ArticleNature methods2019
Exploring single-cell data with deep multitasking neural networks.
Article in Nature methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
173 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Automated and reproducible cell identification in mass cytometry using neural networks.Briefings in bioinformatics · 2023Pooled it
- A meta-analysis of RNA-Seq studies to identify novel genes that regulate aging.Experimental gerontology · 2023Pooled it
- Systematic Evaluation of Competing Brain Transcriptomic Representations Reveals Reciprocal Patterns Across Heterogeneous Contexts.International journal of molecular sciences · 2026Article
- ProtoCloud: A prototypical self-explaining model for single-cell analysis.Cell genomics · 2026Article
- scMEDAL: interpretable single-cell transcriptomics analysis with batch effect visualization via deep mixed-effects autoencoder.Nature communications · 2026Article
- Multiscale hyperbolic embedding reveals hierarchical structure in complex biological systems.NPJ systems biology and applications · 2026Article
- Supervised machine learning identifies impaired mitochondrial quality control in β-cells with development of type 2 diabetes.NPJ systems biology and applications · 2026Article
- Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.Nature communications · 2026Article
- stSCI: A multi-task learning framework for integrative analysis of single-cell and spatial transcriptomics data.Innovation (Cambridge (Mass.)) · 2026Article
- Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning.Science translational medicine · 2026Article
- AI-Based Prediction of Gene Expression in Single-Cell and Multiscale Genomics and Transcriptomics.International journal of molecular sciences · 2026Review
- scLTF: A self-training clustering framework for large-scale single-cell RNA-Seq data.PloS one · 2026Article
- Artificial Intelligence Revolution in Transcriptomics: From Single Cells to Spatial Atlases.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- CellScope: high-performance cell atlas workflow with tree-structured representation.Nature communications · 2025Article
- Single-cell and spatial detection of senescent cells using DeepScence.Cell genomics · 2025Article
- MAGNET: Multi-view graph autoencoder with cell-gene attention for cell interaction network reconstruction from spatial transcriptomics.PLoS computational biology · 2025Article
- Supervised machine learning identifies impaired mitochondrial quality control in β cells with development of type 2 diabetes.bioRxiv : the preprint server for biology · 2025Article
- AAnet Resolves a Continuum of Spatially Localized Cell States to Unveil Intratumoral Heterogeneity.Cancer discovery · 2025Article
- Multiscale Hyperbolic Embedding for Cell Hierarchies in Large-Scale Bioinformatics Data.bioRxiv : the preprint server for biology · 2025Article
- Advancing biological understanding of cellular senescence with computational multiomics.Nature genetics · 2025Review
113 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
It is currently challenging to analyze single-cell data consisting of many cells and samples, and to address variations arising from batch effects and different sample preparations. For this purpose, we present SAUCIE, a deep neural network that combines parallelization and scalability offered by neural networks, with the deep representation of data that can be learned by them to perform many single-cell data analysis tasks. Our regularizations (penalties) render features learned in hidden layers of the neural network interpretable. On large, multi-patient datasets, SAUCIE's various hidden layers contain denoised and batch-corrected data, a low-dimensional visualization and unsupervised clustering, as well as other information that can be used to explore the data. We analyze a 180-sample dataset consisting of 11 million T cells from dengue patients in India, measured with mass cytometry. SAUCIE can batch correct and identify cluster-based signatures of acute dengue infection and create a patient manifold, stratifying immune response to dengue.
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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.