From patterns to pathways: gene expression data analysis comes of age.
Many different biological questions are routinely studied using transcriptional profiling on microarrays. A wide range of approaches are available for gleaning insights from the data obtained from such experiments. The appropriate choice of data-analysis technique depends both on the data and on the goals of the experiment. This review summarizes some of the common themes in microarray data analysis, including detection of differential expression, clustering, and predicting sample characteristics. Several approaches to each problem, and their relative merits, are discussed and key areas for additional research highlighted.
Slonim
Donna K
DK
Department of Genomics, Wyeth Research, 35 Cambridge Park Drive, Cambridge, Massachusetts 02140, USA. dslonim@wyeth.com
D016000:Cluster Analysis; D019295:Computational Biology; D003627:Data Interpretation, Statistical; D005544:Forecasting; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D013223:Statistics as Topic; D014158:Transcription, Genetic