scholarly journals Graph combinatorics based group-level network inference

2019 ◽  
Author(s):  
Shuo Chen ◽  
Qiong Wu ◽  
L. Elliot Hong
Keyword(s):  
Statistical Inference ◽  
Network Topology ◽  
Network Inference ◽  
Brain Regions ◽  
Superior Performance ◽  
Omics Data ◽  
Group Level ◽  
Data Set ◽  
Joint Inference ◽  
Brain Connectome

AbstractWe consider group-level statistical inference for networks, where outcomes are multivariate edge variables constrained in an adjacency matrix. The graph notation is used to represent a network, where nodes are identical biological units (e.g. brain regions) shared across subjects and edge-variables indicate the strengths of interactive relationships between nodes. Edge-variables vary across subjects and may be associated with covariates of interest. The statistical inference for multivariate edge-variables is challenging because both localized inference on individual edges and the joint inference of a combinatorial of edges (network-level) are desired. Different from conventional multivariate variables (e.g. omics data), the inference of a combinatorial of edges is closely linked with network topology and graph combinatorics. We propose a novel objective function with 𝓁0 norm regularization to robustly capture subgraphs/subnetworks from the whole brain connectome and thus reveal the latent network topology of phenotype-related edges. Our statistical inferential procedure and theories are constructed based on graph combinatorics. We apply the proposed approach to a brain connectome study to identify latent brain functional subnetworks that are associated with schizophrenia and verify the findings using an independent replicate data set. The results demonstrate that the proposed method achieves superior performance with remarkably increased replicability.

scholarly journals Biological network inference from single-cell multi-omics data using heterogeneous graph transformer

2021 ◽  
Author(s):  
Anjun Ma ◽  
Xiaoying Wang ◽  
Cankun Wang ◽  
Jingxian Li ◽  
Tong Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Single Cell ◽  
Gene Networks ◽  
Network Inference ◽  
Lung Tumor ◽  
Superior Performance ◽  
Omics Data ◽  
Learning Platform ◽  
Cell Clustering ◽  
Cell Cell

We present DeepMAPS, a deep learning platform for cell-type-specific biological gene network inference from single-cell multi-omics (scMulti-omics). DeepMAPS includes both cells and genes in a heterogeneous graph to infer cell-cell, cell-gene, and gene-gene relations simultaneously. The graph attention neural network considers a cell and a gene with both local and global information, making DeepMAPS more robust to data noises. We benchmarked DeepMAPS on 18 datasets for cell clustering and network inference, and the results showed that our method outperforms various existing tools. We further applied DeepMAPS on a case study of lung tumor leukocyte CITE-seq data and observed superior performance in cell clustering, and predicted biologically meaningful cell-cell communication pathways based on the inferred gene networks. To improve the feasibility and ensure the reproducibility of analyzing scMulti-omics data, we deployed a webserver with multi-functions and various visualizations. Overall, we valued DeepMAPS as a novel platform of the state-of-the-art deep learning model in the single-cell study and can promote the use of scMulti-omics data in the community.

scholarly journals A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 629
Author(s):  
Andrea Duggento ◽  
Gaetano Valenza ◽  
Luca Passamonti ◽  
Salvatore Nigro ◽  
Maria Giovanna Bianco ◽  
...  
Keyword(s):  
Granger Causality ◽  
Current Knowledge ◽  
Laguerre Polynomials ◽  
Brain Regions ◽  
Parameters Estimation ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Global Parameter ◽  
Brain Connectome ◽  
Human Connectome Project

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g., data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signals often present long-range dependencies which commonly require high autoregressive model orders/number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener–Volterra decompositions with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.

scholarly journals Extracting Brain Disease-Related Connectome Subgraphs by Adaptive Dense Subgraph Discovery

2020 ◽  
Author(s):  
Qiong Wu ◽  
Xiaoqi Huang ◽  
Adam Culbreth ◽  
James Waltz ◽  
Elliot Hong ◽  
...  
Keyword(s):  
False Negative ◽  
Synthetic Data ◽  
Superior Performance ◽  
Group Level ◽  
Dense Subgraph ◽  
Computationally Efficient ◽  
Whole Brain ◽  
Brain Connectome ◽  
Neuroimaging Data ◽  
Dense Subgraph Discovery

AbstractGroup-level brain connectome analysis has attracted increasing interest in neuropsychiatric research with the goal of identifying connectomic subnetworks (subgraphs) that are systematically associated with brain disorders. However, extracting disease-related subnetworks from the whole brain connectome has been challenging, because no prior knowledge is available regarding the sizes and locations of the subnetworks. In addition, neuroimaging data is often mixed with substantial noise that can further obscure informative subnetwork detection. We propose a likelihood-based adaptive dense subgraph discovery (ADSD) model to extract disease-related subgraphs from the group-level whole brain connectome data. Our method is robust to both false positive and false negative errors of edge-wise inference and thus can lead to a more accurate discovery of latent disease-related connectomic subnetworks. We develop computationally efficient algorithms to implement the novel ADSD objective function and derive theoretical results to guarantee the convergence properties. We apply the proposed approach to a brain fMRI study for schizophrenia research and identify well-organized and biologically meaningful subnetworks that exhibit schizophrenia-related salience network centered connectivity abnormality. Analysis of synthetic data also demonstrates the superior performance of the ADSD method for latent subnetwork detection in comparison with existing methods in various settings.

scholarly journals Knowledge guided multi-level network inference

2020 ◽  
Author(s):  
Christoph Ogris ◽  
Yue Hu ◽  
Janine Arloth ◽  
Nikola S. Müller
Keyword(s):  
Data Analysis ◽  
Quantitative Trait ◽  
Network Inference ◽  
Integrated Analysis ◽  
Full Potential ◽  
Omics Data ◽  
Lasso Regression ◽  
Data Set ◽  
Level Data ◽  
Multi Level

AbstractConstantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of so-called multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Few exceptions exist, for example the pairwise integration for quantitative trait analysis. However, the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. Here we propose a versatile approach, to perform a multi-level integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo. KiMONo performs network inference using statistical modeling on top of a powerful knowledge-guided strategy exploiting prior information from biological sources. Within the resulting network, nodes represent features of all input types and edges refer to associations between them, e.g. underlying a disease. Our method infers the network by combining sparse grouped-LASSO regression with a genomic position-confined Biogrid protein-protein interaction prior. In a comprehensive evaluation, we demonstrate that our method is robust to noise and still performs on low-sample size data. Applied to the five-level data set of the publicly available Pan-cancer collection, KiMONO integrated mutation, epigenetics, transcriptomics, proteomics and clinical information, detecting cancer specific omic features. Moreover, we analysed a four-level data set from a major depressive disorder cohort, including genetic, epigenetic, transcriptional and clinical data. Here we demonstrated KiMONo’s analytical power to identify expression quantitative trait methylation sites and loci and show it’s advantage to state-of-the-art methods. Our results show the general applicability to the full spectrum multi-omics data and demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets. The method is freely available as an R package (https://github.com/cellmapslab/kimono).

scholarly journals A Parsimonious Granger Causality Formulation for Capturing Arbitrarily Long Multivariate Associations

2019 ◽  
Author(s):  
Andrea Duggento ◽  
Gaetano Valenza ◽  
Luca Passamonti ◽  
Salvatore Nigro ◽  
Maria Giovanna Bianco ◽  
...  
Keyword(s):  
Granger Causality ◽  
Current Knowledge ◽  
Laguerre Polynomials ◽  
Brain Regions ◽  
Parameters Estimation ◽  
Autoregressive Models ◽  
Superior Performance ◽  
Global Parameter ◽  
Brain Connectome ◽  
Human Connectome Project

High-frequency neuroelectric signals like electroencephalography (EEG) or magnetoencephalography (MEG) provide a unique opportunity to infer causal relationships between local activity of brain areas. While causal inference is commonly performed through Classical Granger causality (GC) based on multivariate autoregressive models, this method may encounter important limitations (e.g. data paucity) in the case of high dimensional data from densely connected systems like the brain. Additionally, physiological signal often present long-range dependencies which commonly require high autoregressive model orders / number of parameters. We present a generalization of autoregressive models for GC estimation based on Wiener-Volterra decomposition with Laguerre polynomials as basis functions. In this basis, the introduction of only one additional global parameter allows to capture to capture arbitrary long dependencies without increasing model order, hence retaining model simplicity, linearity and ease of parameters estimation. We validate our method in synthetic data generated from families of complex, densely connected networks and demonstrate superior performance as compared to classical GC. Additionally, we apply our framework to studying the directed human brain connectome through MEG data from 89 subjects drawn from the Human Connectome Project (HCP) database, showing that it is able to reproduce current knowledge as well as to uncover previously unknown directed influences between cortical and limbic brain regions.

scholarly journals Communicability Characterization of Structural DWI Subcortical Networks in Alzheimer’s Disease

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 475 ◽  
Author(s):  
Eufemia Lella ◽  
Nicola Amoroso ◽  
Domenico Diacono ◽  
Angela Lombardi ◽  
Tommaso Maggipinto ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Brain Regions ◽  
Brain Network ◽  
Data Set ◽  
Brain Connectome ◽  
Subcortical Brain ◽  
Classification Framework ◽  
Subcortical Regions ◽  
Cortical Regions

In this paper, we investigate the connectivity alterations of the subcortical brain network due to Alzheimer’s disease (AD). Mostly, the literature investigated AD connectivity abnormalities at the whole brain level or at the cortex level, while very few studies focused on the sub-network composed only by the subcortical regions, especially using diffusion-weighted imaging (DWI) data. In this work, we examine a mixed cohort including 46 healthy controls (HC) and 40 AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data set. We reconstruct the brain connectome through the use of state of the art tractography algorithms and we propose a method based on graph communicability to enhance the information content of subcortical brain regions in discriminating AD. We develop a classification framework, achieving 77% of area under the receiver operating characteristic (ROC) curve in the binary discrimination AD vs. HC only using a 12 × 12 subcortical features matrix. We find some interesting AD-related connectivity patterns highlighting that subcortical regions tend to increase their communicability through cortical regions to compensate the physical connectivity reduction between them due to AD. This study also suggests that AD connectivity alterations mostly regard the inter-connectivity between subcortical and cortical regions rather than the intra-subcortical connectivity.

Properties and methods of estimation for a bivariate exponentiated Fréchet distribution

2020 ◽  
Vol 70 (5) ◽  
pp. 1211-1230
Author(s):  
Abdus Saboor ◽  
Hassan S. Bakouch ◽  
Fernando A. Moala ◽  
Sheraz Hussain
Keyword(s):  
Maximum Likelihood ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Superior Performance ◽  
Estimation Methods ◽  
Conditional Probability Density ◽  
Data Set ◽  
Fréchet Distribution ◽  
Frechet Distribution

AbstractIn this paper, a bivariate extension of exponentiated Fréchet distribution is introduced, namely a bivariate exponentiated Fréchet (BvEF) distribution whose marginals are univariate exponentiated Fréchet distribution. Several properties of the proposed distribution are discussed, such as the joint survival function, joint probability density function, marginal probability density function, conditional probability density function, moments, marginal and bivariate moment generating functions. Moreover, the proposed distribution is obtained by the Marshall-Olkin survival copula. Estimation of the parameters is investigated by the maximum likelihood with the observed information matrix. In addition to the maximum likelihood estimation method, we consider the Bayesian inference and least square estimation and compare these three methodologies for the BvEF. A simulation study is carried out to compare the performance of the estimators by the presented estimation methods. The proposed bivariate distribution with other related bivariate distributions are fitted to a real-life paired data set. It is shown that, the BvEF distribution has a superior performance among the compared distributions using several tests of goodness–of–fit.

scholarly journals Functional network topology of the right insula affects emotion dysregulation in hyperactive-impulsive attention-deficit/hyperactivity disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tammo Viering ◽  
Pieter J. Hoekstra ◽  
Alexandra Philipsen ◽  
Jilly Naaijen ◽  
Andrea Dietrich ◽  
...  
Keyword(s):  
Attention Deficit Hyperactivity Disorder ◽  
Network Topology ◽  
Resting State ◽  
Latent Variable ◽  
Emotion Dysregulation ◽  
Adult Adhd ◽  
Brain Regions ◽  
Graph Analysis ◽  
Hyperactivity Disorder ◽  
The Right

AbstractEmotion dysregulation is common in attention-deficit/hyperactivity disorder (ADHD). It is highly prevalent in young adult ADHD and related to reduced well-being and social impairments. Neuroimaging studies reported neural activity changes in ADHD in brain regions associated with emotion processing and regulation. It is however unknown whether deficits in emotion regulation relate to changes in functional brain network topology in these regions. We used a combination of graph analysis and structural equation modelling (SEM) to analyze resting-state functional connectivity in 147 well-characterized young adults with ADHD and age-matched healthy controls from the NeuroIMAGE database. Emotion dysregulation was gauged with four scales obtained from questionnaires and operationalized through a latent variable derived from SEM. Graph analysis was applied to resting-state data and network topology measures were entered into SEM models to identify brain regions whose local network integration and connectedness differed between subjects and was associated with emotion dysregulation. The latent variable of emotion dysregulation was characterized by scales gauging emotional distress, emotional symptoms, conduct symptoms, and emotional lability. In individuals with ADHD characterized by prominent hyperactivity-impulsivity, the latent emotion dysregulation variable was related to an increased clustering and local efficiency of the right insula. Thus, in the presence of hyperactivity-impulsivity, clustered network formation of the right insula may underpin emotion dysregulation in young adult ADHD.

scholarly journals The Core of the Global Corporate Network

2021 ◽  
Author(s):  
Ricardo Giglio ◽  
Thomas Lux
Keyword(s):  
Network Topology ◽  
World Wide ◽  
Data Set ◽  
The Core ◽  
Comprehensive Data ◽  
The World ◽  
Corporate Network ◽  
Board Membership ◽  
National Networks ◽  
Wide Population

AbstractWe investigate the network topology of a comprehensive data set of the world-wide population of corporate entities. In particular, we have extracted information on the boards of all companies listed in Bloomberg’s archive of company profiles in October, 2015, a total of almost 100,000 firms. We provide information on board membership overlaps at various levels, and, in particular, show that there exists a core of directors who accumulate a large number of seats and are highly connected among themselves both at the level of national networks and at the worldwide aggregated level.

scholarly journals CADD-Splice—improving genome-wide variant effect prediction using deep learning-derived splice scores

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Philipp Rentzsch ◽  
Max Schubach ◽  
Jay Shendure ◽  
Martin Kircher
Keyword(s):  
Prediction Models ◽  
Splice Variants ◽  
Superior Performance ◽  
Data Set ◽  
Pathogenic Variants ◽  
Genome Wide ◽  
Donor And Acceptor ◽  
Human Proteins ◽  
Variant Effect ◽  
Variant Effect Prediction

Abstract Background Splicing of genomic exons into mRNAs is a critical prerequisite for the accurate synthesis of human proteins. Genetic variants impacting splicing underlie a substantial proportion of genetic disease, but are challenging to identify beyond those occurring at donor and acceptor dinucleotides. To address this, various methods aim to predict variant effects on splicing. Recently, deep neural networks (DNNs) have been shown to achieve better results in predicting splice variants than other strategies. Methods It has been unclear how best to integrate such process-specific scores into genome-wide variant effect predictors. Here, we use a recently published experimental data set to compare several machine learning methods that score variant effects on splicing. We integrate the best of those approaches into general variant effect prediction models and observe the effect on classification of known pathogenic variants. Results We integrate two specialized splicing scores into CADD (Combined Annotation Dependent Depletion; cadd.gs.washington.edu), a widely used tool for genome-wide variant effect prediction that we previously developed to weight and integrate diverse collections of genomic annotations. With this new model, CADD-Splice, we show that inclusion of splicing DNN effect scores substantially improves predictions across multiple variant categories, without compromising overall performance. Conclusions While splice effect scores show superior performance on splice variants, specialized predictors cannot compete with other variant scores in general variant interpretation, as the latter account for nonsense and missense effects that do not alter splicing. Although only shown here for splice scores, we believe that the applied approach will generalize to other specific molecular processes, providing a path for the further improvement of genome-wide variant effect prediction.

Sign in / Sign up

Export Citation Format

Share Document