Data integration

In the last decade, an unprecedented amount of post-genomic experimental information has become available. Datasets originating from transcriptomic analysis, metabolite profiling, and proteomics can be produced faster, with ever increasing accuracy and decreasing cost. However, putting the pieces together is not trivial. Our understanding of cellular phenomena based on omics data depends on - and is limited by - our capability to implement appropriate analysis tools able to integrate the different omics approaches [75, 78]. Bringing together such disparate datasets presents a considerable challenge [76]. Such analysis is time consuming and prone to both error and speculation. Consequently, there is a substantial need to consider both the methods currently being used and the statistical principles involved in the analysis of post-genomic experimental data.