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
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Journal ArticleDOI
Methylomic profiling of human brain tissue supports a neurodevelopmental origin for schizophrenia.
Ruth Pidsley,Ruth Pidsley,Joana Viana,Eilis Hannon,Helen Spiers,Claire Troakes,Safa Al-Saraj,Naguib Mechawar,Gustavo Turecki,Leonard C. Schalkwyk,Leonard C. Schalkwyk,Nicholas John Bray,Jonathan Mill,Jonathan Mill +13 more
TL;DR: Methylomic data from human fetal cortex samples indicates that schizophrenia-associated differentially methylated positions are significantly enriched for loci at which DNA methylation is dynamically altered during human fetal brain development, suggesting that epigenetic mechanisms may mediate these effects.
Journal ArticleDOI
Novel Alzheimer risk genes determine the microglia response to amyloid-β but not to TAU pathology.
Annerieke Sierksma,Ashley Lu,Renzo Mancuso,Nicola Fattorelli,Nicola Thrupp,Evgenia Salta,Jesus Zoco,David Blum,Luc Buée,Bart De Strooper,Bart De Strooper,Mark Fiers +11 more
TL;DR: It is concluded that genetic risk of AD functionally translates into different microglia pathway responses to Aβ pathology, placing AD genetic risk downstream of the amyloid pathway but upstream of TAU pathology.
Journal ArticleDOI
Environmental drivers of a microbial genomic transition zone in the ocean's interior.
Daniel R. Mende,Jessica A. Bryant,Jessica A. Bryant,Frank O. Aylward,Frank O. Aylward,John M. Eppley,Torben Nielsen,Torben Nielsen,David M. Karl,Edward F. DeLong,Edward F. DeLong +10 more
TL;DR: Metagenomic analyses reveal that microbial genomes undergo a community-wide transition in size and GC content across a narrow depth range, indicating that nutrient limitation is a major driver in marine microbial genomic and proteomic evolution.
Journal ArticleDOI
The Naive State of Human Pluripotent Stem Cells: A Synthesis of Stem Cell and Preimplantation Embryo Transcriptome Analyses
TL;DR: This work uses a systems biology approach to comprehensively assess the conservation of gene networks in naive pluripotent stem cells with preimplantation embryos and finds naive gene networks between human and mouse PSCs are not well conserved and better resemble their respective blastocysts.
References
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