ArticleNPJ systems biology and applications2021
Sparsely-connected autoencoder (SCA) for single cell RNAseq data mining.
Article in NPJ systems biology and applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.Neuron · 2026Article
- Spatiomolecular mapping reveals anatomical organization of heterogeneous cell types in the human nucleus accumbens.bioRxiv : the preprint server for biology · 2025Article
- Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing.Burns & trauma · 2025Review
- Designing interpretable deep learning applications for functional genomics: a quantitative analysis.Briefings in bioinformatics · 2024Review
- NetActivity enhances transcriptional signals by combining gene expression into robust gene set activity scores through interpretable autoencoders.Nucleic acids research · 2024Article
- Advancing single-cell RNA-seq data analysis through the fusion of multi-layer perceptron and graph neural network.Briefings in bioinformatics · 2023Article
- Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.Nature communications · 2023Article
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- scCAN: single-cell clustering using autoencoder and network fusion.Scientific reports · 2022Article
- Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder.Journal of grid computing · 2022Article
- Sparsely Connected Autoencoders: A Multi-Purpose Tool for Single Cell omics Analysis.International journal of molecular sciences · 2021Article
- Laniakea@ReCaS: exploring the potential of customisable Galaxy on-demand instances as a cloud-based service.BMC bioinformatics · 2021Article
- Single-Cell Analysis Using Machine Learning Techniques and Its Application to Medical Research.Biomedicines · 2021Review
- A Survey of Autoencoder Algorithms to Pave the Diagnosis of Rare Diseases.International journal of molecular sciences · 2021Review
- MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning.International journal of molecular sciences · 2021Article
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Authors and funding
9 authors.
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Abstract
Single-cell RNA sequencing (scRNAseq) is an essential tool to investigate cellular heterogeneity. Thus, it would be of great interest being able to disclose biological information belonging to cell subpopulations, which can be defined by clustering analysis of scRNAseq data. In this manuscript, we report a tool that we developed for the functional mining of single cell clusters based on Sparsely-Connected Autoencoder (SCA). This tool allows uncovering hidden features associated with scRNAseq data. We implemented two new metrics, QCC (Quality Control of Cluster) and QCM (Quality Control of Model), which allow quantifying the ability of SCA to reconstruct valuable cell clusters and to evaluate the quality of the neural network achievements, respectively. Our data indicate that SCA encoded space, derived by different experimentally validated data (TF targets, miRNA targets, Kinase targets, and cancer-related immune signatures), can be used to grasp single cell cluster-specific functional features. In our implementation, SCA efficacy comes from its ability to reconstruct only specific clusters, thus indicating only those clusters where the SCA encoding space is a key element for cells aggregation. SCA analysis is implemented as module in rCASC framework and it is supported by a GUI to simplify it usage for biologists and medical personnel.
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