Chapter 2: Data-driven view of disease biology

Abstract

Modern experimental strategies often generate genome-scale measurements of human tissues or cell lines in various physiological states. Investigators often use these datasets individually to help elucidate molecular mechanisms of human diseases. Here we discuss approaches that effectively weight and integrate hundreds of heterogeneous datasets to gene-gene networks that focus on a specific process or disease. Diverse and systematic genome-scale measurements provide such approaches both a great deal of power and a number of challenges. We discuss some such challenges as well as methods to address them. We also raise important considerations for the assessment and evaluation of such approaches. When carefully applied, these integrative data-driven methods can make novel high-quality predictions that can transform our understanding of the molecular-basis of human disease.

Figure 1. Potential distributions of experimental results obtained for datasets collected under three different conditions.

The dotted line indicates the distribution of negative examples and the solid line indicates the distribution of positive examples. In condition A the positive examples more often occur to the right of the negative examples, in condition B both sets overlap, and in condition C the positive examples occur more often to the left of the negative examples.

Figure 2. An example of querying HEFalMp for the role of APOE across all biological processes (http://hefalmp.princeton.edu/).

Figure 3. The result of querying HEFalMp for the role of APOE across all biological processes.

Red links indicate that there is a high probability of a functional relationship between the two genes.

Figure 4. The highest and lowest contributing datasets for the pair of APOE and PLTP are shown (http://hefalmp.princeton.edu/gene/one_specific_gene/18543?argument=21697&context=0).

These contributions are based on how well the bin containing the queried gene pair separated known positive functional relationships from known negative functional relationships.

Figure 5. The diseases that are significantly connected to APOE through the guilt by association strategy used in HEFalMp.

Alzheimer disease and Macular degeneration are both annotated to the disease in OMIM as noted by the gold bars to the left of the disease (http://hefalmp.princeton.edu/gene/diseases?context=0&name=APOE). The other diseases are implicated by APOE's functional relationships to genes annotated to that disease in OMIM.

Figure 6. The genes that are most significantly connected to Alzheimer disease genes using the HEFalMp network and OMIM disease gene annotations (http://hefalmp.princeton.edu/disease/all_genes/55?context=0).

The gold bars to the left of APP and APOE indicate that both genes were annotated Alzheimer disease according to OMIM.

Figure 7. The functional relationship network discovered by a data driven integration for the YFG gene in YFO.