ArticleEntropy (Basel, Switzerland)2020
Robust and Scalable Learning of Complex Intrinsic Dataset Geometry via ElPiGraph.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
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Who cites it
49 citing papers in PubMed.
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- Article
- Joint clinical and molecular subtyping of COPD with variational autoencoders.Nature communications · 2026Article
- Clonal embeddings allow exploratory analysis of lineage-resolved single-cell data.bioRxiv : the preprint server for biology · 2026Article
- Trajectory-based identification of cognitive-performance phenotypes across adulthood from psychophysiological testing.Frontiers in aging neuroscience · 2026Article
- Spatial transcriptomics reveal heterogeneous cell‒cell interactions among brain regions in cuprizone model consistent with multiple sclerosis lesions.Frontiers in bioinformatics · 2026Article
- Cell lineage tracing: Methods, applications, and challenges.Quantitative biology (Beijing, China) · 2025Review
- PHLOWER leverages single-cell multimodal data to infer complex, multi-branching cell differentiation trajectories.Nature methods · 2025Article
- Deciphering Complex Interactions Between LTR Retrotransposons and Three Papaver Species Using LTR_Stream.Genomics, proteomics & bioinformatics · 2025Article
- Systematic identification of oscillatory gene expression in single cell types.bioRxiv : the preprint server for biology · 2025Article
- Reply to Takefuji: Limitations of Linear Dimensional Reduction Methods in Chronic Obstructive Pulmonary Disease Phenotyping.American journal of respiratory and critical care medicine · 2025Article
- PILOT-GM-VAE: patient-level analysis of single-cell disease atlas with optimal transport of Gaussian mixture variational autoencoders.Briefings in bioinformatics · 2025Article
- Spatial proteomics of Alzheimer's disease-specific human microglial states.Nature immunology · 2025Article
- MCSPNature cancer · 2025Article
- Identification and Validation of New Molecular Subtypes within the Early and Late Mild Cognitive Impairment Stages of Alzheimer's Disease.medRxiv : the preprint server for health sciences · 2025Article
- Temporal Exploration of Chronic Obstructive Pulmonary Disease Phenotypes: Insights from the COPDGene and SPIROMICS Cohorts.American journal of respiratory and critical care medicine · 2025Observational
- Real-world clinical multi-omics analyses reveal bifurcation of ER-independent and ER-dependent drug resistance to CDK4/6 inhibitors.Nature communications · 2025Article
- A time-resolved single-cell roadmap of the logic driving anterior neural crest diversification from neural border to migration stages.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multidimensional datapoint clouds representing large datasets are frequently characterized by non-trivial low-dimensional geometry and topology which can be recovered by unsupervised machine learning approaches, in particular, by principal graphs. Principal graphs approximate the multivariate data by a graph injected into the data space with some constraints imposed on the node mapping. Here we present ElPiGraph, a scalable and robust method for constructing principal graphs. ElPiGraph exploits and further develops the concept of elastic energy, the topological graph grammar approach, and a gradient descent-like optimization of the graph topology. The method is able to withstand high levels of noise and is capable of approximating data point clouds via principal graph ensembles. This strategy can be used to estimate the statistical significance of complex data features and to summarize them into a single consensus principal graph. ElPiGraph deals efficiently with large datasets in various fields such as biology, where it can be used for example with single-cell transcriptomic or epigenomic datasets to infer gene expression dynamics and recover differentiation landscapes.
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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.