scholarly journals Community Detection in a Weighted Directed Hypergraph Representation of Cell-to-cell Communication Networks

2020 ◽  
Author(s):  
Rui Hou ◽  
Michael Small ◽  
Alistair R. R. Forrest
Keyword(s):  
Communication Networks ◽  
Community Detection ◽  
Cell Communication ◽  
Cell Types ◽  
Community Structures ◽  
Simple Graphs ◽  
Detection Algorithms ◽  
Complex Processes ◽  
Community Identification ◽  
Directed Hypergraph

AbstractCell-to-cell communication is mainly triggered by ligand-receptor activities. Through ligandreceptor pairs, cells coordinate complex processes such as development, homeostasis, and immune response. In this work, we model the ligand-receptor-mediated cell-to-cell communication network as a weighted directed hypergraph. In this mathematical model, collaborating cell types are considered as a node community while the ligand-receptor pairs connecting them are considered a hyperedge community. We first define the community structures in a weighted directed hypergraph and develop an exact community detection method to identify these communities. We then modify approximate community detection algorithms designed for simple graphs to identify the nodes and hyperedges within each community. Application to synthetic hypergraphs with known community structure confirmed that one of the proposed approximate community identification strategies, named HyperCommunity algorithm, can effectively and precisely detect embedded communities. We then applied this strategy to two organism-wide datasets and identified putative community structures. Notably the method identifies non-overlapping edge-communities mediated by different sets of ligand-receptor pairs, however node-communities can overlap.

scholarly journals Community Detection in Multiplex Networks

2021 ◽  
Vol 54 (3) ◽  
pp. 1-35
Author(s):  
Matteo Magnani ◽  
Obaida Hanteer ◽  
Roberto Interdonato ◽  
Luca Rossi ◽  
Andrea Tagarelli
Keyword(s):  
Community Detection ◽  
Experimental Evaluation ◽  
Network Models ◽  
Ground Truth ◽  
Community Structures ◽  
Multiplex Networks ◽  
Detection Algorithms ◽  
Multiplex Network ◽  
The Right ◽  
Modes Of Interaction

A multiplex network models different modes of interaction among same-type entities. In this article, we provide a taxonomy of community detection algorithms in multiplex networks. We characterize the different algorithms based on various properties and we discuss the type of communities detected by each method. We then provide an extensive experimental evaluation of the reviewed methods to answer three main questions: to what extent the evaluated methods are able to detect ground-truth communities, to what extent different methods produce similar community structures, and to what extent the evaluated methods are scalable. One goal of this survey is to help scholars and practitioners to choose the right methods for the data and the task at hand, while also emphasizing when such choice is problematic.

Detecting community structure by belonging intensity analysis of intermediate nodes

2019 ◽  
Vol 30 (11) ◽  
pp. 1950079
Author(s):  
Mengjia Shen ◽  
Dong Lv ◽  
Zhixin Ma
Keyword(s):  
Community Structure ◽  
Communication Networks ◽  
Community Detection ◽  
Real World ◽  
Intensity Analysis ◽  
Human Communication ◽  
Intermediate Node ◽  
Detection Algorithms ◽  
Interactive Behavior

Community structure is a common characteristic of complex networks and community detection is an important methodology to reveal the structure of real-world networks. In recent years, many algorithms have been proposed to detect the high-quality communities in real-world networks. However, these algorithms have shortcomings of performing calculation on the whole network or defining objective function and the number of commonties in advance, which affects the performance and complexity of community detection algorithms. In this paper, a novel algorithm has been proposed to detect communities in networks by belonging intensity analysis of intermediate nodes, named BIAS, which is inspired from the interactive behavior in human communication networks. More specifically, intermediate nodes are middlemen between different groups in social networks. BIAS algorithm defines belonging intensity using local interactions and metrics between nodes, and the belonging intensity of intermediate node in different communities is analyzed to distinguish which community the intermediate node belongs to. The experiments of our algorithm with other state-of-the-art algorithms on synthetic networks and real-world networks have shown that BIAS algorithm has better accuracy and can significantly improve the quality of community detection without prior information.

scholarly journals Predicting cell-to-cell communication networks using NATMI

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Rui Hou ◽  
Elena Denisenko ◽  
Huan Ting Ong ◽  
Jordan A. Ramilowski ◽  
Alistair R. R. Forrest
Keyword(s):  
Single Cell ◽  
Communication Networks ◽  
Cell Communication ◽  
Cost Effective ◽  
Cell Types ◽  
Sequencing Technologies ◽  
Multiple Cell ◽  
Multicellular Interactions ◽  
Autocrine Signalling ◽  
Different Cell Types

Abstract Development of high throughput single-cell sequencing technologies has made it cost-effective to profile thousands of cells from diverse samples containing multiple cell types. To study how these different cell types work together, here we develop NATMI (Network Analysis Toolkit for Multicellular Interactions). NATMI uses connectomeDB2020 (a database of 2293 manually curated ligand-receptor pairs with literature support) to predict and visualise cell-to-cell communication networks from single-cell (or bulk) expression data. Using multiple published single-cell datasets we demonstrate how NATMI can be used to identify (i) the cell-type pairs that are communicating the most (or most specifically) within a network, (ii) the most active (or specific) ligand-receptor pairs active within a network, (iii) putative highly-communicating cellular communities and (iv) differences in intercellular communication when profiling given cell types under different conditions. Furthermore, analysis of the Tabula Muris (organism-wide) atlas confirms our previous prediction that autocrine signalling is a major feature of cell-to-cell communication networks, while also revealing that hundreds of ligands and their cognate receptors are co-expressed in individual cells suggesting a substantial potential for self-signalling.

scholarly journals FunRes: resolving tissue-specific functional cell states based on a cell–cell communication network model

2020 ◽  
Author(s):  
Sascha Jung ◽  
Kartikeya Singh ◽  
Antonio del Sol
Keyword(s):  
Communication Network ◽  
Communication Networks ◽  
Cell Communication ◽  
Cell Types ◽  
Computational Method ◽  
Tissue Specific ◽  
Great Utility ◽  
A Cell ◽  
Cell Cell

Abstract The functional specialization of cell types arises during development and is shaped by cell–cell communication networks determining a distribution of functional cell states that are collectively important for tissue functioning. However, the identification of these tissue-specific functional cell states remains challenging. Although a plethora of computational approaches have been successful in detecting cell types and subtypes, they fail in resolving tissue-specific functional cell states. To address this issue, we present FunRes, a computational method designed for the identification of functional cell states. FunRes relies on scRNA-seq data of a tissue to initially reconstruct the functional cell–cell communication network, which is leveraged for partitioning each cell type into functional cell states. We applied FunRes to 177 cell types in 10 different tissues and demonstrated that the detected states correspond to known functional cell states of various cell types, which cannot be recapitulated by existing computational tools. Finally, we characterize emerging and vanishing functional cell states in aging and disease, and demonstrate their involvement in key tissue functions. Thus, we believe that FunRes will be of great utility in the characterization of the functional landscape of cell types and the identification of dysfunctional cell states in aging and disease.

scholarly journals Patient-Specific Cell Communication Networks Associate With Disease Progression in Cancer

2021 ◽  
Vol 12 ◽  
Author(s):  
David L. Gibbs ◽  
Boris Aguilar ◽  
Vésteinn Thorsson ◽  
Alexander V. Ratushny ◽  
Ilya Shmulevich
Keyword(s):  
Communication Networks ◽  
Probabilistic Method ◽  
Cell Communication ◽  
Cell Types ◽  
Tumor Stage ◽  
The Cancer Genome Atlas ◽  
Patient Specific ◽  
Specific Cell ◽  
High Level ◽  
Cell Cell

The maintenance and function of tissues in health and disease depends on cell–cell communication. This work shows how high-level features, representing cell–cell communication, can be defined and used to associate certain signaling “axes” with clinical outcomes. We generated a scaffold of cell–cell interactions and defined a probabilistic method for creating per-patient weighted graphs based on gene expression and cell deconvolution results. With this method, we generated over 9,000 graphs for The Cancer Genome Atlas (TCGA) patient samples, each representing likely channels of intercellular communication in the tumor microenvironment (TME). It was shown that cell–cell edges were strongly associated with disease severity and progression, in terms of survival time and tumor stage. Within individual tumor types, there are predominant cell types, and the collection of associated edges were found to be predictive of clinical phenotypes. Additionally, genes associated with differentially weighted edges were enriched in Gene Ontology terms associated with tissue structure and immune response. Code, data, and notebooks are provided to enable the application of this method to any expression dataset (https://github.com/IlyaLab/Pan-Cancer-Cell-Cell-Comm-Net).

scholarly journals Connecting different heart diseases through intercellular communication

Biology Open ◽  
2021 ◽  
Vol 10 (9) ◽  
Author(s):  
Tania Martins-Marques
Keyword(s):  
Communication Networks ◽  
Intercellular Communication ◽  
Heart Diseases ◽  
Clinical Manifestations ◽  
Cell Communication ◽  
Cell Types ◽  
Mediated Communication ◽  
Tunneling Nanotubes ◽  
Organelle Transfer ◽  
Heart Beating

ABSTRACT Well-orchestrated intercellular communication networks are pivotal to maintaining cardiac homeostasis and to ensuring adaptative responses and repair after injury. Intracardiac communication is sustained by cell–cell crosstalk, directly via gap junctions (GJ) and tunneling nanotubes (TNT), indirectly through the exchange of soluble factors and extracellular vesicles (EV), and by cell–extracellular matrix (ECM) interactions. GJ-mediated communication between cardiomyocytes and with other cardiac cell types enables electrical impulse propagation, required to sustain synchronized heart beating. In addition, TNT-mediated organelle transfer has been associated with cardioprotection, whilst communication via EV plays diverse pathophysiological roles, being implicated in angiogenesis, inflammation and fibrosis. Connecting various cell populations, the ECM plays important functions not only in maintaining the heart structure, but also acting as a signal transducer for intercellular crosstalk. Although with distinct etiologies and clinical manifestations, intercellular communication derailment has been implicated in several cardiac disorders, including myocardial infarction and hypertrophy, highlighting the importance of a comprehensive and integrated view of complex cell communication networks. In this review, I intend to provide a critical perspective about the main mechanisms contributing to regulate cellular crosstalk in the heart, which may be considered in the development of future therapeutic strategies, using cell-based therapies as a paradigmatic example. This Review has an associated Future Leader to Watch interview with the author.

scholarly journals CellPhoneDB v2.0: Inferring cell-cell communication from combined expression of multi-subunit receptor-ligand complexes

2019 ◽  
Author(s):  
Mirjana Efremova ◽  
Miquel Vento-Tormo ◽  
Sarah A. Teichmann ◽  
Roser Vento-Tormo
Keyword(s):  
Communication Networks ◽  
Cell Communication ◽  
Cell Types ◽  
Published Data ◽  
Data Sets ◽  
Cellular Interactions ◽  
Receptor Ligand ◽  
Link Type ◽  
Transcriptomics Data ◽  
Cell Cell

AbstractCell-cell communication mediated by receptor-ligand complexes is crucial for coordinating diverse biological processes, such as development, differentiation and responses to infection. In order to understand how the context-dependent crosstalk of different cell types enables physiological processes to proceed, we developed CellPhoneDB, a novel repository of ligands, receptors and their interactions1. Our repository takes into account the subunit architecture of both ligands and receptors, representing heteromeric complexes accurately. We integrated our resource with a statistical framework that predicts enriched cellular interactions between two cell types from single-cell transcriptomics data. Here, we outline the structure and content of our repository, the procedures for inferring cell-cell communication networks from scRNA-seq data and present a practical step-by-step guide to help implement the protocol. CellPhoneDB v2.0 is a novel version of our resource that incorporates additional functionalities to allow users to introduce new interacting molecules and reduce the time and resources needed to interrogate large datasets. CellPhoneDB v2.0 is publicly available at https://github.com/Teichlab/cellphonedb and as a user-friendly web interface at http://www.cellphonedb.org/. In our protocol, we demonstrate how to reveal meaningful biological discoveries from CellPhoneDB v2.0 using published data sets.

scholarly journals Constructing Real-Life Benchmarks for Community Detection by Rewiring Edges

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Hong-Fei Ren ◽  
Xiao-Ke Xu
Keyword(s):  
Performance Evaluation ◽  
Community Detection ◽  
Multiple Scales ◽  
Special Functions ◽  
Structural Characteristics ◽  
Real Life ◽  
Original Network ◽  
Community Structures ◽  
Functional Characteristics ◽  
Detection Algorithms

In order to make the performance evaluation of community detection algorithms more accurate and deepen our analysis of community structures and functional characteristics of real-life networks, a new benchmark constructing method is designed from the perspective of directly rewiring edges in a real-life network instead of building a model. Based on the method, two kinds of novel benchmarks with special functions are proposed. The first kind can accurately approximate the microscale and mesoscale structural characteristics of the original network, providing ideal proxies for real-life networks and helping to realize performance analysis of community detection algorithms when a real network varies characteristics at multiple scales. The second kind is able to independently vary the community intensity in each generated benchmark and make the robustness evaluation of community detection algorithms more accurate. Experimental results prove the effectiveness and superiority of our proposed method. It enables more real-life networks to be used to construct benchmarks and helps to deepen our analysis of community structures and functional characteristics of real-life networks.

scholarly journals Extracellular Vesicles in Human Oogenesis and Implantation

2019 ◽  
Vol 20 (9) ◽  
pp. 2162 ◽  
Author(s):  
Francesca Andronico ◽  
Rosalia Battaglia ◽  
Marco Ragusa ◽  
Davide Barbagallo ◽  
Michele Purrello ◽  
...  
Keyword(s):  
Extracellular Vesicles ◽  
Cell Communication ◽  
Cell Types ◽  
Female Reproduction ◽  
Reproductive Process ◽  
Complex Processes ◽  
Egg Maturation ◽  
Different Cell Types ◽  
Human Oogenesis

Reproduction, the ability to generate offspring, represents one of the most important biological processes, being essential for the conservation of the species. In mammals, it involves different cell types, tissues and organs, which, by several signaling molecules, coordinate the different events such as gametogenesis, fertilization and embryo development. In the last few years, the role of Extracellular Vesicles, as mediators of cell communication, has been investigated in every phase of these complex processes. Microvesicles and exosomes, identified in the fluid of ovarian follicles during egg maturation, are involved in communication between the developing oocyte and the somatic follicular cells. More recently, it has been demonstrated that, during implantation, Extracellular Vesicles could participate in the complex dialog between the embryo and maternal tissues. In this review, we will focus our attention on extracellular vesicles and their cargo in human female reproduction, mainly underlining the involvement of microRNAs in intercellular communication during the several phases of the reproductive process.

LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

2018 ◽  
Vol 32 (06) ◽  
pp. 1850062 ◽  
Author(s):  
Kamal Berahmand ◽  
Asgarali Bouyer
Keyword(s):  
Community Detection ◽  
High Stability ◽  
Label Propagation ◽  
Community Structures ◽  
Detection Algorithms ◽  
Propagation Algorithm ◽  
Structural And Functional Properties ◽  
Random Behavior ◽  
Wide Range ◽  
Link Strength

Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

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