Biological functions are often realized by groups of interacting molecules or cells . Membership in these groups may overlap when molecules or cells are reused in multiple functions. Traditional clustering methods assign each component to one group. Noisy measurements are common in high-throughput biological datasets. These two limitations reduce our ability to accurately define clusters in biological datasets and to interpret their biological functions. To address these limitations , we designed an algorithm called SpeakEasy , which detects overlapping or non-overlapping communities in biological networks. Input to SpeakEasy can be physical networks, such as molecular interactions, or inferred networks, such as gene coexpression networks. The networks can be directed or undirected, and may contain negative links. SpeakEasy combines traditional bottom-up and top-down approaches to clustering, by creating competition between clusters. Nodes that oscillate between multiple clusters in this competition are classified as multi-community nodes. SpeakEasy can quickly process networks with tens of thousands of nodes , quantify the stability of each cluster and detect an optimal number of clusters without requiring user preset parameter. Clustering networks derived from gene microarrays, protein affinity , sorted cell populations, electrophysiology , functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights. For instance, we can identify overlapping co-regulated genes sets, multi-complex proteins and robust changes to the community structure of co-active brain regions in Parkinson disease. These insights rely on a robust overlapping clustering approach that enables a more realistic interpretation of common high-throughput datasets .
Biological functions are carried out by groups of interacting molecules , cells or tissues, known as communities . Membership in these communities may overlap when biological components are involved in multiple functions. However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit our ability to detect biological communities, and therefore our ability to understand biological functions. To address these limitations and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using top-down and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds of thousands of nodes . SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including: gene microarrays, protein interactions , sorted cell populations, electrophysiology and fMRI brain imaging .
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Traditional clustering methods assign each component to one group. Noisy measurements are common in high-throughput biological datasets. These two limitations reduce
However, traditional clustering methods detect non-overlapping communities. These detected communities may also be unstable and difficult to replicate, because traditional methods are sensitive to noise and parameter settings. These aspects of traditional clustering methods limit
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accurately define clusters in biological datasets and to interpret their
detect biological communities, and therefore our ability to understand
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, we designed an algorithm called SpeakEasy , which detects overlapping or non-overlapping communities in biological networks. Input to SpeakEasy can be physical networks, such as molecular interactions, or inferred networks, such as gene coexpression networks. The networks can be directed or undirected, and may contain negative links. SpeakEasy combines traditional bottom-up and
and detect robust overlapping biological communities, we propose an unorthodox clustering method called SpeakEasy which identifies communities using
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approaches to clustering, by creating competition between clusters. Nodes that oscillate between multiple clusters in this competition are classified as multi-community nodes. SpeakEasy can quickly process networks with tens
and bottom-up approaches simultaneously. Specifically, nodes join communities based on their local connections, as well as global information about the network structure. This method can quantify the stability of each community, automatically identify the number of communities, and quickly cluster networks with hundreds
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, quantify the stability of each cluster and detect an optimal number of clusters without requiring user preset parameter. Clustering networks derived from
. SpeakEasy shows top performance on synthetic clustering benchmarks and accurately identifies meaningful biological communities in a range of datasets, including:
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, functional magnetic resonance imaging of resting-state brain activity, and synthetic datasets validate the ability of SpeakEasy to facilitate biological insights. For instance, we can identify overlapping co-regulated genes sets, multi-complex proteins and robust changes to the community structure of co-active brain regions in Parkinson disease. These insights rely on a robust overlapping clustering approach that enables a more realistic interpretation of common high-throughput datasets
and fMRI brain imaging