ArticleBMC bioinformatics2007
Robust regression for periodicity detection in non-uniformly sampled time-course gene expression data.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed, 56 citations in OpenAlex.
- Article
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- Differential network connectivity analysis for microbiome data adjusted for clinical covariates using jackknife pseudo-values.BMC bioinformatics · 2024Article
- Chronic sleep loss disrupts rhythmic gene expression inFrontiers in physiology · 2022Article
- Assessing Granger-Causality in the Southern Humboldt Current Ecosystem Using Cross-Spectral Methods.Entropy (Basel, Switzerland) · 2020Article
- Building blocks are synthesized on demand during the yeast cell cycle.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- On detection of periodicity in C-reactive protein (CRP) levels.Scientific reports · 2018Article
- Temporal probabilistic modeling of bacterial compositions derived from 16S rRNA sequencing.Bioinformatics (Oxford, England) · 2018Article
- TTCA: an R package for the identification of differentially expressed genes in time course microarray data.BMC bioinformatics · 2017Article
- On the health paradox of occupational and leisure-time physical activity using objective measurements: Effects on autonomic imbalance.PloS one · 2017Article
- Prolonged Sitting is Associated with Attenuated Heart Rate Variability during Sleep in Blue-Collar Workers.International journal of environmental research and public health · 2015Article
- Metabolome progression during early gut microbial colonization of gnotobiotic mice.Scientific reports · 2015Article
- Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years.PloS one · 2015Article
- Mouse hair cycle expression dynamics modeled as coupled mesenchymal and epithelial oscillators.PLoS computational biology · 2014Article
- Heart rate variability analysis using robust period detection.Biomedical engineering online · 2014Article
- Cosinor-based rhythmometry.Theoretical biology & medical modelling · 2014Review
- The analytical landscape of static and temporal dynamics in transcriptome data.Frontiers in genetics · 2014Review
- The transcriptional and gene regulatory network of Lactococcus lactis MG1363 during growth in milk.PloS one · 2013Article
- Spectral analysis on time-course expression data: detecting periodic genes using a real-valued iterative adaptive approach.Advances in bioinformatics · 2013Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundIn practice many biological time series measurements, including gene microarrays, are conducted at time points that seem to be interesting in the biologist's opinion and not necessarily at fixed time intervals. In many circumstances we are interested in finding targets that are expressed periodically. To tackle the problems of uneven sampling and unknown type of noise in periodicity detection, we propose to use robust regression.
methodsThe aim of this paper is to develop a general framework for robust periodicity detection and review and rank different approaches by means of simulations. We also show the results for some real measurement data.
resultsThe simulation results clearly show that when the sampling of time series gets more and more uneven, the methods that assume even sampling become unusable. We find that M-estimation provides a good compromise between robustness and computational efficiency.
conclusionSince uneven sampling occurs often in biological measurements, the robust methods developed in this paper are expected to have many uses. The regression based formulation of the periodicity detection problem easily adapts to non-uniform sampling. Using robust regression helps to reject inconsistently behaving data points. AVAILABILITY: The implementations are currently available for Matlab and will be made available for the users of R as well. More information can be found in the web-supplement 1.
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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.