Abstract
BackgroundRecent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo. Instead, some circadian-related genes may be delayed by 4–8 hrs in peak expression in one tissue relative to the other. These interesting biological observations prompt a statistical question regarding how to distinguish the synchronized genes from genes that are systematically lagged in phase/peak expression time across two tissues.ResultsWe propose a set of techniques from circular statistics to analyze phase angles of circadian-related genes in two tissues. We first estimate the phases of a cycling gene separately in each tissue, which are then used to estimate the paired angular difference of the phase angles of the gene in the two tissues. These differences are modeled as a mixture of two von Mises distributions which enables us to cluster genes into two groups; one group having synchronized transcripts with the same phase in the two tissues, the other containing transcripts with a discrepancy in phase between the two tissues. For each cluster of genes we assess the association of phases across the tissue types using circular-circular regression. We also develop a bootstrap methodology based on a circular-circular regression model to evaluate the improvement in fit provided by allowing two components versus a one-component von-Mises model.ConclusionWe applied our proposed methodologies to the circadian-related genes common to heart and liver tissues in Storch et al. [2], and found that an estimated 80% of circadian-related transcripts common to heart and liver tissues were synchronized in phase, and the other 20% of transcripts were lagged about 8 hours in liver relative to heart. The bootstrap p-value for being one cluster is 0.063, which suggests the possibility of two clusters. Our methodologies can be extended to analyze peak expression times of circadian-related genes across more than two tissues, for example, kidney, heart, liver, and the suprachiasmatic nuclei (SCN) of the hypothalamus.
Highlights
Recent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo
Most cell cycle gene expression patterns are based on cell cultures studied in vitro, while most circadian gene expressions are based on various tissues or organs in vivo
We expect that a set of cell cycle genes commonly expressed in various conditions are consistent in their peak expression/ activation time [9]. It is an opening question whether phases or peak expression times for a set of circadian-related genes commonly expressed in multiple tissues, such as heart, liver, kidney, and suprachiasmatic nuclei (SCN) of the hypothalamus are in synchrony because expression of some circadian-related genes may be tissue-specific
Summary
Recent circadian clock studies using gene expression microarray in two different tissues of mouse have revealed not all circadian-related genes are synchronized in phase or peak expression times across tissues in vivo. BMC Bioinformatics 2006, 7:87 http://www.biomedcentral.com/1471-2105/7/87 ulated efforts to apply and develop methodologies in circular/directional statistics to elucidate important characteristics of circadian gene expression and compare their patterns of peak expression times (phase angles) across different tissue types, to help elucidate their diverse tissue-specific functions [1,2,4,5]. To date, less is known about circadian genes: only eight core mammalian circadian genes have been identified: Csnk1e, Cry, Cry, Per, Per, Per, Clock, and Bmall [8] It is not clear whether these known circadian genes and any other circadian-related genes identified from high-throughput microarrays can be assigned to a few functional phases, in analogy to the phases (G1, S, G2, M) in cell cycle. Statistical tools for analyzing such a type of circular data cross multiple tissues need to be developed
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