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This article is part of the supplement: Italian Society of Bioinformatics (BITS): Annual Meeting 2007

Open Access Highly Accessed Research

Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells

Margherita Mutarelli123, Luigi Cicatiello13, Lorenzo Ferraro1, Olì MV Grober13, Maria Ravo1, Angelo M Facchiano2, Claudia Angelini4 and Alessandro Weisz13*

Author Affiliations

1 Department of General Pathology - Second University of Napoli, Napoli, Italy

2 Institute of Food Sciences, National Research Council (ISA-CNR), Avellino, Italy

3 AIRC Naples Oncogenomics Center, Napoli, Italy

4 Institute of Applied Calculus, National Research Council (IAC-CNR) Napoli, Italy

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BMC Bioinformatics 2008, 9(Suppl 2):S12  doi:10.1186/1471-2105-9-S2-S12

Published: 26 March 2008

Abstract

Background

Microarray experiments enable simultaneous measurement of the expression levels of virtually all transcripts present in cells, thereby providing a ‘molecular picture’ of the cell state. On the other hand, the genomic responses to a pharmacological or hormonal stimulus are dynamic molecular processes, where time influences gene activity and expression. The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal relationships between samples.

Results

We compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive human breast cancer cells. Gene expression was monitored with the innovative Illumina BeadArray platform, which includes an average of 30-40 replicates for each probe sequence randomly distributed on the chip surface. We present and discuss the results obtained by applying to these datasets different statistical methods for serial gene expression analysis. The influence of the normalization algorithm applied on data and of different parameter or threshold choices for the selection of differentially expressed transcripts has also been evaluated. In most cases, the selection was found fairly robust with respect to changes in parameters and type of normalization. We then identified which genes showed an expression profile significantly affected by the hormonal treatment over time. The final list of differentially expressed genes underwent cluster analysis of functional type, to identify groups of genes with similar regulation dynamics.

Conclusions

Several methods for processing time series gene expression data are presented, including evaluation of benefits and drawbacks of the different methods applied. The resulting protocol for data analysis was applied to characterization of the gene expression changes induced by estrogen in human breast cancer ZR-75.1 cells over an entire cell cycle.