Unlocking proteomic heterogeneity in complex diseases through visual analytics

Abstract

Despite years of preclinical development, biological interventions designed to treat complex diseases like asthma often fail in phase III clinical trials. These failures suggest that current methods to analyze biomedical data might be missing critical aspects of biological complexity such as the assumption that cases and controls come from homogeneous distributions. Here we discuss why and how methods from the rapidly evolving field of visual analytics can help translational teams (consisting of biologists, clinicians, and bioinformaticians) to address the challenge of modeling and inferring heterogeneity in the proteomic and phenotypic profiles of patients with complex diseases. Because a primary goal of visual analytics is to amplify the cognitive capacities of humans for detecting patterns in complex data, we begin with an overview of the cognitive foundations for the field of visual analytics. Next, we organize the primary ways in which a specific form of visual analytics called networks have been used to model and infer biological mechanisms, which help to identify the properties of networks that are particularly useful for the discovery and analysis of proteomic heterogeneity in complex diseases. We describe one such approach called subject-protein networks, and demonstrate its application on two proteomic datasets. This demonstration provides insights to help translational teams overcome theoretical, practical, and pedagogical hurdles for the widespread use of subject-protein networks for analyzing molecular heterogeneities, with the translational goal of designing biomarker-based clinical trials, and accelerating the development of personalized approaches to medicine.

Methylation differences reveal heterogeneity in preterm pathophysiology: results from bipartite network analyses

“…Our analysis consisted of three steps [18]: (1) exploratory visual analysis to identify emergent bipartite relationships such as patterns of how methylation sites co-occur across subjects; (2) quantitative analysis to quantitatively verify and statistically evaluate the emergent patterns such as clusters; (3) inference of the biological mechanisms underlying different emergent clusters of subjects. This three-step method used in our earlier studies has revealed complex but comprehensible visual patterns, leading to inferences about the biomarkers and underlying mechanisms involved.…”

“…Because the network layout suggested a clustered topology for subjects and for methylation sites, we used the agglomerative hierarchical clustering method [41], which is best suited for networks that have small clusters [18, 35]. The clustering was done using the Manhattan dissimilarity measure with the Ward linkage function, and the number of clusters and their boundaries were determined based on natural breaks in the subject and methylation site dendrograms.…”

“…In prior work [35–40] we have shown that such “subject-variable” [18] bipartite networks are especially effective in helping to comprehend subject subgroups because they not only help to identify how subjects cluster with each other, but also how they are related to variables such as methylation sites. This feature enables an understanding of intra-cluster associations (e.g., the left cluster in enriched with cases associated predominantly with three methylation sites) and inter-cluster associations (e.g.…”

“…One promising approach for identifying and comprehending such complex patterns of co-occurrence is through unsupervised bipartite network analysis [17]. For example, we have demonstrated that subject-variable bipartite networks [18] (which represent both subjects and variables in the same representation) can enable (1) the rapid identification of significant patient subgroups, and the variables (e.g., genes) that are strongly associated with them, and (2) the comprehension of those relationships resulting in hypotheses for processes (e.g., biological mechanisms) underlying those subgroups. Here we demonstrate the use of bipartite network analysis and visualization for re-analyzing data from our previous study [16] with the goal of enabling new insights into molecular heterogeneity and potential mechanisms that underlie PTB.…”

Towards Team-Centered Informatics: Accelerating Innovation in Multidisciplinary Scientific Teams Through Visual Analytics

“…As shown in Figure 2A, the bipartite visual analytical method takes as input any data set consisting of patients and their characteristics (e.g., mutated genes), and automatically outputs a quantitative and visual description of patient subgroups (Bhavnani et al, 2015). The quantitative output provides the number, size, and statistical significance of patient subgroups and their most highly co-occurring characteristics.…”