Identifying network communities with a high resolution

Dimension reduction, cell clustering, and cell-cell communication inference for single-cell transcriptomics with DcjComm

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell-cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell-cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

Improved brain community structure detection by two-step weighted modularity maximization

The human brain can be regarded as a complex network with interacting connections between brain regions. Complex brain network analyses have been widely applied to functional magnetic resonance imaging (fMRI) data and have revealed the existence of community structures in brain networks. The identification of communities may provide insight into understanding the topological functions of brain networks. Among various community detection methods, the modularity maximization (MM) method has the advantages of model conciseness, fast convergence and strong adaptability to large-scale networks and has been extended from single-layer networks to multilayer networks to investigate the community structure changes of brain networks. However, the problems of MM, suffering from instability and failing to detect hierarchical community structure in networks, largely limit the application of MM in the community detection of brain networks. In this study, we proposed the weighted modularity maximization (WMM) method by using the weight matrix to weight the adjacency matrix and improve the performance of MM. Moreover, we further proposed the two-step WMM method to detect the hierarchical community structures of networks by utilizing node attributes. The results of the synthetic networks without node attributes demonstrated that WMM showed better partition accuracy than both MM and robust MM and better stability than MM. The two-step WMM method showed better accuracy of community partitioning than WMM for synthetic networks with node attributes. Moreover, the results of resting state fMRI (rs-fMRI) data showed that two-step WMM had the advantage of detecting the hierarchical communities over WMM and was more insensitive to the density of the rs-fMRI networks than WMM.

KISL: knowledge-injected semi-supervised learning for biological co-expression network modules

The exploration of important biomarkers associated with cancer development is crucial for diagnosing cancer, designing therapeutic interventions, and predicting prognoses. The analysis of gene co-expression provides a systemic perspective on gene networks and can be a valuable tool for mining biomarkers. The main objective of co-expression network analysis is to discover highly synergistic sets of genes, and the most widely used method is weighted gene co-expression network analysis (WGCNA). With the Pearson correlation coefficient, WGCNA measures gene correlation, and uses hierarchical clustering to identify gene modules. The Pearson correlation coefficient reflects only the linear dependence between variables, and the main drawback of hierarchical clustering is that once two objects are clustered together, the process cannot be reversed. Hence, readjusting inappropriate cluster divisions is not possible. Existing co-expression network analysis methods rely on unsupervised methods that do not utilize prior biological knowledge for module delineation. Here we present a method for identification of outstanding modules in a co-expression network using a knowledge-injected semi-supervised learning approach (KISL), which utilizes apriori biological knowledge and a semi-supervised clustering method to address the issue existing in the current GCN-based clustering methods. To measure the linear and non-linear dependence between genes, we introduce a distance correlation due to the complexity of the gene-gene relationship. Eight RNA-seq datasets of cancer samples are used to validate its effectiveness. In all eight datasets, the KISL algorithm outperformed WGCNA when comparing the silhouette coefficient, Calinski-Harabasz index and Davies-Bouldin index evaluation metrics. According to the results, KISL clusters had better cluster evaluation values and better gene module aggregation. Enrichment analysis of the recognition modules demonstrated their effectiveness in discovering modular structures in biological co-expression networks. In addition, as a general method, KISL can be applied to various co-expression network analyses based on similarity metrics. Source codes for the KISL and the related scripts are available online at https://github.com/Mowonhoo/KISL.git.

Community Detection in Protein-Protein Interaction Networks and Applications

The ability to identify and characterize not only the protein-protein interactions but also their internal modular organization through network analysis is fundamental for understanding the mechanisms of biological processes at the molecular level. Indeed, the detection of the network communities can enhance our understanding of the molecular basis of disease pathology, and promote drug discovery and disease treatment in personalized medicine. This work gives an overview of recent computational methods for the detection of protein complexes and functional modules in protein-protein interaction networks, also providing a focus on some of its applications. We propose a systematic reformulation of frequently adopted taxonomies for these methods, also proposing new categories to keep up with the most recent research. We review the literature of the last five years (2017-2021) and provide links to existing data and software resources. Finally, we survey recent works exploiting module identification and analysis, in the context of a variety of disease processes for biomarker identification and therapeutic target detection. Our review provides the interested reader with an up-to-date and self-contained view of the existing research, with links to state-of-the-art literature and resources, as well as hints on open issues and future research directions in complex detection and its applications.

Large-scale prediction of key dynamic interacting proteins in multiple cancers

Tracking cancer dynamic protein-protein interactions (PPIs) and deciphering their pathogenesis remain a challenge. We presented a dynamic PPIs' hypothesis: permanent and transient interactions might achieve dynamic switchings from normal cells to malignancy, which could cause maintenance functions to be interrupted and transient functions to be sustained. Based on the hypothesis, we first predicted >1400 key cancer genes (KCG) by applying PPI-express we proposed to 18 cancer gene expression datasets. We then further screened out key dynamic interactions (KDI) of cancer based on KCG and transient and permanent interactions under both conditions. Two prominent functional characteristics, "Cell cycle-related" and "Immune-related", were presented for KCG, suggesting that these might be their general characteristics. We found that, compared to permanent to transient KDI pairs (P2T) in the network, transient to permanent (T2P) have significantly higher edge betweenness (EB), and P2T pairs tending to locate intra-functional modules may play roles in maintaining normal biological functions, while T2P KDI pairs tending to locate inter-modules may play roles in biological signal transduction. It was consistent with our hypothesis. Also, we analyzed network characteristics of KDI pairs and their functions. Our findings of KDI may serve to understand and explain a few hallmarks of cancer.

Functional brain abnormalities in major depressive disorder using a multiscale community detection approach

Major depressive disorder (MDD) is a serious disease associated with abnormal brain regions, however, the interconnection between specific brain regions related to depression has not been fully explored. To solve this problem, the paper proposes a novel multiscale community detection method to compare the differences in brain regions between normal controls (NC) and MDD patients. This study adopted the Brainnetome Atlas to divide the brain into 246 regions and extract the time series of each region. The Pearson correlation was used to measure the similarity among different brain regions to conduct the brain functional network and to perform multiscale community detection. The optimal brain community structure of each group was further explored based on the modularized Qcut algorithm, normalized mutual information (NMI), and variation of information (VI). The Jaccard index was then applied to compare the abnormalities of each brain region from different community environments between the brain function networks of NC and MDD patients. The experiments revealed several abnormal brain regions between NC and MDD, including the superior frontal gyrus, middle frontal gyrus, inferior frontal gyrus, orbital gyrus, superior temporal gyrus, middle temporal gyrus, inferior temporal gyrus, posterior superior temporal sulcus, inferior parietal gyrus, precuneus, postcentral gyrus, insular gyrus, cingulate gyrus, hippocampus and basal ganglia. Finally, a new subnetwork related to cognitive function was discovered, which was composed of the island gyrus and inferior frontal gyrus. All experiments indicated that the proposed method is useful in detecting functional brain abnormalities in MDD, and it can provide valuable insights into the diagnosis and treatment of MDD.

Connecting the dots: The boons and banes of network modeling

Network modeling transforms data into a structure of nodes and edges such that edges represent relationships between pairs of objects, then extracts clusters of densely connected nodes in order to capture high-dimensional relationships hidden in the data. This efficient and flexible strategy holds potential for unveiling complex patterns concealed within massive datasets, but standard implementations overlook several key issues that can undermine research efforts. These issues range from data imputation and discretization to correlation metrics, clustering methods, and validation of results. Here, we enumerate these pitfalls and provide practical strategies for alleviating their negative effects. These guidelines increase prospects for future research endeavors as they reduce type I and type II (false-positive and false-negative) errors and are generally applicable for network modeling applications across diverse domains.

A Novel Local Community Detection Method Using Evolutionary Computation

The local community detection is a significant branch of the community detection problems. It aims at finding the local community to which a given starting node belongs. The local community detection plays an important role in analyzing the complex networks and recently has drawn much attention from the researchers. In the past few years, several local community detection algorithms have been proposed. However, the previous methods only make use of the limited local information of networks but overlook the other valuable information. In this article, we propose an evolutionary computation-based algorithm called evolutionary-based local community detection (ELCD) algorithm to detect local communities in the complex networks by taking advantages of the entire obtained information. The performance of the proposed algorithm is evaluated on both synthetic and real-world benchmark networks. The experimental results show that the proposed algorithm has a superior performance compared with the state-of-the-art local community detection methods. Furthermore, we test the proposed algorithm on incomplete real-world networks to show its effectiveness on the networks whose global information cannot be obtained.

Identification of DNA methylation patterns and biomarkers for clear-cell renal cell carcinoma by multi-omics data analysis

Background

Tumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.

Method

The discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA.

Results

Based on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.

Conclusion

The present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Network Structural Transformation-Based Community Detection with Autoencoder

In this paper, we proposed a novel community detection method based on the network structure transformation, that utilized deep learning. The probability transfer matrix of the network adjacency matrix was calculated, and the probability transfer matrix was used as the input of the deep learning network. We use a denoising autoencoder to nonlinearly map the probability transfer matrix into a new sub space. The community detection was calculated with the deep learning nonlinear transform of the network structure. The network nodes were clustered in the new space with the K-means clustering algorithm. The division of the community structure was obtained. We conducted extensive experimental tests on the benchmark networks and the standard networks (known as the initial division of communities). We tested the clustering results of the different types, and compared with the three base algorithms. The results showed that the proposed community detection model was effective. We compared the results with other traditional community detection methods. The empirical results on datasets of varying sizes demonstrated that our proposed method outperformed the other community detection methods for this task.

FSM: Fast and scalable network motif discovery for exploring higher-order network organizations

Networks exhibit rich and diverse higher-order organizational structures. Network motifs, which are recurring significant patterns of inter-connections, are recognized as fundamental units to study the higher-order organizations of networks. However, the principle of selecting representative network motifs for local motif based clustering remains largely unexplored. We present a scalable algorithm called FSM for network motif discovery. FSM is advantageous in twofold. First, it accelerates the motif discovery process by effectively reducing the number of times for subgraph isomorphism labeling. Second, FSM adopts multiple heuristic optimizations for subgraph enumeration and classification to further improve its performance. Experimental results on biological networks show that, comparing with the existing network motif discovery algorithm, FSM is more efficient on computational efficiency and memory usage. Furthermore, with the large, frequent, and sparse network motifs discovered by FSM, the higher-order organizational structures of biological networks were successfully revealed, indicating that FSM is suitable to select network representative network motifs for exploring high-order network organizations.

Detecting protein complexes based on a combination of topological and biological properties in protein-protein interaction network

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called CSC to detect protein complexes. The method is evaluated in terms of positive predictive value, sensitivity and accuracy using the datasets of the model organism, yeast and humans. CSC outperforms several other competing algorithms for both organisms. Further, we present a framework to establish the usefulness of CSC in analyzing the influence of a given disease gene in a complex topologically as well as biologically considering eight major association factors.

Protein complex finding and ranking: An application to Alzheimer's disease

Protein complexes are known to play a major role in controlling cellular activity in a living being. Identifying complexes from raw protein-protein interactions (PPIs) is an important area of research. Earlier work has been limited mostly to yeast and a few other model organisms. Such protein complex identification methods, when applied to large human PPIs often give poor performance. We introduce a novel method called ComFiR to detect such protein complexes and further rank diseased complexes based on a query disease. We have shown that it has better performance in identifying protein complexes from human PPI data. This method is evaluated in terms of positive predictive value, sensitivity and accuracy. We have introduced a ranking approach and showed its application on Alzheimer's disease.

Effect of Explicit Evaluation on Neural Connectivity Related to Listening to Unfamiliar Music

People can experience different emotions when listening to music. A growing number of studies have investigated the brain structures and neural connectivities associated with perceived emotions. However, very little is known about the effect of an explicit act of judgment on the neural processing of emotionally-valenced music. In this study, we adopted the novel consensus clustering paradigm, called binarisation of consensus partition matrices (Bi-CoPaM), to study whether and how the conscious aesthetic evaluation of the music would modulate brain connectivity networks related to emotion and reward processing. Participants listened to music under three conditions - one involving a non-evaluative judgment, one involving an explicit evaluative aesthetic judgment, and one involving no judgment at all (passive listening only). During non-evaluative attentive listening we obtained auditory-limbic connectivity whereas when participants were asked to decide explicitly whether they liked or disliked the music excerpt, only two clusters of intercommunicating brain regions were found: one including areas related to auditory processing and action observation, and the other comprising higher-order structures involved with visual processing. Results indicate that explicit evaluative judgment has an impact on the neural auditory-limbic connectivity during affective processing of music.

Connectivity patterns during music listening: Evidence for action-based processing in musicians

Musical expertise is visible both in the morphology and functionality of the brain. Recent research indicates that functional integration between multi-sensory, somato-motor, default-mode (DMN), and salience (SN) networks of the brain differentiates musicians from non-musicians during resting state. Here, we aimed at determining whether brain networks differentially exchange information in musicians as opposed to non-musicians during naturalistic music listening. Whole-brain graph-theory analyses were performed on participants' fMRI responses. Group-level differences revealed that musicians' primary hubs comprised cerebral and cerebellar sensorimotor regions whereas non-musicians' dominant hubs encompassed DMN-related regions. Community structure analyses of the key hubs revealed greater integration of motor and somatosensory homunculi representing the upper limbs and torso in musicians. Furthermore, musicians who started training at an earlier age exhibited greater centrality in the auditory cortex, and areas related to top-down processes, attention, emotion, somatosensory processing, and non-verbal processing of speech. We here reveal how brain networks organize themselves in a naturalistic music listening situation wherein musicians automatically engage neural networks that are action-based while non-musicians use those that are perception-based to process an incoming auditory stream. Hum Brain Mapp 38:2955-2970, 2017. © 2017 Wiley Periodicals, Inc.

Intrinsic functional architecture of the macaque dorsal and ventral lateral frontal cortex

Investigations of the cellular and connectional organization of the lateral frontal cortex (LFC) of the macaque monkey provide indispensable knowledge for generating hypotheses about the human LFC. However, despite numerous investigations, there are still debates on the organization of this brain region. In vivo neuroimaging techniques such as resting-state functional magnetic resonance imaging (fMRI) can be used to define the functional circuitry of brain areas, producing results largely consistent with gold-standard invasive tract-tracing techniques and offering the opportunity for cross-species comparisons within the same modality. Our results using resting-state fMRI from macaque monkeys to uncover the intrinsic functional architecture of the LFC corroborate previous findings and inform current debates. Specifically, within the dorsal LFC, we show that 1) the region along the midline and anterior to the superior arcuate sulcus is divided in two areas separated by the posterior supraprincipal dimple, 2) the cytoarchitectonically defined area 6DC/F2 contains two connectional divisions, and 3) a distinct area occupies the cortex around the spur of the arcuate sulcus, updating what was previously proposed to be the border between dorsal and ventral motor/premotor areas. Within the ventral LFC, the derived parcellation clearly suggests the presence of distinct areas: 1) an area with a somatomotor/orofacial connectional signature (putative area 44), 2) an area with an oculomotor connectional signature (putative frontal eye fields), and 3) premotor areas possibly hosting laryngeal and arm representations. Our results illustrate in detail the intrinsic functional architecture of the macaque LFC, thus providing valuable evidence for debates on its organization.NEW & NOTEWORTHY Resting-state functional MRI is used as a complementary method to invasive techniques to inform current debates on the organization of the macaque lateral frontal cortex. Given that the macaque cortex serves as a model for the human cortex, our results help generate more fine-tuned hypothesis for the organization of the human lateral frontal cortex.

Genre Complexes in Popular Music

Recent work in the sociology of music suggests a declining importance of genre categories. Yet other work in this research stream and in the sociology of classification argues for the continued prevalence of genres as a meaningful tool through which creators, critics and consumers focus their attention in the topology of available works. Building from work in the study of categories and categorization we examine how boundary strength and internal differentiation structure the genre pairings of some 3 million musicians and groups. Using a range of network-based and statistical techniques, we uncover three musical “complexes,” which are collectively constituted by 16 smaller genre communities. Our analysis shows that the musical universe is not monolithically organized but rather composed of multiple worlds that are differently structured—i.e., uncentered, single-centered, and multi-centered.

Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach

Pancreatic cancer is the leading cause of death from solid malignancies worldwide. Currently, gemcitabine is the only drug approved for treating pancreatic cancer. Developing new therapeutic drugs for this disease is, therefore, an urgent need. The C-Map project has provided a wealth of gene expression data that can be mined for repositioning drugs, a promising approach to new drug discovery. Typically, a drug is considered potentially useful for treating a disease if the drug-induced differential gene expression profile is negatively correlated with the differentially expressed genes in the target disease. However, many of the potentially useful drugs (PUDs) identified by gene expression profile correlation are likely false positives because, in C-Map, the cultured cell lines to which the drug is applied are not derived from diseased tissues. To solve this problem, we developed a combined approach for predicting candidate drugs for treating pancreatic cancer. We first identified PUDs for pancreatic cancer by using C-Map-based gene expression correlation analyses. We then applied an algorithm (Met-express) to predict key pancreatic cancer (KPC) enzymes involved in pancreatic cancer metabolism. Finally, we selected candidates from the PUDs by requiring that their targets be KPC enzymes or the substrates/products of KPC enzymes. Using this combined approach, we predicted seven candidate drugs for treating pancreatic cancer, three of which are supported by literature evidence, and three were experimentally validated to be inhibitory to pancreatic cancer celllines.

Integrated Genomic and Network-Based Analyses of Complex Diseases and Human Disease Network

A disease phenotype generally reflects various pathobiological processes that interact in a complex network. The highly interconnected nature of the human protein interaction network (interactome) indicates that, at the molecular level, it is difficult to consider diseases as being independent of one another. Recently, genome-wide molecular measurements, data mining and bioinformatics approaches have provided the means to explore human diseases from a molecular basis. The exploration of diseases and a system of disease relationships based on the integration of genome-wide molecular data with the human interactome could offer a powerful perspective for understanding the molecular architecture of diseases. Recently, subnetwork markers have proven to be more robust and reliable than individual biomarker genes selected based on gene expression profiles alone, and achieve higher accuracy in disease classification. We have applied one of these methodologies to idiopathic dilated cardiomyopathy (IDCM) data that we have generated using a microarray and identified significant subnetworks associated with the disease. In this paper, we review the recent endeavours in this direction, and summarize the existing methodologies and computational tools for network-based analysis of complex diseases and molecular relationships among apparently different disorders and human disease network. We also discuss the future research trends and topics of this promising field.

Quantitative assessment of gene expression network module-validation methods

Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.

Persistency and flexibility of complex brain networks underlie dual-task interference

Previous studies on multitasking suggest that performance decline during concurrent task processing arises from interfering brain modules. Here, we used graph-theoretical network analysis to define functional brain modules and relate the modular organization of complex brain networks to behavioral dual-task costs. Based on resting-state and task fMRI we explored two organizational aspects potentially associated with behavioral interference when human subjects performed a visuospatial and speech task simultaneously: the topological overlap between persistent single-task modules, and the flexibility of single-task modules in adaptation to the dual-task condition. Participants showed a significant decline in visuospatial accuracy in the dual-task compared with single visuospatial task. Global analysis of topological similarity between modules revealed that the overlap between single-task modules significantly correlated with the decline in visuospatial accuracy. Subjects with larger overlap between single-task modules showed higher behavioral interference. Furthermore, lower flexible reconfiguration of single-task modules in adaptation to the dual-task condition significantly correlated with larger decline in visuospatial accuracy. Subjects with lower modular flexibility showed higher behavioral interference. At the regional level, higher overlap between single-task modules and less modular flexibility in the somatomotor cortex positively correlated with the decline in visuospatial accuracy. Additionally, higher modular flexibility in cingulate and frontal control areas and lower flexibility in right-lateralized nodes comprising the middle occipital and superior temporal gyri supported dual-tasking. Our results suggest that persistency and flexibility of brain modules are important determinants of dual-task costs. We conclude that efficient dual-tasking benefits from a specific balance between flexibility and rigidity of functional brain modules.

ClusterViz: A Cytoscape APP for Cluster Analysis of Biological Network

Cluster analysis of biological networks is one of the most important approaches for identifying functional modules and predicting protein functions. Furthermore, visualization of clustering results is crucial to uncover the structure of biological networks. In this paper, ClusterViz, an APP of Cytoscape 3 for cluster analysis and visualization, has been developed. In order to reduce complexity and enable extendibility for ClusterViz, we designed the architecture of ClusterViz based on the framework of Open Services Gateway Initiative. According to the architecture, the implementation of ClusterViz is partitioned into three modules including interface of ClusterViz, clustering algorithms and visualization and export. ClusterViz fascinates the comparison of the results of different algorithms to do further related analysis. Three commonly used clustering algorithms, FAG-EC, EAGLE and MCODE, are included in the current version. Due to adopting the abstract interface of algorithms in module of the clustering algorithms, more clustering algorithms can be included for the future use. To illustrate usability of ClusterViz, we provided three examples with detailed steps from the important scientific articles, which show that our tool has helped several research teams do their research work on the mechanism of the biological networks.

The effects of chronic alcohol self-administration in nonhuman primate brain networks

BACKGROUND

Long-term alcohol abuse is associated with change in behavior, brain structure, and brain function. However, the nature of these changes is not well understood. In this study, we used network science to analyze a nonhuman primate model of ethanol self-administration to evaluate functional differences between animals with chronic alcohol use and animals with no exposure to alcohol. Of particular interest was how chronic alcohol exposure may affect the resting state network.

METHODS

Baseline resting state functional magnetic resonance imaging was acquired in a cohort of vervet monkeys. Animals underwent an induction period where they were exposed to an isocaloric maltose dextrin solution (control) or ethanol in escalating doses over three 30-day epochs. Following induction, animals were given ad libitum access to water and a maltose dextrin solution (control) or water and ethanol for 22 h/d over 12 months. Cross-sectional analyses examined region of interests in hubs and community structure across animals to determine differences between drinking and nondrinking animals after the 12-month free access period.

RESULTS

Animals were classified as lighter (<2.0 g/kg/d) or heavier drinkers (≥2.0 g/kg/d) based on a median split of their intake pattern during the 12-month ethanol free access period. Statistical analysis of hub connectivity showed significant differences in heavier drinkers for hubs in the precuneus, posterior parietal cortices, superior temporal gyrus, subgenual cingulate, and sensorimotor cortex. Heavier drinkers were also shown to have less consistent communities across the brain compared to lighter drinkers. The different level of consumption between the lighter and heavier drinking monkeys suggests that differences in connectivity may be intake dependent.

CONCLUSIONS

Animals that consume alcohol show topological differences in brain network organization, particularly in animals that drink heavily. Differences in the resting state network were linked to areas that are associated with spatial association, working memory, and visuomotor processing.

Community structure detection for overlapping modules through mathematical programming in protein interaction networks

Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership. OverMod identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply ‘hubs’, i.e. proteins with many interaction partners but with a more specific biochemical role.

Network science and the effects of music preference on functional brain connectivity: from Beethoven to Eminem

Most people choose to listen to music that they prefer or 'like' such as classical, country or rock. Previous research has focused on how different characteristics of music (i.e., classical versus country) affect the brain. Yet, when listening to preferred music--regardless of the type--people report they often experience personal thoughts and memories. To date, understanding how this occurs in the brain has remained elusive. Using network science methods, we evaluated differences in functional brain connectivity when individuals listened to complete songs. We show that a circuit important for internally-focused thoughts, known as the default mode network, was most connected when listening to preferred music. We also show that listening to a favorite song alters the connectivity between auditory brain areas and the hippocampus, a region responsible for memory and social emotion consolidation. Given that musical preferences are uniquely individualized phenomena and that music can vary in acoustic complexity and the presence or absence of lyrics, the consistency of our results was unexpected. These findings may explain why comparable emotional and mental states can be experienced by people listening to music that differs as widely as Beethoven and Eminem. The neurobiological and neurorehabilitation implications of these results are discussed.

Construction and validation of a gene co-expression network in grapevine (Vitis vinifera. L.)

Gene co-expression analysis has been widely used for predicting gene functions because genes within modules of a co-expression network may be involved in similar biological processes and exhibit similar biological functions. To detect gene relationships in the grapevine genome, we constructed a grapevine gene co-expression network (GGCN) by compiling a total of 374 publically available grapevine microarray datasets. The GGCN consisted of 557 modules containing a total of 3834 nodes with 13 479 edges. The functions of the subnetwork modules were inferred by Gene ontology (GO) enrichment analysis. In 127 of the 557 modules containing two or more GO terms, 38 modules exhibited the most significantly enriched GO terms, including 'protein catabolism process', 'photosynthesis', 'cell biosynthesis process', 'biosynthesis of plant cell wall', 'stress response' and other important biological processes. The 'response to heat' GO term was highly represented in module 17, which is composed of many heat shock proteins. To further determine the potential functions of genes in module 17, we performed a Pearson correlation coefficient test, analyzed orthologous relationships with Arabidopsis genes and established gene expression correlations with real-time quantitative reverse transcriptase PCR (qRT-PCR). Our results indicated that many genes in module 17 were upregulated during the heat shock and recovery processes and downregulated in response to low temperature. Furthermore, two putative genes, Vit_07s0185g00040 and Vit_02s0025g04060, were highly expressed in response to heat shock and recovery. This study provides insight into GGCN gene modules and offers important references for gene functions and the discovery of new genes at the module level.

Online community detection for large complex networks

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance.

Expression-based network biology identifies immune-related functional modules involved in plant defense

Background

Plants respond to diverse environmental cues including microbial perturbations by coordinated regulation of thousands of genes. These intricate transcriptional regulatory interactions depend on the recognition of specific promoter sequences by regulatory transcription factors. The combinatorial and cooperative action of multiple transcription factors defines a regulatory network that enables plant cells to respond to distinct biological signals. The identification of immune-related modules in large-scale transcriptional regulatory networks can reveal the mechanisms by which exposure to a pathogen elicits a precise phenotypic immune response.

Results

We have generated a large-scale immune co-expression network using a comprehensive set of Arabidopsis thaliana (hereafter Arabidopsis) transcriptomic data, which consists of a wide spectrum of immune responses to pathogens or pathogen-mimicking stimuli treatments. We employed both linear and non-linear models to generate Arabidopsis immune co-expression regulatory (AICR) network. We computed network topological properties and ascertained that this newly constructed immune network is densely connected, possesses hubs, exhibits high modularity, and displays hallmarks of a “real” biological network. We partitioned the network and identified 156 novel modules related to immune functions. Gene Ontology (GO) enrichment analyses provided insight into the key biological processes involved in determining finely tuned immune responses. We also developed novel software called OCCEAN (One Click Cis-regulatory Elements ANalysis) to discover statistically enriched promoter elements in the upstream regulatory regions of Arabidopsis at a whole genome level. We demonstrated that OCCEAN exhibits higher precision than the existing promoter element discovery tools. In light of known and newly discovered cis-regulatory elements, we evaluated biological significance of two key immune-related functional modules and proposed mechanism(s) to explain how large sets of diverse GO genes coherently function to mount effective immune responses.

Conclusions

We used a network-based, top-down approach to discover immune-related modules from transcriptomic data in Arabidopsis. Detailed analyses of these functional modules reveal new insight into the topological properties of immune co-expression networks and a comprehensive understanding of multifaceted plant defense responses. We present evidence that our newly developed software, OCCEAN, could become a popular tool for the Arabidopsis research community as well as potentially expand to analyze other eukaryotic genomes.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-421) contains supplementary material, which is available to authorized users.

Age-related differences in advantageous decision making are associated with distinct differences in functional community structure

Human decision making is dependent on not only the function of several brain regions but also their synergistic interaction. The specific function of brain areas within the ventromedial prefrontal cortex has long been studied in an effort to understand choice evaluation and decision making. These data specifically focus on whole-brain functional interconnectivity using the principles of network science. The Iowa Gambling Task (IGT) was the first neuropsychological task used to model real-life decisions in a way that factors reward, punishment, and uncertainty. Clinically, it has been used to detect decision-making impairments characteristic of patients with prefrontal cortex lesions. Here, we used performance on repeated blocks of the IGT as a behavioral measure of advantageous and disadvantageous decision making in young and mature adults. Both adult groups performed poorly by predominately making disadvantageous selections in the beginning stages of the task. In later phases of the task, young adults shifted to more advantageous selections and outperformed mature adults. Modularity analysis revealed stark underlying differences in visual, sensorimotor and medial prefrontal cortex community structure. In addition, changes in orbitofrontal cortex connectivity predicted behavioral deficits in IGT performance. Contrasts were driven by a difference in age but may also prove relevant to neuropsychiatric disorders associated with poor decision making, including the vulnerability to alcohol and/or drug addiction.

Survey of network-based approaches to research of cardiovascular diseases

Cardiovascular diseases (CVDs) are the leading health problem worldwide. Investigating causes and mechanisms of CVDs calls for an integrative approach that would take into account its complex etiology. Biological networks generated from available data on biomolecular interactions are an excellent platform for understanding interconnectedness of all processes within a living cell, including processes that underlie diseases. Consequently, topology of biological networks has successfully been used for identifying genes, pathways, and modules that govern molecular actions underlying various complex diseases. Here, we review approaches that explore and use relationships between topological properties of biological networks and mechanisms underlying CVDs.

Algorithms and tools for protein-protein interaction networks clustering, with a special focus on population-based stochastic methods

MOTIVATION

Protein-protein interaction (PPI) networks are powerful models to represent the pairwise protein interactions of the organisms. Clustering PPI networks can be useful for isolating groups of interacting proteins that participate in the same biological processes or that perform together specific biological functions. Evolutionary orthologies can be inferred this way, as well as functions and properties of yet uncharacterized proteins.

RESULTS

We present an overview of the main state-of-the-art clustering methods that have been applied to PPI networks over the past decade. We distinguish five specific categories of approaches, describe and compare their main features and then focus on one of them, i.e. population-based stochastic search. We provide an experimental evaluation, based on some validation measures widely used in the literature, of techniques in this class, that are as yet less explored than the others. In particular, we study how the capability of Genetic Algorithms (GAs) to extract clusters in PPI networks varies when different topology-based fitness functions are used, and we compare GAs with the main techniques in the other categories. The experimental campaign shows that predictions returned by GAs are often more accurate than those produced by the contestant methods. Interesting issues still remain open about possible generalizations of GAs allowing for cluster overlapping.

AVAILABILITY AND IMPLEMENTATION

We point out which methods and tools described here are publicly available.

CONTACT

simona.rombo@math.unipa.it

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional connectivity networks with and without global signal correction

In functional connectivity analyses in BOLD (blood oxygenation level dependent) fMRI data, there is an ongoing debate on whether to correct global signals in fMRI time series data. Although the discussion has been ongoing in the fMRI community since the early days of fMRI data analyses, this subject has gained renewed attention in recent years due to the surging popularity of functional connectivity analyses, in particular graph theory-based network analyses. However, the impact of correcting (or not correcting) for global signals has not been systematically characterized in the context of network analyses. Thus, in this work, I examined the effect of global signal correction on an fMRI network analysis. In particular, voxel-based resting-state fMRI networks were constructed with and without global signal correction. The resulting functional connectivity networks were compared. Without global signal correction, the distributions of the correlation coefficients were positively biased. I also found that, without global signal correction, nodes along the interhemisphic fissure were highly connected whereas some nodes and subgraphs around white-matter tracts became disconnected from the rest of the network. These results from this study show differences between the networks with or without global signal correction.

Fully automated protein complex prediction based on topological similarity and community structure

To understand the function of protein complexes and their association with biological processes, a lot of studies have been done towards analyzing the protein-protein interaction (PPI) networks. However, the advancement in high-throughput technology has resulted in a humongous amount of data for analysis. Moreover, high level of noise, sparseness, and skewness in degree distribution of PPI networks limits the performance of many clustering algorithms and further analysis of their interactions.

Network-based stratification of tumor mutations

Many forms of cancer have multiple subtypes with different causes and clinical outcomes. Somatic tumor genome sequences provide a rich new source of data for uncovering these subtypes but have proven difficult to compare, as two tumors rarely share the same mutations. Here we introduce network-based stratification (NBS), a method to integrate somatic tumor genomes with gene networks. This approach allows for stratification of cancer into informative subtypes by clustering together patients with mutations in similar network regions. We demonstrate NBS in ovarian, uterine and lung cancer cohorts from The Cancer Genome Atlas. For each tissue, NBS identifies subtypes that are predictive of clinical outcomes such as patient survival, response to therapy or tumor histology. We identify network regions characteristic of each subtype and show how mutation-derived subtypes can be used to train an mRNA expression signature, which provides similar information in the absence of DNA sequence.

The effects of alcohol on the nonhuman primate brain: a network science approach to neuroimaging

BACKGROUND

Animal studies have long been an important tool for basic research as they offer a degree of control often lacking in clinical studies. Of particular value is the use of nonhuman primates (NHPs) for neuroimaging studies. Currently, studies have been published using functional magnetic resonance imaging (fMRI) to understand the default-mode network in the NHP brain. Network science provides an alternative approach to neuroimaging allowing for evaluation of whole-brain connectivity. In this study, we used network science to build NHP brain networks from fMRI data to understand the basic functional organization of the NHP brain. We also explored how the brain network is affected following an acute ethanol (EtOH) pharmacological challenge.

METHODS

Baseline resting-state fMRI was acquired in an adult male rhesus macaque (n = 1) and a cohort of vervet monkeys (n = 10). A follow-up scan was conducted in the rhesus macaque to assess network variability and to assess the effects of an acute EtOH challenge on the brain network.

RESULTS

The most connected regions in the resting-state networks were similar across species and matched regions identified as the default-mode network in previous NHP fMRI studies. Under an acute EtOH challenge, the functional organization of the brain was significantly impacted.

CONCLUSIONS

Network science offers a great opportunity to understand the brain as a complex system and how pharmacological conditions can affect the system globally. These models are sensitive to changes in the brain and may prove to be a valuable tool in long-term studies on alcohol exposure.

Normalized modularity optimization method for community identification with degree adjustment

As a fundamental problem in network study, community identification has attracted much attention from different fields. Representing a seminal work in this area, the modularity optimization method has been widely applied and studied. However, this method has issues in resolution limit and extreme degeneracy and may not perform well for networks with unbalanced structures. Although several methods have been proposed to overcome these limitations, they are all based on the original idea of defining modularity through comparing the total number of edges within the putative communities in the observed network with that in an equivalent randomly generated network. In this paper, we show that this modularity definition is not suitable to analyze some networks such as those with unbalanced structures. Instead, we propose to define modularity through the average degree within the communities and formulate modularity as comparing the sum of average degree within communities of the observed network to that of an equivalent randomly generated network. In addition, we also propose a degree-adjusted approach for further improvement when there are unbalanced structures. We analyze the theoretical properties of our degree adjusted method. Numerical experiments for both artificial networks and real networks demonstrate that average degree plays an important role in network community identification, and our proposed methods have better performance than existing ones.

C-element: a new clustering algorithm to find high quality functional modules in PPI networks

Graph clustering algorithms are widely used in the analysis of biological networks. Extracting functional modules in protein-protein interaction (PPI) networks is one such use. Most clustering algorithms whose focuses are on finding functional modules try either to find a clique like sub networks or to grow clusters starting from vertices with high degrees as seeds. These algorithms do not make any difference between a biological network and any other networks. In the current research, we present a new procedure to find functional modules in PPI networks. Our main idea is to model a biological concept and to use this concept for finding good functional modules in PPI networks. In order to evaluate the quality of the obtained clusters, we compared the results of our algorithm with those of some other widely used clustering algorithms on three high throughput PPI networks from Sacchromyces Cerevisiae, Homo sapiens and Caenorhabditis elegans as well as on some tissue specific networks. Gene Ontology (GO) analyses were used to compare the results of different algorithms. Each algorithm's result was then compared with GO-term derived functional modules. We also analyzed the effect of using tissue specific networks on the quality of the obtained clusters. The experimental results indicate that the new algorithm outperforms most of the others, and this improvement is more significant when tissue specific networks are used.

Integration of cancer gene co-expression network and metabolic network to uncover potential cancer drug targets

Cell metabolism is critical for cancer cell transformation and progression. In this study, we have developed a novel method, named Met-express, that integrates a cancer gene co-expression network with the metabolic network to predict key enzyme-coding genes and metabolites in cancer cell metabolism. Met-express successfully identified a group of key enzyme-coding genes and metabolites in lung, leukemia, and breast cancers. Literature reviews suggest that approximately 33-53% of the predicted genes are either known or suggested anti-cancer drug targets, while 22% of the predicted metabolites are known or high-potential drug compounds in therapeutic use. Furthermore, experimental validations prove that 90% of the selected genes and 70% of metabolites demonstrate the significant anti-cancer phenotypes in cancer cells, implying that they may play important roles in cancer metabolism. Therefore, Met-express is a powerful tool for uncovering novel therapeutic biomarkers.

Integrative analysis using module-guided random forests reveals correlated genetic factors related to mouse weight

Complex traits such as obesity are manifestations of intricate interactions of multiple genetic factors. However, such relationships are difficult to identify. Thanks to the recent advance in high-throughput technology, a large amount of data has been collected for various complex traits, including obesity. These data often measure different biological aspects of the traits of interest, including genotypic variations at the DNA level and gene expression alterations at the RNA level. Integration of such heterogeneous data provides promising opportunities to understand the genetic components and possibly genetic architecture of complex traits. In this paper, we propose a machine learning based method, module-guided Random Forests (mgRF), to integrate genotypic and gene expression data to investigate genetic factors and molecular mechanism underlying complex traits. mgRF is an augmented Random Forests method enhanced by a network analysis for identifying multiple correlated variables of different types. We applied mgRF to genetic markers and gene expression data from a cohort of F2 female mouse intercross. mgRF outperformed several existing methods in our extensive comparison. Our new approach has an improved performance when combining both genotypic and gene expression data compared to using either one of the two types of data alone. The resulting predictive variables identified by mgRF provide information of perturbed pathways that are related to body weight. More importantly, the results uncovered intricate interactions among genetic markers and genes that have been overlooked if only one type of data was examined. Our results shed light on genetic mechanisms of obesity and our approach provides a promising complementary framework to the “genetics of gene expression” analysis for integrating genotypic and gene expression information for analyzing complex traits.

Obesity has become a perilous global epidemic that can lead to complex diseases, such as diabetes and cardiovascular diseases. Much effort has been devoted to the studies of the genetic mechanisms that pillow the manifestation of obesity. Although a large quantity of experimental data has been accumulated lately using high-throughput techniques, our understanding of genetic mechanisms of obesity is still limited. The proposed method is motivated to address three critical issues that have impeded the existing methods. The first is the curse of dimensionality in selecting a subset of genetic elements related to the traits of interest from a large number of candidates. The second is genetic multiplicity underlying non-Mendelian traits, in which multiple genes are in interplay. The third issue is the integration of data from multiple sources in light of genetic multiplicity and curse of dimensionality. Here, we propose a new method, which augments the Random Forests method with a network-based analysis, to integrate genotypic and gene expression information and identify correlated multiple genetic elements underlying mouse weight. Our results shed light on complex genetic interactions underlying obesity, which can form viable hypotheses worthy of further investigation.

M-pick, a modularity-based method for OTU picking of 16S rRNA sequences

BACKGROUND

Binning 16S rRNA sequences into operational taxonomic units (OTUs) is an initial crucial step in analyzing large sequence datasets generated to determine microbial community compositions in various environments including that of the human gut. Various methods have been developed, but most suffer from either inaccuracies or from being unable to handle millions of sequences generated in current studies. Furthermore, existing binning methods usually require a priori decisions regarding binning parameters such as a distance level for defining an OTU.

RESULTS

We present a novel modularity-based approach (M-pick) to address the aforementioned problems. The new method utilizes ideas from community detection in graphs, where sequences are viewed as vertices on a weighted graph, each pair of sequences is connected by an imaginary edge, and the similarity of a pair of sequences represents the weight of the edge. M-pick first generates a graph based on pairwise sequence distances and then applies a modularity-based community detection technique on the graph to generate OTUs to capture the community structures in sequence data. To compare the performance of M-pick with that of existing methods, specifically CROP and ESPRIT-Tree, sequence data from different hypervariable regions of 16S rRNA were used and binning results were compared.

CONCLUSIONS

A new modularity-based clustering method for OTU picking of 16S rRNA sequences is developed in this study. The algorithm does not require a predetermined cut-off level, and our simulation studies suggest that it is superior to existing methods that require specified distance levels to define OTUs. The source code is available at http://plaza.ufl.edu/xywang/Mpick.htm.

Assessing identity, redundancy and confounds in Gene Ontology annotations over time

MOTIVATION

The Gene Ontology (GO) is heavily used in systems biology, but the potential for redundancy, confounds with other data sources and problems with stability over time have been little explored.

RESULTS

We report that GO annotations are stable over short periods, with 3% of genes not being most semantically similar to themselves between monthly GO editions. However, we find that genes can alter their 'functional identity' over time, with 20% of genes not matching to themselves (by semantic similarity) after 2 years. We further find that annotation bias in GO, in which some genes are more characterized than others, has declined in yeast, but generally increased in humans. Finally, we discovered that many entries in protein interaction databases are owing to the same published reports that are used for GO annotations, with 66% of assessed GO groups exhibiting this confound. We provide a case study to illustrate how this information can be used in analyses of gene sets and networks.

AVAILABILITY

Data available at http://chibi.ubc.ca/assessGO.