Bioconductor software has become a standard tool for the analysis and comprehension of data from high-throughput genomics experiments. Its application spans a broad field of technologies used in contemporary molecular biology. In this volume, the authors present a collection of cases to apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: import and preprocessing of data from various sources, statistical modeling of differential gene expression, biological metadata, application of graphs and graph rendering, machine learning for clustering and classification problems, gene set enrichment analysis.