ArticleBioinformatics (Oxford, England)2019
GrandPrix: scaling up the Bayesian GPLVM for single-cell data.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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
29 citing papers in PubMed.
- Bayesian inference of haematopoietic stem/progenitor cell differentiation phenotypic manifolds and their bifurcation points using Gaussian processes and Gibbs sampling.Scientific reports · 2026Article
- Navigating the data processing for cytometry-based single-cell proteomics.Nature protocols · 2026Review
- Gene-level alignment of single-cell trajectories.Nature methods · 2025Article
- Genetic association mapping leveraging Gaussian processes.Journal of human genetics · 2024Review
- The CD8Immunity · 2024Article
- A dynamical perspective: moving towards mechanism in single-cell transcriptomics.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2024Review
- Oscillatory differentiation dynamics fundamentally restricts the resolution of pseudotime reconstruction algorithms.Journal of the Royal Society, Interface · 2024Article
- The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data.Bioinformatics (Oxford, England) · 2023Article
- Methods and Insights from Single-Cell Expression Quantitative Trait Loci.Annual review of genomics and human genetics · 2023Review
- Mapping interindividual dynamics of innate immune response at single-cell resolution.Nature genetics · 2023Article
- ANPELA: Significantly Enhanced Quantification Tool for Cytometry-Based Single-Cell Proteomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination.Computational and structural biotechnology journal · 2023Review
- RNA velocity unraveled.PLoS computational biology · 2022Article
- Panoramic Manifold Projection (Panoramap) for Single-Cell Data Dimensionality Reduction and Visualization.International journal of molecular sciences · 2022Article
- An integrated atlas of human placental development delineates essential regulators of trophoblast stem cells.Development (Cambridge, England) · 2022Article
- Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.Bioinformatics (Oxford, England) · 2021Article
- Article
- Latent representation learning in biology and translational medicine.Patterns (New York, N.Y.) · 2021Review
- The circadian oscillator analysed at the single-transcript level.Molecular systems biology · 2021Article
- Toward uncharted territory of cellular heterogeneity: advances and applications of single-cell RNA-seq.Journal of translational genetics and genomics · 2021Article
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
Motivation: The Gaussian Process Latent Variable Model (GPLVM) is a popular approach for dimensionality reduction of single-cell data and has been used for pseudotime estimation with capture time information. However, current implementations are computationally intensive and will not scale up to modern droplet-based single-cell datasets which routinely profile many tens of thousands of cells. Results: We provide an efficient implementation which allows scaling up this approach to modern single-cell datasets. We also generalize the application of pseudotime inference to cases where there are other sources of variation such as branching dynamics. We apply our method on microarray, nCounter, RNA-seq, qPCR and droplet-based datasets from different organisms. The model converges an order of magnitude faster compared to existing methods whilst achieving similar levels of estimation accuracy. Further, we demonstrate the flexibility of our approach by extending the model to higher-dimensional latent spaces that can be used to simultaneously infer pseudotime and other structure such as branching. Thus, the model has the capability of producing meaningful biological insights about cell ordering as well as cell fate regulation. Availability and implementation: Software available at github.com/ManchesterBioinference/GrandPrix. Supplementary information: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
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.