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Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
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Deep learning of gene relationships from single cell time-course expression data.

Ye Yuan1, Ziv Bar-Joseph2

  • 1Department of Automation, Shanghai Jiao Tong University, USA.

Briefings in Bioinformatics
|April 20, 2021
PubMed
Summary

We developed Time-course Deep Learning (TDL) models to analyze single-cell RNA sequencing (scRNA-Seq) data. TDL accurately predicts gene interactions and assigns gene functions from time-series scRNA-Seq datasets.

Keywords:
deep learningsingle cell RNA-Seqtime-course data

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Area of Science:

  • Computational Biology
  • Genomics
  • Systems Biology

Background:

  • Time-course gene expression data analysis is crucial for understanding gene regulatory and signaling networks.
  • Traditional methods are often limited to bulk expression data, lacking the resolution of single-cell approaches.
  • Single-cell RNA sequencing (scRNA-Seq) provides high-resolution temporal data but presents unique computational challenges.

Purpose of the Study:

  • To develop novel deep learning methods for predicting gene-gene interactions from time-course scRNA-Seq data.
  • To leverage a unique data encoding and neural network architecture for enhanced interaction prediction.
  • To demonstrate the utility of the developed method in identifying causal relationships and assigning novel gene functions.

Main Methods:

  • Developed a novel encoding strategy for time-course scRNA-Seq data.
  • Implemented deep learning models, specifically convolutional and recurrent neural networks, within a supervised framework.
  • Represented gene expression data as 3D tensors for input into the neural networks.
  • Trained and validated the Time-course Deep Learning (TDL) models on five diverse time-series scRNA-Seq datasets.

Main Results:

  • The TDL models accurately identified causal and regulatory gene-gene interactions.
  • TDL demonstrated efficacy in assigning putative functions to genes based on temporal expression patterns.
  • The method showed improved performance compared to existing approaches for interaction prediction and functional annotation.
  • Successfully applied TDL to multiple independent time-series scRNA-Seq datasets, confirming its generalizability.

Conclusions:

  • Time-course Deep Learning (TDL) offers a powerful and accurate approach for analyzing time-series scRNA-Seq data.
  • TDL enhances the discovery of gene regulatory networks and functional genomics insights.
  • The developed deep learning framework is broadly applicable to new time-series scRNA-Seq datasets, advancing the field of single-cell data analysis.