ArticleBMC bioinformatics2019
Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data.BMC bioinformatics · 2023Pooled it
- CSI-GEP: A GPU-based unsupervised machine learning approach for recovering gene expression programs in atlas-scale single-cell RNA-seq data.Cell genomics · 2025Article
- Article
- Interpretable deep learning in single-cell omics.Bioinformatics (Oxford, England) · 2024Review
- Model-based evaluation of spatiotemporal data reduction methods with unknown ground truth through optimal visualization and interpretability metrics.Briefings in bioinformatics · 2023Article
- NCAE: data-driven representations using a deep network-coherent DNA methylation autoencoder identify robust disease and risk factor signatures.Briefings in bioinformatics · 2023Article
- clusterMaker2: a major update to clusterMaker, a multi-algorithm clustering app for Cytoscape.BMC bioinformatics · 2023Article
- siVAE: interpretable deep generative models for single-cell transcriptomes.Genome biology · 2023Article
- Interpretable generative deep learning: an illustration with single cell gene expression data.Human genetics · 2022Review
- Large-Scale Integrative Analysis of Soybean Transcriptome Using an Unsupervised Autoencoder Model.Frontiers in plant science · 2022Article
- An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation.Current genomics · 2021Review
- Article
- Feature selection revisited in the single-cell era.Genome biology · 2021Review
- Improving gene function predictions using independent transcriptional components.Nature communications · 2021Article
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundUnsupervised machine learning methods (deep learning) have shown their usefulness with noisy single cell mRNA-sequencing data (scRNA-seq), where the models generalize well, despite the zero-inflation of the data. A class of neural networks, namely autoencoders, has been useful for denoising of single cell data, imputation of missing values and dimensionality reduction.
resultsHere, we present a striking feature with the potential to greatly increase the usability of autoencoders: With specialized training, the autoencoder is not only able to generalize over the data, but also to tease apart biologically meaningful modules, which we found encoded in the representation layer of the network. Our model can, from scRNA-seq data, delineate biological meaningful modules that govern a dataset, as well as give information as to which modules are active in each single cell. Importantly, most of these modules can be explained by known biological functions, as provided by the Hallmark gene sets.
conclusionsWe discover that tailored training of an autoencoder makes it possible to deconvolute biological modules inherent in the data, without any assumptions. By comparisons with gene signatures of canonical pathways we see that the modules are directly interpretable. The scope of this discovery has important implications, as it makes it possible to outline the drivers behind a given effect of a cell. In comparison with other dimensionality reduction methods, or supervised models for classification, our approach has the benefit of both handling well the zero-inflated nature of scRNA-seq, and validating that the model captures relevant information, by establishing a link between input and decoded data. In perspective, our model in combination with clustering methods is able to provide information about which subtype a given single cell belongs to, as well as which biological functions determine that membership.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.