Transcriptional profiling of non-small cell lung cancer using oligonucleotide microarrays

The genomic response of skeletal muscle to methylprednisolone using microarrays: tailoring data mining to the structure of the pharmacogenomic time series

High-throughput data collection using gene microarrays has great potential as a method for addressing the pharmacogenomics of complex biological systems. Similarly, mechanism-based pharmacokinetic/pharmacodynamic modeling provides a tool for formulating quantitative testable hypotheses concerning the responses of complex biological systems. As the response of such systems to drugs generally entails cascades of molecular events in time, a time series design provides the best approach to capturing the full scope of drug effects. A major problem in using microarrays for high-throughput data collection is sorting through the massive amount of data in order to identify probe sets and genes of interest. Due to its inherent redundancy, a rich time series containing many time points and multiple samples per time point allows for the use of less stringent criteria of expression, expression change and data quality for initial filtering of unwanted probe sets. The remaining probe sets can then become the focus of more intense scrutiny by other methods, including temporal clustering, functional clustering and pharmacokinetic/pharmacodynamic modeling, which provide additional ways of identifying the probes and genes of pharmacological interest.

Practical approaches to analyzing results of microarray experiments

Microarray technology is rapidly becoming a standard laboratory technique. The main challenges related to the successful implementation of the technology are analysis-related. In this article we provide a practically oriented review focusing on methods for analysis of large-scale gene expression data in the research laboratory. We describe the various common clustering methods and outline our approach to using them. We discuss methods for scoring genes for their relevance, focusing on the statistical meaning of microarray results, especially with regard to the problem of multiple testing. We also deal with the problem of adding biologic meaning to the results of microarray experiments and describe advanced tools that represent different but valid directions in providing automated solutions to this problem. The tools and approaches described and discussed here should provide the reader with a preliminary understanding of the analysis of the results of microarray experiments. The practical focus of this review should remove the mystery behind the analysis of microarray experiments, thus leading to more productive and efficient use of the technology.