Selecting single cell clustering parameter values using subsampling-based robustness metrics

Background Generating and analysing single-cell data has become a widespread approach to examine tissue heterogeneity, and numerous algorithms exist for clustering these datasets to identify putative cell types with shared transcriptomic signatures. However, many of these clustering workflows rely on user-tuned parameter values, tailored to each dataset, to identify a set of biologically relevant clusters. Whereas users often develop their own intuition as to the optimal range of parameters for clustering on each data set, the lack of systematic approaches to identify this range can be daunting to new users of any given workflow. In addition, an optimal parameter set does not guarantee that all clusters are equally well-resolved, given the heterogeneity in transcriptomic signatures in most biological systems. Results Here, we illustrate a subsampling-based approach (chooseR) that simultaneously guides parameter selection and characterizes cluster robustness. Through bootstrapped iterative clustering across a range of parameters, chooseR was used to select parameter values for two distinct clustering workflows (Seurat and scVI). In each case, chooseR identified parameters that produced biologically relevant clusters from both well-characterized (human PBMC) and complex (mouse spinal cord) datasets. Moreover, it provided a simple “robustness score” for each of these clusters, facilitating the assessment of cluster quality. Conclusion chooseR is a simple, conceptually understandable tool that can be used flexibly across clustering algorithms, workflows, and datasets to guide clustering parameter selection and characterize cluster robustness.

Adaptive Variable Neighborhood Search-Based Supply Network Reconfiguration for Robustness Enhancement

Robustness of a supply network highly depends on its structure. Although structural design methods have been proposed to create supply networks with optimal robustness, a real-life supply network can be quite different from these optimal structural designs. Meanwhile, real cases such as Thailand floods and Tohoku earthquake demonstrate the vulnerability of supply networks in real life. Obviously, it is urgent to enhance the robustness of existing real-life supply networks. Thus, in this paper, a supply network reconfiguration method based on adaptive variable neighborhood search (AVNS) is proposed to enhance the structural robustness of supply networks facing both random and target disruptions. Firstly, a supply network model considering the heterogeneous roles of entities is introduced. Based on the model, two robustness metrics, Rr and Rt, are proposed to describe the tolerance of supply networks facing random and target disruptions, respectively. Then, the problem of reconfiguration-based supply network robustness enhancement is described. To solve the problem effectively and efficiently, a new heuristic based on general variable neighborhood search, namely, AVNS, is proposed. Finally, a case study based on three real-life supply networks is presented to verify the applicability and effectiveness of the proposed robustness enhancing method.

Robustness analysis in an inter-cities mobility network: modeling municipal, state and federal initiatives as failures and attacks toward SARS-CoV-2 containment

We present a robustness analysis of an inter-cities mobility complex network, motivated by the challenge of the COVID-19 pandemic and the seek for proper containment strategies. Brazilian data from 2016 are used to build a network with more than five thousand cities (nodes) and twenty-seven states with the edges representing the weekly flow of people between cities via terrestrial transports. Nodes are systematically isolated (removed from the network) either at random (failures) or guided by specific strategies (targeted attacks), and the impacts are assessed with three metrics: the number of components, the size of the giant component, and the total remaining flow of people. We propose strategies to identify which regions should be isolated first and their impact on people mobility. The results are compared with the so-called reactive strategy, which consists of isolating regions ordered by the date the first case of COVID-19 appeared. We assume that the nodes’ failures abstract individual municipal and state initiatives that are independent and possess a certain level of unpredictability. Differently, the targeted attacks are related to centralized strategies led by the federal government in agreement with municipalities and states. Removing a node means completely restricting the mobility of people between the referred city/state and the rest of the network. Results reveal that random failures do not cause a high impact on mobility restraint, but the coordinated isolation of specific cities with targeted attacks is crucial to detach entire network areas and thus prevent spreading. Moreover, the targeted attacks perform better than the reactive strategy for the three analyzed robustness metrics.

Robustness metrics for optical networks

Telecommunication networks are vulnerable towards single or simultaneous nodes/links failures, which may lead to the disruption of network areas. The failures may cause performance degradation, reduced quality of services, reduced nodes/links survivability, stability, and reliability. Therefore, it is important to measure and enhance the network robustness, via the use of robustness metrics. This paper gives an overview of several robustness metrics that are commonly used for optical networks, from the structural, centrality and functional perspectives.

A Survey on Frameworks Used for Robustness Analysis on Interdependent Networks

The analysis of network robustness tackles the problem of studying how a complex network behaves under adverse scenarios, such as failures or attacks. In particular, the analysis of interdependent networks’ robustness focuses on the specific case of the robustness of interacting networks and their emerging behaviors. This survey systematically reviews literature of frameworks that analyze the robustness of interdependent networks published between 2005 and 2017. This review shows that there exists a broad range of interdependent network models, robustness metrics, and studies that can be used to understand the behaviour of different systems under failure or attack. Regarding models, we found that there is a focus on systems where a node in one layer interacts with exactly one node at another layer. In studies, we observed a focus on the network percolation. While among the metrics, we observed a focus on measures that count network elements. Finally, for the networks used to test the frameworks, we found that the focus was on synthetic models, rather than analysis of real network systems. This review suggests opportunities in network research, such as the study of robustness on interdependent networks with multiple interactions and/or spatially embedded networks, and the use of interdependent network models in realistic network scenarios.

Bio-Inspired Human Network Design: A Multi-Currency Robustness Metric Inspired by Ecological Network Analysis

The Ecological Network Analysis (ENA) metric ecological robustness quantifies the unique balance that biological food webs have between their pathway efficiency and redundancy, enabling them to maximize their robustness to system disturbances. This robustness is a potentially desirable quality for human systems to mimic. Modeling the interactions between actors in human networks as predator-prey type exchanges (of a medium or currency rather than caloric exchanges) enables an ENA analysis. ENA has been shown to be a useful tool in improving the design of human networks because it allows the characteristics of biological networks to be mimicked. The application of these metrics is, however, limited to networks with only one flow type. Human networks are composed of many different types of flow interactions and thus a biologically-inspired indicator of total system robustness must take into account all of these interactions. This work further develops the traditional ENA ecological robustness metric to accommodate various flows between actors in multi-currency human networks. Two novel methods for quantifying multi-currency flow network robustness are introduced. The mathematical formulation for these new metrics is presented. The water network for the Kalundborg Eco-Industrial Park (EIP) is used as a case study to determine the benefits of the proposed robustness metrics. The results obtained using the single-currency robustness and the two multi-currency robustness metrics are compared using the case study. Based on the analysis of the results obtained at the system level, as well as at the sub-levels, both multi-currency metrics showed the ability to predict systems characteristics for the multi-currency Kalundborg EIP. While both of these are promising, more research regarding these metrics is needed in order to develop an elegant and comprehensive total system robustness metric.

Classification and Execution of Coupled Decision Problems in Engineering Design for Exploration of Robust Design Solutions

Decision Support Problems (DSPs) are used to model design decisions involving multiple trade-offs. In practice, such design decisions are also coupled, that is, these decisions must be modelled by identifying and addressing the influence they exert on one another. Hence, we need to classify coupled decision problems and to introduce methods for managing uncertainty for such problems. Classification of coupled decision problems allows for the development and execution of decision templates to effect design and to archive design-related knowledge on a computer. Incorporating robustness metrics allows for the exploration of robust design solutions for coupled decision problems by managing uncertainty. In this paper, we present a classification scheme for coupled decisions using DSPs, called the Decision Scenario Matrix and we illustrate its utility by solving a coupled problem using DSPs. The design of a beam to be used as a fender is used to illustrate the efficacy of the formulation of coupled problems. In the first example, we determine a robust design, that is, determine the dimensions of the fender and simultaneously design the material recognizing that the computational models are incomplete and inaccurate. In the second example, we determine robust design solutions when design decisions are coupled, that is, determine the dimensions of the fender and select the material concurrently. Our focus, in this paper, is on illustrating the efficacy of the method rather than on the results.