ArticlePloS one2015
A Linear Mixed Model Spline Framework for Analysing Time Course 'Omics' Data.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Multi-omics integration and artificial intelligence for the conservation and utilization of local chicken genetic resources.Poultry science · 2026Review
- The molecular ICU: a primer on omics, informatics and the future of precision critical care.Critical care (London, England) · 2026Review
- Shape-constrained, changepoint additive models for time series omics data with cpam.Nucleic acids research · 2026Article
- Best Practices for Developing Linear Models With Multiple Explanatory Variables.Advanced genetics (Hoboken, N.J.) · 2026Article
- Multi-omic and computational approaches for biomarker discovery and clinical translation in pediatric sepsis.Frontiers in pharmacology · 2026Review
- Longitudinal omics data analysis: approaches and applications.Computational and structural biotechnology journal · 2026Review
- Site- and cell-type-specific miRNA and mRNA genes and networks across the cortex, striatum, and hypothalamus.Communications biology · 2025Article
- Peripheral blood gene expression stratifies rate of progression to type 1 diabetes in autoantibody-positive children in the TEDDY study.Frontiers in immunology · 2025Article
- Article
- Mathematical Modeling and Inference of Epidermal Growth Factor-Induced Mitogen-Activated Protein Kinase Cell Signaling Pathways.International journal of molecular sciences · 2024Review
- Non-invasive VOCs detection to monitor the gut microbiota metabolism in-vitro.Scientific reports · 2024Article
- A population-based urinary and plasma metabolomics study of environmental exposure to cadmium.Environmental health and preventive medicine · 2024Article
- Serial Sampling of the Small Airway Epithelium to Identify Persistent Smoking-dysregulated Genes.American journal of respiratory and critical care medicine · 2023Article
- Statistical Detection of Differentially Abundant Proteins in Experiments with Repeated Measures Designs and Isobaric Labeling.Journal of proteome research · 2023Article
- Phosphoproteomics data-driven signalling network inference: Does it work?Computational and structural biotechnology journal · 2023Article
- Benchmarking tools for detecting longitudinal differential expression in proteomics data allows establishing a robust reproducibility optimization regression approach.Nature communications · 2022Article
- Statistical challenges in longitudinal microbiome data analysis.Briefings in bioinformatics · 2022Review
- Alterations in the Gut Fungal Community in a Mouse Model of Huntington's Disease.Microbiology spectrum · 2022Article
- Interpretation of network-based integration from multi-omics longitudinal data.Nucleic acids research · 2022Article
- Identifying temporal and spatial patterns of variation from multimodal data using MEFISTO.Nature methods · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Time course 'omics' experiments are becoming increasingly important to study system-wide dynamic regulation. Despite their high information content, analysis remains challenging. 'Omics' technologies capture quantitative measurements on tens of thousands of molecules. Therefore, in a time course 'omics' experiment molecules are measured for multiple subjects over multiple time points. This results in a large, high-dimensional dataset, which requires computationally efficient approaches for statistical analysis. Moreover, methods need to be able to handle missing values and various levels of noise. We present a novel, robust and powerful framework to analyze time course 'omics' data that consists of three stages: quality assessment and filtering, profile modelling, and analysis. The first step consists of removing molecules for which expression or abundance is highly variable over time. The second step models each molecular expression profile in a linear mixed model framework which takes into account subject-specific variability. The best model is selected through a serial model selection approach and results in dimension reduction of the time course data. The final step includes two types of analysis of the modelled trajectories, namely, clustering analysis to identify groups of correlated profiles over time, and differential expression analysis to identify profiles which differ over time and/or between treatment groups. Through simulation studies we demonstrate the high sensitivity and specificity of our approach for differential expression analysis. We then illustrate how our framework can bring novel insights on two time course 'omics' studies in breast cancer and kidney rejection. The methods are publicly available, implemented in the R CRAN package lmms.
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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.