Gene expression profiling provides unprecedented opportunities to study patterns of gene expression regulation, for example, in diseases or developmental processes. Bioinformatics analysis plays an important part of processing the information embedded in large-scale expression profiling studies and for laying the foundation for biological interpretation.
Over the past years, numerous tools have emerged for microarray data analysis. One of the most popular platforms is Bioconductor, an open source and open development software project for the analysis and comprehension of genomic data, based on the R programming language.
In this chapter, we use Bioconductor analysis packages on a heart development dataset to demonstrate the workflow of microarray data analysis from annotation, normalization, expression index calculation, and diagnostic plots to pathway analysis, leading to a meaningful visualization and interpretation of the data.
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