Pathway Analysis of Expression Data: Deciphering Functional Building Blocks of Complex Diseases

Identification of differentially expressed pathways from expression data is an important problem because it allows us to gain insights into the functional working mechanism of cells beyond the detection of differentially expressed genes. In this paper we present a brief guide to methods for the pathway analysis of expression data. Despite the vast amount of different statistical methods that have been developed so far, there is a considerable similarity among them, allowing a systematic classification and a reduction to a few null hypotheses that are effectively tested. Systems biology aims to find emergent phenomena by the integration of heterogeneous data. In general, data integration itself is a part of any scientific inference: its elementary steps are the integration of observations (measurements) into the context of biological knowledge. However, in the case of systems biology, the scale of integration is many folds higher, resulting in a prodigious number of new computational approaches for the simultaneous analyses of heterogeneous data. In this paper we discuss one popular way of integrating biological knowledge into large-scale genome-wide measurements, namely the identification of functionally related genes (pathways) enriched or differentially expressed in gene expression data [1]. It should be noted that the approaches discussed are also applicable to the analyses of, e.g., RNA-seq, metabolomics or proteomics data and, generally, different types of biological measurements when preexisting biological knowledge is available. In the early stages of methodological developments for gene expression data analyses, most approaches were focused on producing so-called gene lists. This is a set of individual genes called differentially expressed as identified by univariate test statistics (e.g., a t-test) [2]–[4]. Instead, more recent approaches clearly reflect systems biology's trend of data integration and interpretation [5]–[7], focusing on sets of functionally related genes (e.g., from the same signaling or metabolic pathway) rather than individual genes. The purpose of this paper is to provide a brief guide to methods for the analysis of differentially expressed pathways or gene sets, which we simply call pathway-based methods. For this reason, we emphasize an illustration of the methods rather than their technical description. The reader is encouraged to follow the cited literature for technical details.