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A scalable approach for discovering conserved active subnetworks across species.

ABSTRACT: Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-species network search algorithm, neXus (Network-cross(X)-species-Search), that discovers conserved, active subnetworks based on parallel differential expression studies in multiple species. Our approach leverages functional linkage networks, which provide more comprehensive coverage of functional relationships than physical interaction networks by combining heterogeneous types of genomic data. We applied our cross-species approach to identify conserved modules that are differentially active in stem cells relative to differentiated cells based on parallel gene expression studies and functional linkage networks from mouse and human. We find hundreds of conserved active subnetworks enriched for stem cell-associated functions such as cell cycle, DNA repair, and chromatin modification processes. Using a variation of this approach, we also find a number of species-specific networks, which likely reflect mechanisms of stem cell function that have diverged between mouse and human. We assess the statistical significance of the subnetworks by comparing them with subnetworks discovered on random permutations of the differential expression data. We also describe several case examples that illustrate the utility of comparative analysis of active subnetworks.

SUBMITTER: Deshpande R

PROVIDER: S-EPMC3000367 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature

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A scalable approach for discovering conserved active subnetworks across species.

Deshpande Raamesh R Sharma Shikha S Verfaillie Catherine M CM Hu Wei-Shou WS Myers Chad L CL

PLoS computational biology 20101209 12

Overlaying differential changes in gene expression on protein interaction networks has proven to be a useful approach to interpreting the cell's dynamic response to a changing environment. Despite successes in finding active subnetworks in the context of a single species, the idea of overlaying lists of differentially expressed genes on networks has not yet been extended to support the analysis of multiple species' interaction networks. To address this problem, we designed a scalable, cross-spec ...[more]

PMID: 21170309

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Project description:BackgroundStudy of biological networks is an essential first step to understand the complex functions they govern in different organisms. The topology of interactions that define how biological networks operate is often determined through high-throughput experiments. Noisy nature of high-throughput experiments, however, can result in multiple alternative network topologies that explain this data equally well. One key step to resolve the differences is to identify the subnetworks which appear significantly more frequently in a biological network data set than expected.MethodWe present a method named SiS (Significant Subnetworks) to find subnetworks with the largest probability to appear in a collection of biological networks. We define these subnetworks as the most probable subnetworks. SiS summarizes the interactions in the given collection of networks in a special template network. It uses the template network to guide the search for most probable subnetworks. It computes the lower and upper bound scores on how good the potential solutions are (i.e., the number of input networks that contain the subnetwork). As the search continues, it tightens the bound dynamically and prunes a massive number of unpromising solutions in that process.Results and conclusionsExperiments on comprehensive data sets depict that the most probable subnetworks found by SiS in a large collection of networks are also very frequent as well. In metabolic network data set, we found that subnetworks in eukaryote are more conserved than those of prokaryote. SiS also scales well to large data sets and subnetworks and runs orders of magnitude faster than an existing method, MULE. Depending on the size of the subnetwork in the same data set, the running time of SiS ranges from a few seconds to minutes; MULE, on the other hand, runs either for hours or does not even finish in days. In human transcription regulatory network data set, SiS finds a large backbone subnetwork that appears frequently regardless of diverse cell types.

Project description:A growing number of studies have explored the process of pre-implantation embryonic development of multiple mammalian species. However, the conservation and variation among different species in their developmental programming are poorly defined due to the lack of effective computational methods for detecting co-regularized genes that are conserved across species. The most sophisticated method to date for identifying conserved co-regulated genes is a two-step approach. This approach first identifies gene clusters for each species by a cluster analysis of gene expression data, and subsequently computes the overlaps of clusters identified from different species to reveal common subgroups. This approach is ineffective to deal with the noise in the expression data introduced by the complicated procedures in quantifying gene expression. Furthermore, due to the sequential nature of the approach, the gene clusters identified in the first step may have little overlap among different species in the second step, thus difficult to detect conserved co-regulated genes.We propose a cross-species bi-clustering approach which first denoises the gene expression data of each species into a data matrix. The rows of the data matrices of different species represent the same set of genes that are characterized by their expression patterns over the developmental stages of each species as columns. A novel bi-clustering method is then developed to cluster genes into subgroups by a joint sparse rank-one factorization of all the data matrices. This method decomposes a data matrix into a product of a column vector and a row vector where the column vector is a consistent indicator across the matrices (species) to identify the same gene cluster and the row vector specifies for each species the developmental stages that the clustered genes co-regulate. Efficient optimization algorithm has been developed with convergence analysis. This approach was first validated on synthetic data and compared to the two-step method and several recent joint clustering methods. We then applied this approach to two real world datasets of gene expression during the pre-implantation embryonic development of the human and mouse. Co-regulated genes consistent between the human and mouse were identified, offering insights into conserved functions, as well as similarities and differences in genome activation timing between the human and mouse embryos.The R package containing the implementation of the proposed method in C?++?is available at: https://github.com/JavonSun/mvbc.git and also at the R platform https://www.r-project.org/jinbo@engr.uconn.edu.

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A scalable approach for discovering conserved active subnetworks across species.

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A scalable approach for discovering conserved active subnetworks across species.

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