HumanBase applies community detection to find cohesive gene clusters from a provided gene list and a selected relevant tissue. Genes within a cluster share local network neighborhoods and together form a cohesive, specific functional module. Module detection enables systematic association of genes - even functionally uncharacterized genes - to specific processes and phenotypes represented in the detected modules. Functional modules are identified with tissue-specific networks, which predict gene interactions from massive data collections. Thus the discovered modules potentially capture higher-order tissue-specific function.
Functional module detection is described in: Krishnan A*, Zhang R*, Yao V, Theesfeld CL, Wong AK, Tadych A, Volfovsky N, Packer A, Lash A, Troyanskaya OG.(2016) Genome-wide prediction and functional characterization of the genetic basis of autism spectrum disorder. Nature Neuroscience.
The approach is based on shared k-nearest-neighbors (SKNN) and the Louvain community-finding algorithm to cluster the user-selected tissue network into distinct modules of tightly connected genes. The SKNN-based strategy has the advantages of alleviating the effect of high-degree genes and accentuating local network structure by connecting genes that are likely to be functionally clustered together.
- This technique proceeds as follows:
- First, we create a subset of the user-selected network containing only the user-provided genes and all the edges between them. Given the resulting graph G with V nodes (user-provided genes) and E edges, with each edge between genes i and j associated with a weight pij,
- Calculate a new weight for the edge between each pair of nodes i and j that is equal to the number of k nearest neighbors (based on the original weights pij) shared by i and j.
- Choose the top 5% of the edges based on the new edge weights, and apply a graph clustering algorithm.
- This approach has two key desirable characteristics:
- Choosing the highest k values instead of all edges deemphasizes high-degree 'hub' nodes and brings equal attention to highly specific edges between low-degree nodes.
- Emphasizing local network-structure by connecting nodes that share a number of local neighbors automatically links genes that are highly likely to be part of the same cluster.
We use a dynamic k = min(50, 0.2 * |V|) to obtain the shared-nearest-neighbor tissue-specific network and apply the Louvain algorithm to cluster this network into distinct modules, where V is the number of query genes. Krishnan et al. (2016) showed that module node membership and cluster sizes are robust by testing a range of values for k from 10 to 100. To stabilize clustering across different runs of the Louvain algorithm, we run the algorithm 100 times and calculate cluster comembership scores for each pair of genes that was equal to the fraction of times (out of 100) the pair was assigned to the same cluster. Genes are assigned to clusters where their comembership score ≥ 0.9.
Resulting modules are then tested for functional enrichment using genes annotated to Gene Ontology biological process terms. GIANT networks use annotations from UniProt-GOA (experimental evidence codes), while MAGE networks use annotations from NCBI gene2go (all evidence codes including computationally inferred). Enrichment is also performed against Disease Ontology and MSigDB gene sets. Representative processes and pathways enriched within each cluster are presented alongside of the cluster with their resulting Q value. The Q value of each term associated to the modules is calculated using one-sided Fisher's exact tests and Benjamini-Hochberg corrections to correct for multiple tests.