Abstract
BackgroundMicroarray 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.ResultsWe 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.ConclusionsSeveral 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.
Highlights
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
This ‘static’ approaches have the disadvantage of not taking into account temporal relationship among samples, leading to results that are often invariant under permutation of the values representing different time points, ignoring the biological causality which can be inferred from the temporal response
Experimental design of the experiment and its implications We present the analysis performed on a time series of microarray data from breast cancer cells treated with estrogens
Summary
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. Complexity of the cellular responses to estrogen and their receptors can ideally be investigated only with comprehensive analytical approaches, including in particular gene expression profiling with microarrays [4,5] These technologies allow to assess at genome-wide scale changes in gene activity resulting, for example, from hormonal and pharmacological treatments or pathological and divergent physiological conditions. Most of the methods to identify differentially expressed genes adapt classical techniques originally designed for static experiments This ‘static’ approaches have the disadvantage of not taking into account temporal relationship among samples, leading to results that are often invariant under permutation of the values representing different time points, ignoring the biological causality which can be inferred from the temporal response. They do not accurately consider the existing temporal structure in the data which can have as consequence a falsely calculated significance of the genes
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