Discriminating different classes of biological networks by analyzing the graphs spectra distribution

The brain's structural and functional systems, protein-protein interaction, and gene networks are examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to norms seen in the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (e.g., normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum of its adjacency matrix. Moreover, large networks generated by the same random process have the same spectrum distribution, allowing us to use it as a "fingerprint". Based on this relationship, we introduce and propose the entropy of a graph spectrum to measure the "uncertainty" of a random graph and the Kullback-Leibler and Jensen-Shannon divergences between graph spectra to compare networks. We also introduce general methods for model selection and network model parameter estimation, as well as a statistical procedure to test the nullity of divergence between two classes of complex networks. Finally, we demonstrate the usefulness of the proposed methods by applying them on (1) protein-protein interaction networks of different species and (2) on networks derived from children diagnosed with Attention Deficit Hyperactivity Disorder (ADHD) and typically developing children. We conclude that scale-free networks best describe all the protein-protein interactions. Also, we show that our proposed measures succeeded in the identification of topological changes in the network while other commonly used measures (number of edges, clustering coefficient, average path length) failed

Identification of altered gene regulatory networks in gliomas by using Gene Network Entropy Analysis

International Symposium on Tumor Biology in Kanazawa & Symposium on Drug Discoverry in Academics 2014 [DATE]: January 23(Thu)-24(Fri),2014, [Place]:Kanazawa Excel Hotel Tpkyu, Kanazawa, Japan, [Organizers]:Kanazawa Association of Tumor Biologists / Cancer Research Institute, Kanazawa Universit

Quadrature conductivity: A quantitative indicator of bacterial abundance in porous media

The abundance and growth stages of bacteria in subsurface porous media affect the concentrations and distributions of charged species within the solid-solution interfaces. Therefore, spectral induced polarization (SIP) measurements can be used to monitor changes in bacterial biomass and growth stage. Our goal was to gain a better understanding of the SIP response of bacteria present in a porous material. Bacterial cell surfaces possess an electric double layer and therefore become polarized in an electric field. We performed SIP measurements over the frequency range of 0.1–1 kHz on cell suspensions alone and cell suspensions mixed with sand at four pore water conductivities. We used Zymomonas mobilis at four different cell densities (including the background). The quadrature conductivity spectra exhibited two peaks, one around 0.05–0.10 Hz and the other around 1–10 Hz. Because SIP measurements on bacterial suspensions are typically made at frequencies greater than 1 Hz, these peaks have not been previously reported. In the bacterial suspensions in growth medium, the quadrature conductivity at peak I was linearly proportional to the density of the bacteria. For the case of the suspensions mixed with sands, we observed that peak II presented a smaller increase in the quadrature conductivity with the cell density. A comparison of the experiments with and without sand grains illustrated the effect of the porous medium on the overall quadrature conductivity response (decrease in the amplitude and shift of the peaks to the lower frequencies). Our results indicate that for a given porous medium, time-lapse SIP has potential for monitoring changes in bacterial abundance within porous media

Measuring network's entropy in ADHD: A new approach to investigate neuropsychiatric disorders

The application of graph analysis methods to the topological organization of brain connectivity has been a useful tool in the characterization of brain related disorders. However, the availability of tools, which enable researchers to investigate functional brain networks, is still a major challenge. Most of the studies evaluating brain images are based on centrality and segregation measurements of complex networks. in this study, we applied the concept of graph spectral entropy (GSE) to quantify the complexity in the organization of brain networks. in addition, to enhance interpretability, we also combined graph spectral clustering to investigate the topological organization of sub-network's modules. We illustrate the usefulness of the proposed approach by comparing brain networks between attention deficit hyperactivity disorder (ADHD) patients and the brain networks of typical developing (TD) controls. the main findings highlighted that GSE involving sub-networks comprising the areas mostly bilateral pre and post central cortex, superior temporal gyrus, and inferior frontal gyri were statistically different (p-value = 0.002) between ADHD patients and TO controls. in the same conditions, the other conventional graph descriptors (betweenness centrality, clustering coefficient, and shortest path length) commonly used to identify connectivity abnormalities did not show statistical significant difference. We conclude that analysis of topological organization of brain sub-networks based on GSE can identify networks between brain regions previously unobserved to be in association with ADHD. (C) 2013 Elsevier Inc. All rights reserved.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Pew Latin American FellowshipFed Univ ABC, Ctr Math Computat & Cognit, BR-09210170 Santo Andre, SP, BrazilPrinceton Univ, Dept Psychol, Princeton, NJ 08540 USAPrinceton Univ, Neurosci Inst, Princeton, NJ 08540 USAUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilUniv Estadual Campinas, Ctr Mol Biol & Genet Engn, BR-13083875 Campinas, SP, BrazilUniv São Paulo, Dept Comp Sci, Inst Math & Stat, BR-05508090 São Paulo, BrazilUniversidade Federal de São Paulo, Dept Psychiat, Lab Interdisciplinar Neurociencias Clin, São Paulo, BrazilWeb of Scienc

Evaluating different methods of microarray data normalization

BACKGROUND: With the development of DNA hybridization microarray technologies, nowadays it is possible to simultaneously assess the expression levels of thousands to tens of thousands of genes. Quantitative comparison of microarrays uncovers distinct patterns of gene expression, which define different cellular phenotypes or cellular responses to drugs. Due to technical biases, normalization of the intensity levels is a pre-requisite to performing further statistical analyses. Therefore, choosing a suitable approach for normalization can be critical, deserving judicious consideration. RESULTS: Here, we considered three commonly used normalization approaches, namely: Loess, Splines and Wavelets, and two non-parametric regression methods, which have yet to be used for normalization, namely, the Kernel smoothing and Support Vector Regression. The results obtained were compared using artificial microarray data and benchmark studies. The results indicate that the Support Vector Regression is the most robust to outliers and that Kernel is the worst normalization technique, while no practical differences were observed between Loess, Splines and Wavelets. CONCLUSION: In face of our results, the Support Vector Regression is favored for microarray normalization due to its superiority when compared to the other methods for its robustness in estimating the normalization curve

Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes.</p> <p>Results</p> <p>Principal Component Analysis (PCA) combined with the Maximum-entropy Linear Discriminant Analysis (MLDA) were applied in order to identify genes with the most discriminative information between normal and tumoral prostatic tissues. Data analysis was carried out using three different approaches, namely: (i) differences in gene expression levels between normal and tumoral conditions from an univariate point of view; (ii) in a multivariate fashion using MLDA; and (iii) with a dependence network approach. Our results show that malignant transformation in the prostatic tissue is more related to functional connectivity changes in their dependence networks than to differential gene expression. The MYLK, KLK2, KLK3, HAN11, LTF, CSRP1 and TGM4 genes presented significant changes in their functional connectivity between normal and tumoral conditions and were also classified as the top seven most informative genes for the prostate cancer genesis process by our discriminant analysis. Moreover, among the identified genes we found classically known biomarkers and genes which are closely related to tumoral prostate, such as KLK3 and KLK2 and several other potential ones.</p> <p>Conclusion</p> <p>We have demonstrated that changes in functional connectivity may be implicit in the biological process which renders some genes more informative to discriminate between normal and tumoral conditions. Using the proposed method, namely, MLDA, in order to analyze the multivariate characteristic of genes, it was possible to capture the changes in dependence networks which are related to cell transformation.</p

Modeling gene expression regulatory networks with the sparse vector autoregressive model

<p>Abstract</p> <p>Background</p> <p>To understand the molecular mechanisms underlying important biological processes, a detailed description of the gene products networks involved is required. In order to define and understand such molecular networks, some statistical methods are proposed in the literature to estimate gene regulatory networks from time-series microarray data. However, several problems still need to be overcome. Firstly, information flow need to be inferred, in addition to the correlation between genes. Secondly, we usually try to identify large networks from a large number of genes (parameters) originating from a smaller number of microarray experiments (samples). Due to this situation, which is rather frequent in Bioinformatics, it is difficult to perform statistical tests using methods that model large gene-gene networks. In addition, most of the models are based on dimension reduction using clustering techniques, therefore, the resulting network is not a gene-gene network but a module-module network. Here, we present the Sparse Vector Autoregressive model as a solution to these problems.</p> <p>Results</p> <p>We have applied the Sparse Vector Autoregressive model to estimate gene regulatory networks based on gene expression profiles obtained from time-series microarray experiments. Through extensive simulations, by applying the SVAR method to artificial regulatory networks, we show that SVAR can infer true positive edges even under conditions in which the number of samples is smaller than the number of genes. Moreover, it is possible to control for false positives, a significant advantage when compared to other methods described in the literature, which are based on ranks or score functions. By applying SVAR to actual HeLa cell cycle gene expression data, we were able to identify well known transcription factor targets.</p> <p>Conclusion</p> <p>The proposed SVAR method is able to model gene regulatory networks in frequent situations in which the number of samples is lower than the number of genes, making it possible to naturally infer partial Granger causalities without any <it>a priori </it>information. In addition, we present a statistical test to control the false discovery rate, which was not previously possible using other gene regulatory network models.</p