ArticleBMC bioinformatics2013
Hierarchical Bayesian modelling of gene expression time series across irregularly sampled replicates and clusters.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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43 citing papers in PubMed.
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- Semi-supervised Bayesian integration of multiple spatial proteomics datasets.PLoS computational biology · 2025Article
- A supervised Bayesian method for time (re)annotation of transcriptomics data.NAR genomics and bioinformatics · 2025Article
- Mathematical modeling reveals cell differentiation processes and progenitor kinetics necessary for proper nephrogenesis.Frontiers in cell and developmental biology · 2025Article
- Bayesian Multi-Group Gaussian Process Models for Heterogeneous Group-Structured Data.Journal of machine learning research : JMLR · 2025Article
- GPMelt: A hierarchical Gaussian process framework to explore the dark meltome of thermal proteome profiling experiments.PLoS computational biology · 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
- Sex-biased gene expression across mammalian organ development and evolution.Science (New York, N.Y.) · 2023Article
- Principles and challenges of modeling temporal and spatial omics data.Nature methods · 2023Review
- Nonlinear expression patterns and multiple shifts in gene network interactions underlie robust phenotypic change in Drosophila melanogaster selected for night sleep duration.PLoS computational biology · 2023Article
- TempShift Reveals the Sequential Development of Human Neocortex and Skewed Developmental Timing of Down Syndrome Brains.Brain sciences · 2023Article
- Circadian regulation of pulmonary disease: the importance of timing.Clinical science (London, England : 1979) · 2023Review
- Semi-Supervised Non-Parametric Bayesian Modelling of Spatial Proteomics.The annals of applied statistics · 2022Article
- Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.Nature methods · 2022Article
- Hierarchical Gaussian Processes and Mixtures of Experts to Model COVID-19 Patient Trajectories.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2022Article
- Phenotypic selection during laboratory evolution of yeast populations leads to a genome-wide sustainable chromatin compaction shift.Frontiers in microbiology · 2022Article
- Immunolipidomics Reveals a Globoside Network During the Resolution of Pro-Inflammatory Response in Human Macrophages.Frontiers in immunology · 2022Article
- Non-parametric modelling of temporal and spatial counts data from RNA-seq experiments.Bioinformatics (Oxford, England) · 2021Article
- RVAgene: generative modeling of gene expression time series data.Bioinformatics (Oxford, England) · 2021Article
- Model-based aversive learning in humans is supported by preferential task state reactivation.Science advances · 2021Article
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundTime course data from microarrays and high-throughput sequencing experiments require simple, computationally efficient and powerful statistical models to extract meaningful biological signal, and for tasks such as data fusion and clustering. Existing methodologies fail to capture either the temporal or replicated nature of the experiments, and often impose constraints on the data collection process, such as regularly spaced samples, or similar sampling schema across replications.
resultsWe propose hierarchical Gaussian processes as a general model of gene expression time-series, with application to a variety of problems. In particular, we illustrate the method's capacity for missing data imputation, data fusion and clustering.The method can impute data which is missing both systematically and at random: in a hold-out test on real data, performance is significantly better than commonly used imputation methods. The method's ability to model inter- and intra-cluster variance leads to more biologically meaningful clusters. The approach removes the necessity for evenly spaced samples, an advantage illustrated on a developmental Drosophila dataset with irregular replications.
conclusionThe hierarchical Gaussian process model provides an excellent statistical basis for several gene-expression time-series tasks. It has only a few additional parameters over a regular GP, has negligible additional complexity, is easily implemented and can be integrated into several existing algorithms. Our experiments were implemented in python, and are available from the authors' website: http://staffwww.dcs.shef.ac.uk/people/J.Hensman/.
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