IMR Press / FBL / Volume 7 / Issue 1 / DOI: 10.2741/A743

Frontiers in Bioscience-Landmark (FBL) is published by IMR Press from Volume 26 Issue 5 (2021). Previous articles were published by another publisher on a subscription basis, and they are hosted by IMR Press on imrpress.com as a courtesy and upon agreement with Frontiers in Bioscience.

Open Access Article
Statistical methods for analysis of time course gene expression data
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1 Rowe Program in Human Genetics, Departments of Medicine and Statistics, University of California, Davis, CA 95616, USA
Academic Editor:Heping Zhang
Front. Biosci. (Landmark Ed) 2002, 7(1), 90–98; https://doi.org/10.2741/A743
Published: 1 May 2002
(This article belongs to the Special Issue Statistics and bioinformatics in medicine)
Abstract

Since many biological systems or regulatory networks are dynamic systems, gene expression levels measured over different time points during a given biological process can often provide more insights about the underlying system. These gene expression data measured over time are often called the time-course gene expression data. One unique feature of such data is the time dependency of the gene expression levels for a given gene at different times or between two different genes. Statistical analysis needs to account for such dependency in order to make valid inferences. This paper presents several statistical methods for analyzing such time-course gene expression data, including the time-lagged correlation coefficient for analyzing the relationship between genes, a mixed-effects model with splines for clustering genes and for estimating missing gene expression data, and a new method for aligning gene expression profiles obtained under two experimental conditions and for identifying gene clusters that show significant changes between two experimental conditions. We used the yeast cell cycle gene expression data sets to illustrate these methods and obtained the biologically meaningful conclusions from these analyses.

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