<b>networksis</b>: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling

Abstractstatnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM). The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm. The coding is optimized for speed and robustness.

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“…networksis: A package to simulate bipartite networks with fixed marginals through sequential importance sampling (Admiraal and Handcock 2007).…”

Section: Optional Packagesmentioning

confidence: 99%

statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data

Handcock¹,

Hunter²,

Butts³

et al. 2008

J. Stat. Soft.

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767

615

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“…Operatively, the software generates a huge number of networks, selects the ones having characteristics similar to the graph under analysis (i.e., degree distribution, connected components, topological conformation), and tries iteratively to optimize the generation parameters until all the created graphs have characteristics similar to those processed. This estimator is extremely useful, since it allows to create a probability distribution by which some peculiarities of the graph can be extrapolated, concerning both its intrinsic topology and specific attributes of the nodes ( Admiraal and Handcock, 2007 ). In particular, the package allows to compute simple statistics about the topology of the graph, such as the significance of the vertex clustering attitude (triangle), or the significance of the network tendency to create multiple paths between two vertices (twopath).…”

Section: Methodsmentioning

confidence: 99%

Integrating multi-omic features exploiting Chromosome Conformation Capture data

Merelli

Tordini²,

Drocco³

et al. 2015

Front. Genet.

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The representation, integration, and interpretation of omic data is a complex task, in particular considering the huge amount of information that is daily produced in molecular biology laboratories all around the world. The reason is that sequencing data regarding expression profiles, methylation patterns, and chromatin domains is difficult to harmonize in a systems biology view, since genome browsers only allow coordinate-based representations, discarding functional clusters created by the spatial conformation of the DNA in the nucleus. In this context, recent progresses in high throughput molecular biology techniques and bioinformatics have provided insights into chromatin interactions on a larger scale and offer a formidable support for the interpretation of multi-omic data. In particular, a novel sequencing technique called Chromosome Conformation Capture allows the analysis of the chromosome organization in the cell’s natural state. While performed genome wide, this technique is usually called Hi–C. Inspired by service applications such as Google Maps, we developed NuChart, an R package that integrates Hi–C data to describe the chromosomal neighborhood starting from the information about gene positions, with the possibility of mapping on the achieved graphs genomic features such as methylation patterns and histone modifications, along with expression profiles. In this paper we show the importance of the NuChart application for the integration of multi-omic data in a systems biology fashion, with particular interest in cytogenetic applications of these techniques. Moreover, we demonstrate how the integration of multi-omic data can provide useful information in understanding why genes are in certain specific positions inside the nucleus and how epigenetic patterns correlate with their expression.

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“…Therefore we randomly reshuffled the associations between drivers and regime shifts, keeping the number of links per node unchanged. Simulations were performed in the R statistical software 26 , using a Sequential Importance Sampling algorithm, in R’s networksis 27 and ergm 28 packages. The comparison between observed interactions and random data is fundamental to understand whether the co-occurrence patterns are due to sampling noise or corresponds to a real pattern.…”

Section: Network Simulationsmentioning

confidence: 99%

Regime shifts in the Anthropocene: drivers, risks, and resilience

Rocha

Peterson

Biggs

2015

Preprint

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Many ecosystems can experience regime shifts: surprising, large and persistent changes in the function and structure of ecosystems. Assessing whether continued global change will lead to further regime shifts, or has the potential to trigger cascading regime shifts has been a central question in global change policy. Addressing this issue has, however, been hampered by the focus of regime shift research on specific cases and types of regime shifts. To systematically assess the global risk of regime shifts we conducted a comparative analysis of 25 generic types of regime shifts across marine, terrestrial and polar systems; identifying their drivers, and impacts on ecosystem services. Our results show that the drivers of regime shifts are diverse and co-occur strongly, which suggests that continued global change can be expected to synchronously increase the risk of multiple regime shifts. Furthermore, many regime shift drivers are related to climate change and food production, whose links to the continued expansion of human activities makes them difficult to limit. Because many regime shifts can amplify the drivers of other regime shifts, continued global change can also be expected to increase the risk of cascading regime shifts. Nevertheless, the variety of scales at which regime shift drivers operate provides opportunities for reducing the risk of many types of regime shifts by addressing local or regional drivers, even in the absence of rapid reduction of global drivers.

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networksis: A Package to Simulate Bipartite Graphs with Fixed Marginals Through Sequential Importance Sampling

Cited by 23 publications

References 13 publications

statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data

statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data

Integrating multi-omic features exploiting Chromosome Conformation Capture data

Regime shifts in the Anthropocene: drivers, risks, and resilience

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