ArticleCell systems2018
Generalizable and Scalable Visualization of Single-Cell Data Using Neural Networks.
Article in Cell systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Meta-analysis of Caenorhabditis elegans single-cell developmental data reveals multi-frequency oscillation in gene activation.Bioinformatics (Oxford, England) · 2020Pooled it
- Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.Briefings in bioinformatics · 2025Article
- Infusing structural assumptions into dimensionality reduction for single-cell RNA sequencing data to identify small gene sets.Communications biology · 2025Article
- Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications.Bioengineering (Basel, Switzerland) · 2025Review
- Single-cell omics: experimental workflow, data analyses and applications.Science China. Life sciences · 2025Review
- Metal-detection based techniques and their applications in metallobiology.Chemical science · 2024Review
- Selective loss of CD107a TIGIT+ memory HIV-1-specific CD8+ T cells in PLWH over a decade of ART.eLife · 2023Article
- Application of Deep Learning on Single-cell RNA Sequencing Data Analysis: A Review.Genomics, proteomics & bioinformatics · 2022Review
- Manifold alignment for heterogeneous single-cell multi-omics data integration using Pamona.Bioinformatics (Oxford, England) · 2021Article
- D-EE: Distributed software for visualizing intrinsic structure of large-scale single-cell data.GigaScience · 2020Article
- Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq.Nature methods · 2020Article
- Gaussian Embedding for Large-scale Gene Set Analysis.Nature machine intelligence · 2020Article
- Immune monitoring using mass cytometry and related high-dimensional imaging approaches.Nature reviews. Rheumatology · 2020Review
- NeuralEE: A GPU-Accelerated Elastic Embedding Dimensionality Reduction Method for Visualizing Large-Scale scRNA-Seq Data.Frontiers in genetics · 2020Article
- Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets.Nature communications · 2019Article
- The art of using t-SNE for single-cell transcriptomics.Nature communications · 2019Article
- Opportunities for improving cancer treatment using systems biology.Current opinion in systems biology · 2019Article
- Estimation of immune cell content in tumor using single-cell RNA-seq reference data.BMC cancer · 2019Article
- Deconvolution of autoencoders to learn biological regulatory modules from single cell mRNA sequencing data.BMC bioinformatics · 2019Article
- Deep learning: new computational modelling techniques for genomics.Nature reviews. Genetics · 2019Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Visualization algorithms are fundamental tools for interpreting single-cell data. However, standard methods, such as t-stochastic neighbor embedding (t-SNE), are not scalable to datasets with millions of cells and the resulting visualizations cannot be generalized to analyze new datasets. Here we introduce net-SNE, a generalizable visualization approach that trains a neural network to learn a mapping function from high-dimensional single-cell gene-expression profiles to a low-dimensional visualization. We benchmark net-SNE on 13 different datasets, and show that it achieves visualization quality and clustering accuracy comparable with t-SNE. Additionally we show that the mapping function learned by net-SNE can accurately position entire new subtypes of cells from previously unseen datasets and can also be used to reduce the runtime of visualizing 1.3 million cells by 36-fold (from 1.5 days to an hour). Our work provides a framework for bootstrapping single-cell analysis from existing datasets.
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What OpenQuestion holds
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.