ArticleGenome biology2021
MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks.
Article in Genome biology, 2021. 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.
- A transcription factor regulatory atlas for activity inference and perturbation prediction.Nucleic acids research · 2026Article
- Interpretation, extrapolation and perturbation of single cells.Nature reviews. Genetics · 2026Review
- iAODE for benchmarking and continuum modeling of single-cell chromatin accessibility.Communications biology · 2026Article
- Unveiling patterns: an exploration of machine learning techniques for unsupervised feature selection in single-cell data.Briefings in bioinformatics · 2026Review
- A mixture of attention experts-embedded flow-based generative model to create synthetic cells in single-cell RNA-Seq datasets.PLoS computational biology · 2025Article
- PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations.Molecular systems biology · 2025Article
- Causal disentanglement for single-cell representations and controllable counterfactual generation.Nature communications · 2025Article
- Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.Genomics, proteomics & bioinformatics · 2025Review
- CoupleVAE: coupled variational autoencoders for predicting perturbational single-cell RNA sequencing data.Briefings in bioinformatics · 2025Article
- BuDDI: Bulk Deconvolution with Domain Invariance to predict cell-type-specific perturbations from bulk.PLoS computational biology · 2025Article
- Disentanglement of single-cell data with biolord.Nature biotechnology · 2024Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- BuDDI:bioRxiv : the preprint server for biology · 2024Article
- Homogeneous Space Construction and Projection for Single-Cell Expression Prediction Based on Deep Learning.Bioengineering (Basel, Switzerland) · 2023Article
- In-silico generation of high-dimensional immune response data in patients using a deep neural network.Cytometry. Part A : the journal of the International Society for Analytical Cytology · 2023Article
- Biologically informed deep learning to query gene programs in single-cell atlases.Nature cell biology · 2023Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- Interpretable generative deep learning: an illustration with single cell gene expression data.Human genetics · 2022Review
- Generative Adversarial Networks for Creating Synthetic Nucleic Acid Sequences of Cat Genome.International journal of molecular sciences · 2022Article
- MichiGAN: sampling from disentangled representations of single-cell data using generative adversarial networks.Genome biology · 2021Article
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Authors and funding
2 authors.
Funding
Abstract
Deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs) generate and manipulate high-dimensional images. We systematically assess the complementary strengths and weaknesses of these models on single-cell gene expression data. We also develop MichiGAN, a novel neural network that combines the strengths of VAEs and GANs to sample from disentangled representations without sacrificing data generation quality. We learn disentangled representations of three large single-cell RNA-seq datasets and use MichiGAN to sample from these representations. MichiGAN allows us to manipulate semantically distinct aspects of cellular identity and predict single-cell gene expression response to drug treatment.
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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.