ArticlePLoS computational biology2016
Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference.
Article in PLoS computational biology, 2016. 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 41 papers.
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Who cites it
41 citing papers in PubMed.
- Multiscale learning of gene network-driven phenotypic dynamics of single cells.Molecular systems biology · 2026Article
- Bayesian inference of haematopoietic stem/progenitor cell differentiation phenotypic manifolds and their bifurcation points using Gaussian processes and Gibbs sampling.Scientific reports · 2026Article
- Temporal network analysis in systems biology: concepts, inference, and validation.Frontiers in bioinformatics · 2026Review
- PseudotimeDE-fast: fast testing of differential gene expression along cell pseudotime.Bioinformatics (Oxford, England) · 2025Article
- Trajectory inference from single-cell genomics data with a process time model.PLoS computational biology · 2025Article
- scGRN-Entropy: Inferring cell differentiation trajectories using single-cell data and gene regulation network-based transfer entropy.PLoS computational biology · 2024Article
- Expansion effect of romiplostim on hematopoietic stem and progenitor cells versus thrombopoietin and eltrombopag.International journal of hematology · 2024Article
- Genetic association mapping leveraging Gaussian processes.Journal of human genetics · 2024Review
- Single-cell transcriptomics of peripheral blood mononuclear cells indicates impaired immune and inflammatory responses in alcohol-associated hepatitis.Human immunology · 2024Article
- RNA velocity unraveled.PLoS computational biology · 2022Article
- Spatio-temporal imaging of cell fate dynamics in single plant cells using luminescence microscope.Quantitative plant biology · 2022Article
- PseudotimeDE: inference of differential gene expression along cell pseudotime with well-calibrated p-values from single-cell RNA sequencing data.Genome biology · 2021Article
- Spatially Resolved Immunometabolism to Understand Infectious Disease Progression.Frontiers in microbiology · 2021Article
- Article
- GMM-Demux: sample demultiplexing, multiplet detection, experiment planning, and novel cell-type verification in single cell sequencing.Genome biology · 2020Article
- GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution.Bioinformatics (Oxford, England) · 2020Article
- Eleven grand challenges in single-cell data science.Genome biology · 2020Review
- Inferring TF activation order in time series scRNA-Seq studies.PLoS computational biology · 2020Article
- Continuous-state HMMs for modeling time-series single-cell RNA-Seq data.Bioinformatics (Oxford, England) · 2019Article
- A Bayesian model for single cell transcript expression analysis on MERFISH data.Bioinformatics (Oxford, England) · 2019Article
Corrections and comments
- Erratum issued
Authors and funding
2 authors.
Funding
Abstract
Single cell gene expression profiling can be used to quantify transcriptional dynamics in temporal processes, such as cell differentiation, using computational methods to label each cell with a 'pseudotime' where true time series experimentation is too difficult to perform. However, owing to the high variability in gene expression between individual cells, there is an inherent uncertainty in the precise temporal ordering of the cells. Pre-existing methods for pseudotime estimation have predominantly given point estimates precluding a rigorous analysis of the implications of uncertainty. We use probabilistic modelling techniques to quantify pseudotime uncertainty and propagate this into downstream differential expression analysis. We demonstrate that reliance on a point estimate of pseudotime can lead to inflated false discovery rates and that probabilistic approaches provide greater robustness and measures of the temporal resolution that can be obtained from pseudotime inference.
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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.