Bioinformatics techniques in microarray research: applied microarray data analysis using R and SAS software.
Exploration of the underlying biological mechanisms of disease is useful for many purposes such as the development of novel treatment modalities in addition to informing on-going risk factor research. DNA-microarray technology is a relatively recent and novel approach to conducting genome-wide gene expression studies to identify previously unknown biological pathways associated with disease. The copious data arising from microarray experiments is not conducive to traditional analytical approaches. Beyond the analytical challenges, there are equally important issues related to the interpretation and presentation of results. This chapter outlines appropriate techniques for analyzing microarray data in a fashion that also yields a list of top genes with differential expression in a given experiment. Derivatives of the top genes list can be used as a starting point for the presentation of study results. The list also serves as the basis for additional techniques related to enhanced interpretation and presentation of results. All analyses described in this chapter can be performed using relatively limited computational resources such as a lap top PC with at least 2.0 GB of memory and 2.0 GHz of processing speed.
Demmer
Ryan T
RT
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA.
Pavlidis
Paul
P
Papapanou
Panos N
PN
D019295:Computational Biology / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software