning approach enhances downstream analyses, including data denoising.
  • scE2EGAE offers a promising framework for future GNN-based scRNA-Seq analysis methods with broad application potential.