Learn More
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(More)
BACKGROUND Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly(More)
Autism spectrum disorder (ASD) is a common, highly heritable neurodevelopmental condition characterized by marked genetic heterogeneity. Thus, a fundamental question is whether autism represents an aetiologically heterogeneous disorder in which the myriad genetic or environmental risk factors perturb common underlying molecular pathways in the brain. Here,(More)
Neuroanatomically precise, genome-wide maps of transcript distributions are critical resources to complement genomic sequence data and to correlate functional and genetic brain architecture. Here we describe the generation and analysis of a transcriptional atlas of the adult human brain, comprising extensive histological analysis and comprehensive(More)
The enormous complexity of the human brain ultimately derives from a finite set of molecular instructions encoded in the human genome. These instructions can be directly studied by exploring the organization of the brain's transcriptome through systematic analysis of gene coexpression relationships. We analyzed gene coexpression relationships in microarray(More)
In many applications, one is interested in determining which of the properties of a network module change across conditions. For example, to validate the existence of a module, it is desirable to show that it is reproducible (or preserved) in an independent test network. Here we study several types of network preservation statistics that do not require a(More)
Induced pluripotent stem (iPS) cells can be obtained from fibroblasts upon expression of Oct4, Sox2, Klf4, and c-Myc. To understand how these factors induce pluripotency, we carried out genome-wide analyses of their promoter binding and expression in iPS and partially reprogrammed cells. We find that target genes of the four factors strongly overlap in iPS(More)
Genetic studies have identified dozens of autism spectrum disorder (ASD) susceptibility genes, raising two critical questions: (1) do these genetic loci converge on specific biological processes, and (2) where does the phenotypic specificity of ASD arise, given its genetic overlap with intellectual disability (ID)? To address this, we mapped ASD and ID risk(More)
THE MERGING OF NETWORK THEORY AND MICROARRAY DATA ANALYSIS TECHNIQUES HAS SPAWNED A NEW FIELD: gene coexpression network analysis. While network methods are increasingly used in biology, the network vocabulary of computational biologists tends to be far more limited than that of, say, social network theorists. Here we review and propose several potentially(More)