Abstract
BackgroundGene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. The response to a given stimulus is usually different for different genes and may depend on time. One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest. From methodological and computational point of view, analyzing high-dimensional time course microarray data requires very specific set of tools which are usually not included in standard software packages. Recently, the authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. The method is based on explicit expressions for calculations and, hence, very computationally efficient.ResultsThe software package BATS (Bayesian Analysis of Time Series) presented here implements the methodology described above. It allows an user to automatically identify and rank differentially expressed genes and to estimate their expression profiles when at least 5–6 time points are available. The package has a user-friendly interface. BATS successfully manages various technical difficulties which arise in time-course microarray experiments, such as a small number of observations, non-uniform sampling intervals and replicated or missing data.ConclusionBATS is a free user-friendly software for the analysis of both simulated and real microarray time course experiments. The software, the user manual and a brief illustrative example are freely available online at the BATS website:
Highlights
Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments
One of the goals of modern molecular biology is the high-throughput identification of genes associated with a particular treatment or a biological process of interest
The statistical method implemented in BATS has been validated using both real and simulated data in [12] and [22]
Summary
Gene expression levels in a given cell can be influenced by different factors, namely pharmacological or medical treatments. One of the goals of modern molecular biology is the highthroughput identification of genes associated with a particular treatment or a biological process of interest. The authors of this paper developed a fully Bayesian approach which allows one to identify differentially expressed genes in a 'one-sample' time-course microarray experiment, to rank them and to estimate their expression profiles. "molecular picture" of a biological system under study and a potential of describing evolution of gene expressions in time This potential has not yet been fully exploited since there is still a shortage of statistical methods which take into account the temporal relationship between the samples in microarray analysis. Papers by [3,4] and the Limma package by [5] have similar approaches
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