ArticleBioinformatics (Oxford, England)2016
Pseudotime estimation: deconfounding single cell time series.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 76 papers.
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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.
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Who cites it
76 citing papers in PubMed, 138 citations in OpenAlex.
- Single-cell spatial mapping of human kidney development implicates the microenvironment in guiding cell fate decisions.Nature genetics · 2026Article
- Bayesian inference of haematopoietic stem/progenitor cell differentiation phenotypic manifolds and their bifurcation points using Gaussian processes and Gibbs sampling.Scientific reports · 2026Article
- Computational Approaches to Cancer Cell Dormancy: From Detection to Dynamic Modelling.Biomolecules · 2026Review
- SHISMA: SHape-driven inference of significant cell type-specific subnetworks from tiMe series single-cell trAnscriptomics.Bioinformatics advances · 2026Article
- A supervised Bayesian method for time (re)annotation of transcriptomics data.NAR genomics and bioinformatics · 2025Article
- Shedding Light on Patterns of Unconventional Expression of Opsin Genes in Hydra vulgaris.Integrative and comparative biology · 2025Article
- Resolving floral development dynamics using genome and single-cell temporal transcriptome of Dendrobium devonianum.Plant biotechnology journal · 2025Article
- Development of a prognostic model for osteosarcoma based on macrophage polarization-related genes using machine learning: implications for personalized therapy.Clinical and experimental medicine · 2025Article
- Lymphotoxin-β promotes breast cancer bone metastasis colonization and osteolytic outgrowth.Nature cell biology · 2024Article
- Comprehensive single-cell transcriptomic profiling reveals molecular subtypes and prognostic biomarkers with implications for targeted therapy in esophageal squamous cell carcinoma.Translational oncology · 2024Article
- Bayesian profile regression for clustering analysis involving a longitudinal response and explanatory variables.Methodology : European journal of research methods for the behavioral & social sciences · 2024Article
- Oscillatory differentiation dynamics fundamentally restricts the resolution of pseudotime reconstruction algorithms.Journal of the Royal Society, Interface · 2024Article
- Systems immunology spanning tumors, lymph nodes, and periphery.Cell reports methods · 2023Review
- Methods and Insights from Single-Cell Expression Quantitative Trait Loci.Annual review of genomics and human genetics · 2023Review
- LVPT: Lazy Velocity Pseudotime Inference Method.Biomolecules · 2023Article
- Transcriptomic forecasting with neural ordinary differential equations.Patterns (New York, N.Y.) · 2023Article
- Gene knockout inference with variational graph autoencoder learning single-cell gene regulatory networks.Nucleic acids research · 2023Article
- 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
- Integrating genetics and transcriptomics to study major depressive disorder: a conceptual framework, bioinformatic approaches, and recent findings.Translational psychiatry · 2023Review
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
motivationRepeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression.
resultsWe present a principled probabilistic model with a Bayesian inference scheme to analyse such data. We demonstrate our method's utility on public microarray, nCounter and RNA-seq datasets from three organisms. Our method almost perfectly recovers withheld capture times in an Arabidopsis dataset, it accurately estimates cell cycle peak times in a human prostate cancer cell line and it correctly identifies two precocious cells in a study of paracrine signalling in mouse dendritic cells. Furthermore, our method compares favourably with Monocle, a state-of-the-art technique. We also show using held-out data that uncertainty in the temporal dimension is a common confounder and should be accounted for in analyses of repeated cross-sectional time series. AVAILABILITY AND IMPLEMENTATION: Our method is available on CRAN in the DeLorean package. CONTACT: john.reid@mrc-bsu.cam.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.