Abstract:It is becoming increasingly evident that the efficacy of single-gene computational analyses for complex traits is nearly exhausted and future advances hinge on unraveling the intricate combinatorial interactions among multiple genes. However, the discovery of modules of genes working in concert to manifest a complex trait has been crippled by combinatorial complexity, genetic heterogeneity, and validation biases. We introduce Maestro, a novel network approach that employs a multifaceted correlation measure, wh… Show more
“…We have addressed this issue by expanding network scaffolding to include two nodes per object, representing high and low values, respectively. 11 As illustrated in Figure 3 B, this expansion separates the clusters and justifies the use of transitivity. Figure 3 Duality node Assume that low values of object A are correlated with low values of object B, high values of object A are correlated with low values of object C, and no other correlations exist for objects A, B, and C. (A) In a standard network for which each object is represented by a single node, the transitivity assumption would falsely suggest that B and C are correlated.…”
Section: Network Constructionmentioning
confidence: 62%
“…This is problematic as when heterogeneity exists, one subgroup may exhibit high correlation, but there is no reason to expect other subgroups to hold any correlation, and this lack of correlation tends to weaken the correlation score. The only correlation measures that we are aware of that account for subset heterogeneity are Hamming distance 34 and its variants, and the two vector-based correlation measures that we have introduced: custom correlation coefficient 35 , 36 for single-nucleotide polymorphism data and Duo 11 for general real-valued data. Figure 2 Subset heterogeneity, effective sample size, and permutation examples Examples for pairs of objects, each with ten attribute values.…”
“…Typical scalar correlation metrics indicate the degree of correlation/anti-correlation but do not indicate whether high or low values are contributing to the relationship, creating an environment for the generation of what we refer to as duality nodes ( Figure 3 ). 11 Duality nodes lead to the merging of unassociated clusters. Moreover, these clusters may be the opposite of each other.…”
Section: Network Constructionmentioning
confidence: 99%
“…Here, we examine benefits and encumbrances of network modeling and demonstrate these characteristics in a popular application domain, gene co-expression analysis. 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 A brief description of this application follows.…”
Summary
Network modeling transforms data into a structure of nodes and edges such that edges represent relationships between pairs of objects, then extracts clusters of densely connected nodes in order to capture high-dimensional relationships hidden in the data. This efficient and flexible strategy holds potential for unveiling complex patterns concealed within massive datasets, but standard implementations overlook several key issues that can undermine research efforts. These issues range from data imputation and discretization to correlation metrics, clustering methods, and validation of results. Here, we enumerate these pitfalls and provide practical strategies for alleviating their negative effects. These guidelines increase prospects for future research endeavors as they reduce type I and type II (false-positive and false-negative) errors and are generally applicable for network modeling applications across diverse domains.
“…We have addressed this issue by expanding network scaffolding to include two nodes per object, representing high and low values, respectively. 11 As illustrated in Figure 3 B, this expansion separates the clusters and justifies the use of transitivity. Figure 3 Duality node Assume that low values of object A are correlated with low values of object B, high values of object A are correlated with low values of object C, and no other correlations exist for objects A, B, and C. (A) In a standard network for which each object is represented by a single node, the transitivity assumption would falsely suggest that B and C are correlated.…”
Section: Network Constructionmentioning
confidence: 62%
“…This is problematic as when heterogeneity exists, one subgroup may exhibit high correlation, but there is no reason to expect other subgroups to hold any correlation, and this lack of correlation tends to weaken the correlation score. The only correlation measures that we are aware of that account for subset heterogeneity are Hamming distance 34 and its variants, and the two vector-based correlation measures that we have introduced: custom correlation coefficient 35 , 36 for single-nucleotide polymorphism data and Duo 11 for general real-valued data. Figure 2 Subset heterogeneity, effective sample size, and permutation examples Examples for pairs of objects, each with ten attribute values.…”
“…Typical scalar correlation metrics indicate the degree of correlation/anti-correlation but do not indicate whether high or low values are contributing to the relationship, creating an environment for the generation of what we refer to as duality nodes ( Figure 3 ). 11 Duality nodes lead to the merging of unassociated clusters. Moreover, these clusters may be the opposite of each other.…”
Section: Network Constructionmentioning
confidence: 99%
“…Here, we examine benefits and encumbrances of network modeling and demonstrate these characteristics in a popular application domain, gene co-expression analysis. 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 A brief description of this application follows.…”
Summary
Network modeling transforms data into a structure of nodes and edges such that edges represent relationships between pairs of objects, then extracts clusters of densely connected nodes in order to capture high-dimensional relationships hidden in the data. This efficient and flexible strategy holds potential for unveiling complex patterns concealed within massive datasets, but standard implementations overlook several key issues that can undermine research efforts. These issues range from data imputation and discretization to correlation metrics, clustering methods, and validation of results. Here, we enumerate these pitfalls and provide practical strategies for alleviating their negative effects. These guidelines increase prospects for future research endeavors as they reduce type I and type II (false-positive and false-negative) errors and are generally applicable for network modeling applications across diverse domains.
“…The DUO metric resembles the Sörensen-Dice Index of vector similarity (a statistic for comparing discrete distributions), however, it has been adapted to compute multiple correlation statistics between vectors. For computational efficiency, DUO calculates the similarity between two binary vectors by binning matrix values into low or high (0 or 1) categories [37][38][39][40]. DUO comparisons produce four possible combinations for a 2-way comparison: (1, 1), (1, 0), (0, 1), and (0, 0).…”
Predicted growth in world population will put unparalleled stress on the need for sustainable energy and global food production, as well as increase the likelihood of future pandemics. In this work, we identify high-resolution environmental zones in the context of a changing climate and predict longitudinal processes relevant to these challenges. We do this using exhaustive vector comparison methods that measure the climatic similarity between all locations on earth at high geospatial resolution. The results are captured as networks, in which edges between geolocations are defined if their historical climates exceed a similarity threshold. We then apply Markov clustering and our novel Correlation of Correlations method to the resulting climatic networks, which provides unprecedented agglomerative and longitudinal views of climatic relationships across the globe. The methods performed here resulted in the fastest (9.37x1018 operations/sec) and one of the largest (168.7x1021 operations) scientific computations ever performed, with more than 100 quadrillion edges considered for a single climatic network. Correlation and network analysis methods of this kind are widely applicable across computational and predictive biology domains, including systems biology, ecology, carbon cycles, biogeochemistry, and zoonosis research.
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