Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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TLDR
A general framework for `soft' thresholding that assigns a connection weight to each gene pair is described and several node connectivity measures are introduced and provided empirical evidence that they can be important for predicting the biological significance of a gene.Abstract:
Gene co-expression networks are increasingly used to explore the system-level functionality of genes. The network construction is conceptually straightforward: nodes represent genes and nodes are connected if the corresponding genes are significantly co-expressed across appropriately chosen tissue samples. In reality, it is tricky to define the connections between the nodes in such networks. An important question is whether it is biologically meaningful to encode gene co-expression using binary information (connected=1, unconnected=0). We describe a general framework for ;soft' thresholding that assigns a connection weight to each gene pair. This leads us to define the notion of a weighted gene co-expression network. For soft thresholding we propose several adjacency functions that convert the co-expression measure to a connection weight. For determining the parameters of the adjacency function, we propose a biologically motivated criterion (referred to as the scale-free topology criterion). We generalize the following important network concepts to the case of weighted networks. First, we introduce several node connectivity measures and provide empirical evidence that they can be important for predicting the biological significance of a gene. Second, we provide theoretical and empirical evidence that the ;weighted' topological overlap measure (used to define gene modules) leads to more cohesive modules than its ;unweighted' counterpart. Third, we generalize the clustering coefficient to weighted networks. Unlike the unweighted clustering coefficient, the weighted clustering coefficient is not inversely related to the connectivity. We provide a model that shows how an inverse relationship between clustering coefficient and connectivity arises from hard thresholding. We apply our methods to simulated data, a cancer microarray data set, and a yeast microarray data set.read more
Citations
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Journal ArticleDOI
Multi-tissue analysis of co-expression networks by higher-order generalized singular value decomposition identifies functionally coherent transcriptional modules.
TL;DR: This work applies a new algorithmic approach for efficient, parameter-free and reproducible identification of network-modules simultaneously across multiple conditions to genome-scale and multi-tissue transcriptomic datasets from rats and humans and identified several common and tissue-specific subnetworks with functional significance, which were not detected by other methods.
Journal ArticleDOI
Discovering biological progression underlying microarray samples.
TL;DR: Sample Progression Discovery may be best viewed as a novel tool for synthesizing biological hypotheses because it provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression.
Journal ArticleDOI
Network-based function prediction and interactomics: the case for metabolic enzymes.
TL;DR: The state of the art in network-based function prediction is reviewed, which can be used to unravel the functions of the uncharacterized proteins accumulating in the genomic databases and some of the underlying difficulties and successes are described.
Journal ArticleDOI
Genetic Drivers of Pancreatic Islet Function.
Mark P. Keller,Daniel M. Gatti,Kathryn L. Schueler,Mary E. Rabaglia,Donnie S. Stapleton,Petr Simecek,Matthew Vincent,Sadie Allen,Aimee Teo Broman,Rhonda Bacher,Christina Kendziorski,Karl W. Broman,Brian S. Yandell,Gary A. Churchill,Alan D. Attie +14 more
TL;DR: The analysis of pancreatic islet gene expression under dietary-induced stress enabled us to identify correlated variation in groups of genes that are functionally linked to diabetes-associated physiological traits, suggesting an expected degree of concordance between diabetes- associated loci in the mouse and those found in human populations.
Journal ArticleDOI
Meta-analysis of gene coexpression networks in the post-mortem prefrontal cortex of patients with schizophrenia and unaffected controls
TL;DR: A novel network-based approach to combining coexpression data across seven postmortem human brain studies of schizophrenia provides results that converge with existing evidence from genetic and genomic studies to support an immunological link to the pathophysiology of schizophrenia.
References
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