ArticleNucleic acids research2017
Network embedding-based representation learning for single cell RNA-seq data.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Navigating single-cell RNA-sequencing: protocols, tools, databases, and applications.Genomics & informatics · 2025Review
- Robust evaluation of deep learning-based representation methods for survival and gene essentiality prediction on bulk RNA-seq data.Scientific reports · 2024Article
- MENDER: fast and scalable tissue structure identification in spatial omics data.Nature communications · 2024Article
- stMMR: accurate and robust spatial domain identification from spatially resolved transcriptomics with multimodal feature representation.GigaScience · 2024Article
- Research progress of single-cell sequencing in tuberculosis.Frontiers in immunology · 2023Review
- Gene network inference from single-cell omics data and domain knowledge for constructing COVID-19-specificFrontiers in genetics · 2023Article
- Cell-specific gene association network construction from single-cell RNA sequence.Cell cycle (Georgetown, Tex.) · 2021Article
- Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data.Bioinformatics (Oxford, England) · 2021Article
- Single-cell Transcriptomes Reveal Characteristics of MicroRNAs in Gene Expression Noise Reduction.Genomics, proteomics & bioinformatics · 2021Article
- An overview of graph databases and their applications in the biomedical domain.Database : the journal of biological databases and curation · 2021Article
- c-CSN: Single-cell RNA Sequencing Data Analysis by Conditional Cell-specific Network.Genomics, proteomics & bioinformatics · 2021Article
- Survey on graph embeddings and their applications to machine learning problems on graphs.PeerJ. Computer science · 2021Article
- Reconstructing gene regulatory networks in single-cell transcriptomic data analysis.Zoological research · 2020Review
- Clustering and classification methods for single-cell RNA-sequencing data.Briefings in bioinformatics · 2020Review
- Probing infectious disease by single-cell RNA sequencing: Progresses and perspectives.Computational and structural biotechnology journal · 2020Review
- Constructing knowledge graphs and their biomedical applications.Computational and structural biotechnology journal · 2020Review
- Combining gene ontology with deep neural networks to enhance the clustering of single cell RNA-Seq data.BMC bioinformatics · 2019Article
- Towards rapid prediction of drug-resistant cancer cell phenotypes: single cell mass spectrometry combined with machine learning.Chemical communications (Cambridge, England) · 2019Article
- Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity.Frontiers in genetics · 2019Article
- Topological-Similarity Based Canonical Representations for Biological Link Prediction.IEEE transactions on computational biology and bioinformaticsArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Single cell RNA-seq (scRNA-seq) techniques can reveal valuable insights of cell-to-cell heterogeneities. Projection of high-dimensional data into a low-dimensional subspace is a powerful strategy in general for mining such big data. However, scRNA-seq suffers from higher noise and lower coverage than traditional bulk RNA-seq, hence bringing in new computational difficulties. One major challenge is how to deal with the frequent drop-out events. The events, usually caused by the stochastic burst effect in gene transcription and the technical failure of RNA transcript capture, often render traditional dimension reduction methods work inefficiently. To overcome this problem, we have developed a novel Single Cell Representation Learning (SCRL) method based on network embedding. This method can efficiently implement data-driven non-linear projection and incorporate prior biological knowledge (such as pathway information) to learn more meaningful low-dimensional representations for both cells and genes. Benchmark results show that SCRL outperforms other dimensional reduction methods on several recent scRNA-seq datasets.
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