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Title: Order Restricted Inference for Oscillatory Systems for Detecting Rhythmic Signals
Authors: Larriba, Yolanda
Rueda, Cristina
Fernández, Miguel
Peddada, Shyamal
Issue Date: 2016
Citation: Nucleic Acids Research, 44(22): e163
Abstract: Many biological processes, such as cell cycle, circadian clock, menstrual cycles, are governed by oscillatory systems consisting of numerous components that exhibit rhythmic patterns over time. It is not always easy to identify such rhythmic components. For example, it is a challenging problem to identify circadian genes in a given tissue using time-course gene expression data. There is a great potential for misclassifying non-rhythmic as rhythmic genes and vice versa. This has been a problem of considerable interest in recent years. In this article we develop a constrained inference based methodology called Order Restricted Inference for Oscillatory Systems (ORIOS) to detect rhythmic signals. Instead of using mathematical functions (e.g. sinusoidal) to describe shape of rhythmic signals, ORIOS uses mathematical inequalities. Consequently, it is robust and not limited by the biologist’s choice of the mathematical model. We studied the performance of ORIOS using simulated as well as real data obtained from mouse liver, pituitary gland and data from NIH3T3, U2OS cell lines. Our results suggest that, for a broad collection of patterns of gene expression, ORIOS has substantially higher power to detect true rhythmic genes in comparison to some popular methods, while also declaring substantially fewer non-rhythmic genes as rhythmic.
Peer Review: SI
DOI: 10.1093/nar/gkw771
Sponsor: Spanish Ministerio de Ciencia e Innovación [MTM2015-71217-R]
Spanish Ministerio de Educación, Cultura y Deporte [FPU14/04534]
Intramural Research Program of the National Institute of Environmental Health Sciences (NIEHS) [Z01 ES101744-04]
Language: eng
Rights: info:eu-repo/semantics/openAccess
Appears in Collections:DEP24 - Artículos de revista

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