Sub-Modular Resolution Analysis by Network Mixture Models
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Elisabetta Marras
Inferring the structure of networks usually involves the attempt of retrieving their modular organization and knowing its possible interpretation, while quantifying the involved computational complexity through the methods and algorithms to be used. In protein interactomics, it is assumed that even the most recently created interactomes are known only up to a certain degree of coverage and accuracy, due to both experimental and computational limitations. Therefore, we need to infer from the measured interactomes about real interactomes as much as we infer from samples relative to a reference population. In order to exploit additional information sources, it is common to integrate multiple omic data and pursue method fusion. Particularly after the advent of high-throughput technologies, the increased complexity of data-intensive applications has determined an important role for network inference. Consequently, advances in spectral clustering, community detection algorithms and modularity optimization methods have been proposed, according to both deterministic and probabilistic solutions. We have considered the two kinds of approaches, and applied some of the available methods to two human interactomes obtained from high-throughput small-scale experiments and mass spectrometry measurements. The main motivation of this study is refining the resolution spectrum at which protein modularity maps can be studied. First, we started by a coarse-grained interactome decomposition through core and community structures, and by applying sub-sampling to the interactome adjacency matrix. Then, we switched to stochastic methods to uncover fine-grained interactome components, and applied both variational and mixture statistical models. Lastly, we integrated our analysis with the biological validation of the retrieved modules. Overall, the proposed approach shows potential for calibrating modularity detection in protein interactomes at different resolutions.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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- Epistatic Interactions
- Testing for Gene-Gene Interaction with AMMI Models
- A Bayesian Hierarchical Model for Quantitative Real-Time PCR Data
- Informative or Noninformative Calls for Gene Expression: A Latent Variable Approach
- Detecting Genotyping Error Using Measures of Degree of Hardy-Weinberg Disequilibrium
- Optimisation of HMM Topologies Enhances DNA and Protein Sequence Modelling
- The Apportionment of Total Genetic Variation by Categorical Analysis of Variance
- Dealing with Heterogeneity between Cohorts in Genomewide SNP Association Studies
- An Empirical Bayesian Method for Estimating Biological Networks from Temporal Microarray Data
- Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment
- Asymptotic Distribution of the "Orthogonal" Quantitative Transmission Disequilibrium Test in a Structured Population: Exact Formula
- Comparing Spatial Maps of Human Population-Genetic Variation Using Procrustes Analysis
- An Internal Calibration Method for Protein-Array Studies
- Weighted-LASSO for Structured Network Inference from Time Course Data
- Trilocus Disequilibrium Analysis of Multiallelic Markers in Outcrossing Populations
- Sparse Partial Least Squares Classification for High Dimensional Data
- Reconstructability Analysis as a Tool for Identifying Gene-Gene Interactions in Studies of Human Diseases
- Sub-Modular Resolution Analysis by Network Mixture Models
- Space Oriented Rank-Based Data Integration
- The Generalized Odds Ratio as a Measure of Genetic Risk Effect in the Analysis and Meta-Analysis of Association Studies
- Network Enrichment Analysis in Complex Experiments
- Shrinkage Estimation of Effect Sizes as an Alternative to Hypothesis Testing Followed by Estimation in High-Dimensional Biology: Applications to Differential Gene Expression
- Buckley-James Boosting for Survival Analysis with High-Dimensional Biomarker Data
- A Random Coefficients Model for Regional Co-Expression Associated with DNA Copy Number
- Locating Multiple Interacting Quantitative Trait Loci with the Zero-Inflated Generalized Poisson Regression
- Classification of Genomic Sequences via Wavelet Variance and a Self-Organizing Map with an Application to Mitochondrial DNA
- Confidently Estimating the Number of DNA Replication Origins
- Generalizing Moving Averages for Tiling Arrays Using Combined P-Value Statistics
- Lasso Logistic Regression, GSoft and the Cyclic Coordinate Descent Algorithm: Application to Gene Expression Data
- Granger Causality Analysis of Human Cell-Cycle Gene Expression Profiles
- Mapping Quantitative Trait Loci in a Non-Equilibrium Population
- On the Optimal Design of Genetic Variant Discovery Studies
- On Optimal Selection of Summary Statistics for Approximate Bayesian Computation
- Assessment of LD Matrix Measures for the Analysis of Biological Pathway Association
- Optimal Tests Shrinking Both Means and Variances Applicable to Microarray Data Analysis
- The Detection of Blur in Affymetrix GeneChips
- Regression-Based Multi-Trait QTL Mapping Using a Structural Equation Model
- Spatial Clustering of Array CGH Features in Combination with Hierarchical Multiple Testing
- Predicting Patient Survival from Longitudinal Gene Expression
- Including Probe-Level Measurement Error in Robust Mixture Clustering of Replicated Microarray Gene Expression
- Reader's Reaction
- An Alternative Model of Type A Dependence in a Gene Set of Correlated Genes
- Letter to the Editor
- Permutation P-values Should Never Be Zero: Calculating Exact P-values When Permutations Are Randomly Drawn
