Journal ArticleDOI
A General Framework for Weighted Gene Co-Expression Network Analysis
Bin Zhang,Steve Horvath +1 more
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
A gene ontology inferred from molecular networks
Janusz Dutkowski,Michael Kramer,Michal A. Surma,Michal A. Surma,Rama Balakrishnan,J. Michael Cherry,Nevan J. Krogan,Nevan J. Krogan,Trey Ideker +8 more
TL;DR: It is shown that large networks of gene and protein interactions in Saccharomyces cerevisiae can be used to infer an ontology whose coverage and power are equivalent to those of the manually curated Gene Ontology (GO).
Journal ArticleDOI
A molecular signature of depression in the amygdala
Etienne Sibille,Yingjie Wang,B.S. Jennifer Joeyen-Waldorf,B.S. Chris Gaiteri,Alexandre Surget,B.S. Sunghee Oh,Catherine Belzung,George C. Tseng,David A. Lewis +8 more
TL;DR: These studies demonstrate that the biological liability to major depression is reflected in a persistent molecular pathology that affects the amygdala, and support the hypothesis of maladaptive changes in this brain region as a putative primary pathology in major depression.
Journal ArticleDOI
RNA Sequencing of Laser-Capture Microdissected Compartments of the Maize Kernel Identifies Regulatory Modules Associated with Endosperm Cell Differentiation
Junpeng Zhan,Dhiraj Thakare,Chuang Ma,Alan M Lloyd,Neesha M. Nixon,Angela M. Arakaki,William J. Burnett,Kyle O. Logan,Dongfang Wang,Xiangfeng Wang,Gary N. Drews,Ramin Yadegari +11 more
TL;DR: Gene coexpression network analysis identified coexpression modules associated with single or multiple kernel compartments including modules for the endosperm cell types, some of which showed enrichment of previously identified temporally activated and/or imprinted genes.
Posted ContentDOI
Gene Expression Elucidates Functional Impact of Polygenic Risk for Schizophrenia
Menachem Fromer,Panos Roussos,Solveig K. Sieberts,Jessica S. Johnson,David H. Kavanagh,Thanneer M. Perumal,Douglas M. Ruderfer,Edwin C. Oh,Aaron Topol,Hardik Shah,Lambertus Klei,Robin Kramer,Dalila Pinto,Zeynep H. Gümüş,A. Ercument Cicek,Kristen K. Dang,Andrew W. Browne,Cong Lu,Li Xie,Ben Readhead,Eli A. Stahl,Mahsa Parvisi,Tymor Hamamsy,John F. Fullard,Ying-Chih Wang,Milind Mahajan,Jonathan M. J. Derry,Joel T. Dudley,Scott E. Hemby,Benjamin A. Logsdon,Konrad Talbot,Towfique Raj,David A. Bennett,Philip L. De Jager,Jun Zhu,Bin Zhang,Patrick F. Sullivan,Andrew Chess,Shaun Purcell,Leslie A. Shinobu,Lara M. Mangravite,Hiroyoshi Toyoshiba,Raquel E. Gur,Chang-Gyu Hahn,David A. Lewis,Vahram Haroutonian,Mette A. Peters,Barbara K. Lipska,Joseph D. Buxbaum,Eric E. Schadt,Keisuke Hirai,Kathryn Roeder,Kristen J. Brennand,Nicholas Katsanis,Enrico Domenici,Bernie Devlin,Pamela Sklar +56 more
TL;DR: Co-expression analyses identify a gene module that shows enrichment for genetic associations and is thus relevant for schizophrenia, paving the way for mechanistic interpretations of genetic liability for schizophrenia and other brain diseases.
Journal ArticleDOI
Integrative genomics reveals novel molecular pathways and gene networks for coronary artery disease.
Ville-Petteri Mäkinen,Mete Civelek,Qingying Meng,Bin Zhang,Jun Zhu,Candace Levian,Tianxiao Huan,Ayellet V. Segrè,Sujoy Ghosh,Juan C. Vivar,Majid Nikpay,Alexandre F.R. Stewart,Christopher P. Nelson,Christina Willenborg,Jeanette Erdmann,Stefan Blakenberg,Christopher J. O'Donnell,Winfried März,Reijo Laaksonen,Stephen E. Epstein,Sekar Kathiresan,Svati H. Shah,Stanley L. Hazen,Muredach P. Reilly,Aldons J. Lusis,Nilesh J. Samani,Heribert Schunkert,Thomas Quertermous,Ruth McPherson,Xia Yang,Themistocles L. Assimes +30 more
TL;DR: The results suggest genetic influences on a diverse set of both known and novel biological processes that contribute to CAD risk, and highlight potential novel targets for further mechanistic studies and therapeutic interventions.
References
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Finding Groups in Data: An Introduction to Cluster Analysis
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