ArticleNature biotechnology2019
Visualizing structure and transitions in high-dimensional biological data.
Article in Nature biotechnology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 549 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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Who cites it
549 citing papers in PubMed, 1 synthesis or guideline pooled it, 1,114 citations in OpenAlex.
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- CardamomOT: A mechanistic optimal transport-based framework for gene regulatory network inference, trajectory reconstruction and generative modeling.PLoS computational biology · 2026Article
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
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- Scalable context-dependent single-cell eQTL mapping reveals disease-relevant regulatory variation beyond static models.medRxiv : the preprint server for health sciences · 2026Article
- A Human Accelerated Region Drives Opposing Heterochronic Changes in Craniofacial and Limb Development.bioRxiv : the preprint server for biology · 2026Article
- Bayesian inference of gene regulatory networks at stochastic steady state.Journal of the Royal Society, Interface · 2026Article
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- CRACD loss drives hypoxia-induced immune escape in gastric cancer.Nature communications · 2026Article
- ContiTE: continuous manifold MoE for few-shot cross-tissue mRNA translation efficiency prediction.Briefings in bioinformatics · 2026Article
- Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data.Nature biotechnology · 2026Article
- Sellar region neurocytomas exhibit a CIMP and neuroendocrine-like epigenetic signature distinct from other intra-axial neurocytomas.Acta neuropathologica · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- HMA-GCA: hybrid manifold augmentation and gated cross-attention for circRNA-miRNA interaction prediction.Bioinformatics (Oxford, England) · 2026Article
489 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
13 authors at 6 institutions in 3 countries.
Funding
Abstract
The high-dimensional data created by high-throughput technologies require visualization tools that reveal data structure and patterns in an intuitive form. We present PHATE, a visualization method that captures both local and global nonlinear structure using an information-geometric distance between data points. We compare PHATE to other tools on a variety of artificial and biological datasets, and find that it consistently preserves a range of patterns in data, including continual progressions, branches and clusters, better than other tools. We define a manifold preservation metric, which we call denoised embedding manifold preservation (DEMaP), and show that PHATE produces lower-dimensional embeddings that are quantitatively better denoised as compared to existing visualization methods. An analysis of a newly generated single-cell RNA sequencing dataset on human germ-layer differentiation demonstrates how PHATE reveals unique biological insight into the main developmental branches, including identification of three previously undescribed subpopulations. We also show that PHATE is applicable to a wide variety of data types, including mass cytometry, single-cell RNA sequencing, Hi-C and gut microbiome data.
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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.