Network Analysis Tools From Biological Networks To Clusters And Pathways Pdf

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The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks e. However, biological systems often present more than two biological states e.

Protocol DOI: Protein—protein interaction networks PPIs collect information on physical—and in some cases—functional interactions between proteins. Most PPIs are annotated with confidence scores , which reflect the probability that a reported interaction is a. Most PPIs are annotated with confidence scores , which reflect the probability that a reported interaction is a true interaction.

PyGNA: a unified framework for geneset network analysis

The study of interactions among biological components can be carried out by using methods grounded on network theory. Most of these methods focus on the comparison of two biological networks e. However, biological systems often present more than two biological states e. To compare two or more networks simultaneously, we developed BioNetStat , a Bioconductor package with a user-friendly graphical interface.

BioNetStat compares correlation networks based on the probability distribution of a feature of the graph e. The analysis of the structural alterations on the network reveals significant modifications in the system.

For example, the analysis of centrality measures provides information about how the relevance of the nodes changes among the biological states.

We evaluated the performance of BioNetStat in both, toy models and two case studies. The latter related to gene expression of tumor cells and plant metabolism. Also, besides being able to identify nodes with modified centralities, BioNetStat identified altered networks associated with signaling pathways that were not identified by other methods.

In the last two decades, the high-dimensional data production, such as metabolomics, proteomics, transcriptomics, and genomics, increased considerably Zhu et al. It brings out the high complexity of the biological systems, posing the challenge to understand how they work. In science, it is fundamental to compare the many states assumed by a system, such as sick against healthy patients or developmental stages of a living being.

A range of strategies can be applied for comparing different states depending on the study hypothesis, such as the t- test to compare two means , the analysis of variance—ANOVA to compare two or more means de Souza et al. However, none of these methods takes into account the relationship among several biological components at the same time.

Biological systems can be assessed by correlation networks, in which the nodes represent the elements variables and edges represent the statistical relations among its elements.

Some approaches have been proposed to qualitatively analyze the correlation networks by performing a visual inspection of their structure Caldana et al. However, these studies do not apply statistical tests or formal control of false positives.

Over the last years, several tools have been developed to statistically test whether correlation networks are different across conditions. DiffCoEx Tesson et al. Here we focus on the last group, in which the tests are performed for each predefined group of variables. Although several biological studies compare more than two networks Caldana et al. However, only GSCA performs tests for predefined groups of variables.

GSCA builds correlations matrices and compares the biological condition networks by using Euclidean distance Choi and Kendziorski, Pairwise comparison between the networks obtains the GSCA generalization for comparing more than two networks.

However, this strategy, in general, gives an inadequate control of type I error Fujita et al. Besides, since the network structure may vary over time and also across systems from the same biological class, searching for precisely similar structures between two graphs is not an effective strategy to compare the behavior of biological pathways Santos et al.

This tool is specific for datasets containing several graphs in each biological condition. GANOVA is not useful when only one network is available per condition, such as in the case of physiological or genes correlations networks. Here we combined the methods proposed by Santos et al. BioNetStat is available at Bioconductor and includes a graphical user interface.

We performed simulation experiments and applied the proposed method in two biological data sets. We propose a method for comparing simultaneously two or more biological correlation networks. In the following subsections, we explain the construction of correlation networks graphs , the structural graph analysis, and the statistical test performed by BioNetStat. A correlation network is an undirected graph, where each node corresponds to a biological variable, and each edge connects a pair of nodes indicating the association between two variables.

In our context, the edge corresponds to the statistical dependence between two variables. To measure and detect monotonic relations, BioNetStat includes the Pearson , Spearman , and Kendall correlation coefficients. Given a measure of statistical dependence, BioNetStat provides three scales of association degree: the absolute correlation coefficient, one minus the p -value of the dependence test, and one minus the p -value adjusted by the False Discovery Rate method Benjamini and Hochberg, Each association degree is a real number varying from zero to one.

The user can choose between unweighted zero or one and weighted network values from zero to one. Zero means no monotonic association between variables, while one means a monotonic association between them. To construct a graph, the user can choose a threshold for edges insertion, based on some association measure correlation or p -value of the independence test.

The proposed method is based on graph topological features. In the following sections, we describe how BioNetStat performs the comparisons based in the Probability Distribution of a Feature of the Graph PDFG , in the vector of some network centrality, and in each node centrality measure.

A random graph G is a graph generated by a random process. In the last decades, several random graph models have been proposed for studying biological networks. For example, Barabasi and Albert proposed the scale-free model, in which a few nodes have many connections hubs and many nodes present a lower number of connections Jeong et al. An example where to which the scale-free model suits well is in the representation of the protein-protein interactions networks, in which only a few essential proteins interact with many others and are central to metabolism, whereas many proteins display lower numbers of interactions because they participate in a few specific metabolic pathways.

We want to test whether the r graphs G 1 , G 2 , …, G r each one representing a state were generated by the same random graph model. In case the PDFG are different, it would be assumed that the graphs were generated by different random graph models. As will be seen next, here we analyzed correlation networks in which the elements correspond to variables such as genes, proteins, metabolites, and phenotypic variables.

Examples of states include different treatments or conditions. An alteration in the structure of the network, detected by a change in the PDFG, could mean that a healthy human cell may be turning into a tumor cell or the tumor tissue might be entering in a new degree of aggressiveness.

The PDFG is the probability density function of some topological feature x and has n v elements x 1 , x 2 , …, x n v. Examples of topological features are the set of eigenvalues of the adjacency matrix of the graph, or graph centrality measures. Formally, the PDFG g is defined as:. In real systems, the PDFG is unknown. The user can choose between the Sturges' Sturges, and the Silverman's Silverman, criteria to define the Kernel bandwidth for the Gaussian Kernel estimator.

In the analyses performed in this work, we used the Sturges' criterion. The differential network analysis is a comparison between two or more graphs based on their PDFG. Calculate the average PDFG as:. The KL divergence measures the discrepancy between two probability distributions. For graphs, we can use the KL divergence to select the graph model that best describes the observed graph or to discriminate PDFGs Takahashi et al.

Formally, we define the KL divergence between graphs as follows. As in section 2. The aim is to test if the centrality values of r graphs G 1 , G 2 , …, G r , of each state, are the same among all graphs. BioNetStat considers five node centrality measures, namely degree, eigenvector, closeness, betweenness, and clustering coefficient, and one edge centrality edge betweenness. This, such a node may be involved in numerous biological processes. The eigenvector centrality of a node is proportional to the centralities of its neighbors weighted by the strength of the connections Bonacich, That is, a node is progressively more important as it connects with higher numbers of strongly connected neighbors nodes.

The closeness and betweenness centralities are related to the shortest paths in the network Rubinov and Sporns, The closeness centrality measures the average proximity of a node to all other nodes Freeman, The betweenness centrality measures the importance of a node in the communication of the network.

It counts how many shortest paths pass through the node Freeman, The clustering coefficient quantifies how connected the neighbors of a node are Watts and Strogatz, Finally, the edge betweenness centrality is similar to the betweenness centrality for nodes Girvan and Newman, It quantifies how many shortest paths pass through an edge, measuring its importance in the communication of the network.

The mathematical definitions of these six measures are shown in the Table S5. Our tool, therefore, affords evaluation of data by assessing: i importance of a node in relation to the entire population of nodes in the network; ii proximity among nodes; iii importance of a node in the communication within the network, and iv the connectivity strength of the network as a whole. The differential analysis consists of the same steps described in section 2. In the same way that was done in section 2.

The differential node analysis consists in similar steps as in section 2. Construct r new graphs by resampling the observations without replacement.

Repeat steps 2 and 3 until obtaining the desired number of permutation replications. BioNetStat receives two files as input. One is the Biological samples file , with the pre-processed data, containing the values of the variables e. This file must be a table, in which the columns indicate the variables and rows indicate the biological samples. At least one of these columns should indicate the label of rows e. A second file, variable set file , contains the pre-defined set of variables e.

For differential network analysis , presented in sections 2. An example of the output is shown in Supplementary Data Sheet 1. If the user performs the node differential analysis section 2.

BioNetStat also includes a visual inspection of alterations in the correlation networks heatmaps of the adjacency matrices.

It also includes a list of the differences in the pairwise correlations, a table of variable set properties e. This functionality allows the user to visualize the gene expression, the concentration of proteins and metabolites, and the centrality of nodes at the KEGG pathway maps. The BioNetStat pipeline is summarized in Figure 1. For a detailed tutorial and manual, we refer the user to the Bioconductor page: doi: Figure 1.

Schematic diagram of BioNetStat.

PyGNA: a unified framework for geneset network analysis

Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better.

Networks in biology can appear complex and difficult to decipher. We illustrate how to interpret biological networks with the help of frequently used visualization and analysis patterns. Networks represent relationships. In a biological context, many different types of relationships can be measured, such as physical interactions between proteins or genetic interactions revealed by combinations of mutations. When large collections of diverse relationships are generated from several different high-throughput experimental analyses of a single biological system, network visualization and analysis can prove particularly useful 1 — 3. To illustrate how data visualized as a network can be easier to interpret than long lists of proteins, interactions and correlations, we analyze an example network representing the yeast chromosome maintenance and duplication machinery Fig.

To browse Academia. Skip to main content. By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Download Free PDF. Network Analysis Tools: from biological networks to clusters and pathways Nature protocols,

Brohée, S. et al. NeAT: a toolbox for the analysis of biological networks, clusters, classes and pathways. Nucleic Acids Res. 36, W–W .

How to visually interpret biological data using networks

The Bader Lab is involved in a number of collaborative open-source bioinformatics projects designed to make biological pathway data easy to visualize and analyze. Additional features are available as plugins. Plugins are available for network and molecular profiling analyses, new layouts, additional file format support and connection with databases.

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Javascript is currently disabled in your browser. Several features of this site will not function whilst javascript is disabled. Received 27 January Published 4 June Volume Pages 11— Review by Single anonymous peer review.

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  1. Bevis C.

    Network Analysis Tools: from biological networks to clusters and pathways. September ; Nature Protocols 3(10)

  2. Lihuel G.

    Network Analysis Tools: from biological networks to clusters and pathways. Karoline Faust. INTRODUCTIONThis is the last article in a series of four protocols for.

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