Discovering disease-disease associations by fusing systems-level molecular data

Abstract

The advent of genome-scale genetic and genomic studies allows new insight into disease classification. Recently, a shift was made from linking diseases simply based on their shared genes towards systems-level integration of molecular data. Here, we aim to find relationships between diseases based on evidence from fusing all available molecular interaction and ontology data. We propose a multi-level hierarchy of disease classes that significantly overlaps with existing disease classification. In it, we find 14 disease-disease associations currently not present in Disease Ontology and provide evidence for their relationships through comorbidity data and literature curation. Interestingly, even though the number of known human genetic interactions is currently very small, we find they are the most important predictor of a link between diseases. Finally, we show that omission of any one of the included data sources reduces prediction quality, further highlighting the importance in the paradigm shift towards systems-level data fusion.

Figure 1. Data fusion.

Panel A is a graphical representation of our data fusion by matrix factorisation approach to discovering disease-disease associations. The shown block-based matrix representation exactly corresponds to the data fusion schema in Figure 3-A. We combine 11 data sources on four different types of objects (see Methods): drugs, genes, Disease Ontology (DO) terms and Gene Ontology (GO) terms. These data are encoded in two types of matrices: constraint matrices, which relate objects of the same type (such as drugs if they have common adverse effects) and are placed on the main diagonal (illustrated by matrices with blue entries); and relation matrices, which relate objects of different types and are placed off the main diagonal (illustrated by matrices with grey entries). Our data fusion approach involves three main steps. First, we construct a block-based matrix representation of all data sources used in our study (panel A, left). The molecular data encoded in these matrices are sparse, incomplete and noisy (depicted by different shades of blue and grey) and some matrices are completely missing because associated data sources are not available (e.g. no link between GO terms and drugs). In the second step, we simultaneously decompose all relation matrices as products of low-rank matrix factors and use constraint matrices to regularise low-rank approximations of relation matrices. The key idea of our data fusion approach is sharing low-rank matrix factors between relation matrices that describe objects of common type. The resulting factorised system (panel A, middle) contains matrix factors that are specific to every type of objects (four matrices in left part; e.g. G_Drug), and matrix factors that are specific to every data source (six matrix factors in right part; e.g. S_{Gene, DO Term}). Thus, low-rank matrix factors capture source- and object type-specific patterns. Finally, we use matrix factors to reconstruct relation matrices and complete their unobserved entries (panel A, right). Panel B shows the algorithm for assigning diseases to classes and obtaining disease-disease association predictions.

Figure 2. Multi-layered hierarchical decomposition of disease classes.

Our analysis yields 108 disease classes using the most stringent threshold for predicting disease-disease associations. Identified classes are rather small and each class contains at most 17 diseases with the exception of the largest disease class that consists of 146 diseases (at root layer). We further decompose the largest class by re-running the data fusion process on set of diseases that are in the largest class in order to identify its fine-grained structure (level one). We repeat data fusion analysis using this top-down strategy two more times (levels two and three), which results in a hierarchical decomposition of most reliable disease classes (see Methods).

Figure 3. System-level data fusion approach to disease re-classification.

Panel A shows the relationships between data sources: nodes represent four types of objects, i.e. genes, GO terms, DO terms and drugs; arcs denote data sources that relate objects of different types (relation matrices, R_ij, i ≠ j), or objects of the same type (constraints, Θ_i). Panel B shows a disease class predicted by data fusion overlaid with a DO graph. Members of the disease class are outlined. This illustrates the ability of data fusion to successfully capture real disease classes: diseases associated with crescentic glomerulonephritis are presented.