CoexpressionNetwork

 

Welcome to the Weighted Gene Co-Expression Network Page

Weighted Gene Co-expression Network Analysis ( WGCNA )

University of California, Los Angeles

 

Gene Network Team Members (Pictures)

Steve Horvath, Chaochao (Ricky) Cai, Jun Dong, Tova Fuller, Peter Langfelder, Wen Lin, Michael Mason, Jeremy Miller, Mike Oldham, Anja Presson, Lin Song, Kellen Winden, Yafeng Zhang

Former Members

Jason Aten, Marc Carlson, Sud Doss, Anatole Ghazalpour, Chi-ying Lee, Ai Li, Chris Plaisier, Moira Regelson, Lin Wang, Andy Yip, Bin Zhang, Wei Zhao

Correspondence:

shorvath@mednet.ucla.edu

https://www.biostat.ucla.edu/people/horvath

 

CONTENTS

 Keywords: Gene Coexpression Network, Gene
Co-expression Network, Module.

 Overview of WGCNA

 Book on weighted network analysis

 Workshop material

 Software implementation of WGCNA

  1. WGCNA is available as a comprehensive package for R environment.
    This package implements the newest, most powerful and efficient network methods. Recommended for all R users.

  2. WGCNA is also available as a point-and-click application.Unfortunately this application is not maintained anymore. It is known to have compatibility problems with R-2.8.x and newer, and the methods it implements are not all state of the art. We recommend using the above R package within the R
    environment.

 

Theory Papers

  1. Bin Zhang and Steve Horvath (2005) “A General Framework for Weighted Gene Co-Expression Network Analysis”, Statistical Applications in Genetics and Molecular Biology: Vol. 4: No. 1, Article 17 PMID: 16646834

  1. “Connectivity, Module-Conformity, and Significance: Understanding Gene Co-Expression Network Methods” by Jun Dong and Steve Horvath

  1. Link to report and code: https://horvath.genetics.ucla.edu/html/ModuleConformity/

  2. Dong J, Horvath S (2007) Understanding Network Concepts in Modules, BMC Systems Biology 2007, 1:24 PMID: 17547772 PMCID: PMC3238286

  3. Horvath S, Dong J (2008) Geometric Interpretation of Gene Coexpression Network Analysis. PLoS Comput Biol
    4(8): e1000117 PMID: 18704157 PMCID: PMC2446438

  1. Yip A, Horvath S (2007) Gene network interconnectedness and the generalized topological overlap measure. BMC Bioinformatics 2007,
    8:22 PMID: 17250769 PMCID: PMC1797055

  1. Li A, Horvath S (2006) Network Neighborhood Analysis with the multi-node topological overlap measure. Bioinformatics. doi:10.1093/bioinformatics/btl581 PMID: 17110366

  • Description: Software for carrying out neighborhood analysis based on topological overlap. The paper shows that an initial seed neighborhood comprised of 2 or more highly interconnected genes (high TOM, high connectivity) yields superior results. It also shows that topological overlap is superior to
    correlation when dealing with expression data.

  • Link to report and code: https://horvath.genetics.ucla.edu/html/MTOM/

  • Link to paper: Bioinformatics

  • Link to talk: PowerPoint  PDF

  1. Langfelder P, Zhang B, Horvath S (2007) Defining clusters from a hierarchical cluster tree: the
    Dynamic Tree Cut library for R. Bioinformatics. November/btm563PMID: 18024473

  1. Langfelder P, Horvath S (2007) Eigengene networks for studying the relationships between co-expression modules. BMC Systems Biology
    2007, 1:54 PMID: 18031580

  1. Aten JE, Fuller TF, Lusis AJ, Horvath S (2008)
    Using genetic markers to orient the edges in quantitative trait networks: the NEO software. BMC Systems Biology 2008, 2:34 PMID: 18412962 PMCID: PMC2387136

  1. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559 PMID: 19114008 PMCID: PMC2631488

  1. Langfelder P, Luo R, Oldham MC, Horvath S (2011) Is my network module preserved and reproducible? PloS Comp Biol. 7(1): e1001057 PMID: 21283776 PMCID: PMC3024255

  1. Miller JA, Cai C, Langfelder P, Geschwind DH, Kurian SM, Salomon DR,
    Horvath S (2011) Strategies for aggregating gene expression data: The collapseRows R function. BMC Bioinformatics12:322. PMID: 21816037, PMCID:
    PMC3166942

  1. Langfelder P, Horvath S (2012)
    Fast R Functions for Robust Correlations and Hierarchical Clustering
    Journal of Statistical Software 46(11) 1-17 PMID: 23050260 PMCID: PMC3465711

  1. Oldham MC, Langfelder P, Horvath S (2012) Network methods for describing sample relationships in genomic datasets: application to Huntington’s disease. BMC Syst Biol. 2012 Jun 12;6(1):63. PMID: 22691535 46(11) 1-17


  1. Song L, Langfelder P, Horvath S. (2012) Comparison of co-expression measures: mutual information, correlation, and model based indices.BMC Bioinformatics;13(1):328. PMID: 23217028
    46(11) 1-17


  1. Song L, Langfelder P, Horvath S (2013) Random generalized linear model: a highly accurate and interpretable ensemble predictor. BMC Bioinformatics 14:5 PMID: 23323760 DOI: 10.1186/1471-2105-14-5.

  1. Ranola JM, Langfelder P, Lange K, Horvath S Cluster and propensity based approximation of a network. BMC Syst Biol. 2013 Mar 14;7(1):21 PMID: 23497424


  • Description: Since correlation networks are based on pairwise correlation coefficients, they exhibit a topological structure characterized by the following keyword: factorizable modules, module eigengenes, and ME based connectivity kME (see Horvath and Dong 2008 and chapter 6 in Horvath 2011). In Ranola (2013), we show that this correlation network based structure can also be generalized to other networks. This allows us to generalize correlation based techniques to general networks, e.g. we show how to carry out significance tests for individual edges in general networks.

  • Link to paper:
    BMC Systems Biology

  • Link to report and code:
    https://horvath.genetics.ucla.edu/html/PropClust

  1. Langfelder P, Mischel PS, Horvath S (2013) When Is Hub Gene Selection Better than Standard Meta-Analysis? PLoS ONE 8(4): e61505. doi:10.1371/journal.pone.0061505 PMID: PMCID: PMC3629234


  • Description: While network methods and systems biologic approaches are intuitive, few studies have investigated whether network methods are superior to standard biostatistical techniques when the latter are applicable. Here we discuss when correlation network methods are superior to standard meta analysis techniques in case of multiple genomic data sets.

  • Link to paper:
    PloS One

  • Link to report and code:
    http://genetics.ucla.edu/labs/horvath/CoexpressionNetwork/MetaAnalysis/

 

Applied Papers

  1. Horvath S, Zhang B, Carlson M, Lu KV, Zhu S, Felciano RM,
    Laurance MF, Zhao W, Shu, Q, Lee Y, Scheck AC, Liau LM, Wu H, Geschwind DH, Febbo PG, Kornblum HI, Cloughesy TF, Nelson SF, Mischel PS (2006)
    “Analysis of Oncogenic Signaling Networks in Glioblastoma Identifies ASPM as a Novel Molecular Target”, PNAS | November 14, 2006 | vol. 103 | no. 46 | 17402-17407

  1. Yeast Network Application. “Gene
    Connectivity, Function, and Sequence Conservation: Predictions from
    Modular Yeast Co-Expression Networks” (2006)  by  Carlson MRJ, Zhang B, Fang Z, Mischel PS, Horvath S, and Nelson SF, BMC Genomics 2006, 7:40

 

  1. Ghazalpour A, Doss S, Zhang B, Wang S, Plaisier C, Castellanos R, Brozell A, Schadt EE,
    Drake TA, Lusis AJ, Horvath S (2006) “Integrating Genetic and Network Analysis to Characterize Genes Related to Mouse Weight”. PLoS Genetics. Volume 2 | Issue 8 | AUGUST 2006

  • General description: How to integrate SNP markers into weighted gene co-expression network analysis? These 2 papers (with Fuller etc, 2007)
    outline how SNP markers and co-expression networks can be used to screen for gene expressions underlying a complex trait. They also illustrate the use of the module eigengene based connectivity measure kME.

  • Description: Single network analysis

  • Link to https://horvath.genetics.ucla.edu/html/
    CoexpressionNetwork/MouseWeight/

  • Link to paper: PLoS Genetics

  • Link to talk: PowerPoint  PDF

  1. Fuller TF, Ghazalpour A, Aten JE, Drake TA, Lusis AJ, Horvath S (2007) “Weighted Gene Co-expression Network Analysis Strategies Applied to Mouse Weight”, Mamm Genome. 18(6):463-472

  1. “Identification of inflammatory gene modules based on variations of
    human endothelial cell responses to oxidized lipids.” (2006) by Peter S. Gargalovic, Minori Imura, Bin Zhang, Nima M. Gharavi, Michael J. Clark, Joanne Pagnon, Wen-Pin Yang, Aiqing He, Amy Truong, Shilpa
    Patel , Stanley F. Nelson , Steve Horvath, Judith A. Berliner, Todd G. Kirchgessner, and Aldons J. Lusis

  • Description: This application presents a ‘supervised’ gene co-expression network analysis. In general, we prefer to construct a co-expression network and associated modules without
    regard to an external microarray sample trait (unsupervised WGCNA). But if thousands of genes are differentially expressed, one can construct a
    network on the basis of differentially expressed genes (supervised WGCNA)

  • Link to
    https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/InflammatoryModule

  • Link to paper: PNAS
    Webpage      PNAS PDF

  1. MC Oldham, S Horvath, DH Geschwind (2006) Conservation and evolution of gene co-expression networks in human and chimpanzee brain.
    PNAS.

  • Description: This paper presents a differential co-expression network analysis. It studies module preservation between two networks. By screening for genes with differential topological
    overlap, we identify biologically interesting genes. The paper also shows the value of summarizing a module by its module eigengene.

  • Link to https://horvath.genetics.ucla.edu/html/CoexpressionNetwork
    /HumanChimp/

  • Link to paper: PNAS</p >

  • Link to talk: PDF

  1. Gong KW, Zhao W, Li N, Barajas B, Kleinman M, Sioutas C, Horvath S, Lusis AJ, Nel A, Arauj JA (2007) Air-pollutant
    chemicals and oxidized lipids exhibit genome-wide synergistic effects on endothelial cells. Genome Biology 2007, 8:R149doi

  1. Jeremy A. Miller, Michael C. Oldham, and Daniel H. Geschwind (2008) A Systems Level Analysis of Transcriptional Changes
    in Alzheimer’s Disease and Normal Aging. J. Neurosci. 28: 1410-1420

  1. Chen Y, Zhu J, Lum PY, Yang X, Pinto S, MacNeil DJ, Zhang C, Lamb J, Edwards S, Sieberts SK, Leonardson A, Castellini LW, Wang S, Champy MF, Zhang B, Emilsson V, Doss S,
    Ghazalpour A, Horvath S, Drake TA, Lusis AJ, Schadt EE. Variations in DNA elucidate molecular networks that cause disease. Nature. 2008 Mar 27;452(7186):429-35.

  1. Oldham MC, Konopka G, Iwamoto K, Langfelder P, Kato T, Horvath S, Geschwind DH (2008) Functional organization of the transcriptome in human brain.
    Nature Neuroscience. October 12

  • Description: This is the first comprehensive analysis of gene coexpression relationships in human cerebral cortex, caudate nucleus and cerebellum. The results demonstrate that the transcriptomes
    of human brain regions are robustly organized into modules of coexpressed genes that reflect the underlying cellular composition of brain tissue. This article highlights the value of WGCNA for annotating
    genes with regard to coexpression module membership. Toward this end, it makes use of fuzzy module membership measures which are highly related to intramodular connectivity (Dong and Horvath 2008). The fuzzy module
    membership measures (and intramodular connectivity) can be used i) to determine whether a gene is close to one or more modules, ii) to determine whether a module is preserved across data, iii) and to find differentially connected genes. The paper also demonstrates the use of of dynamic tree cutting for module detection and the use of eigengene
    networks to describe relationships between coexpression modules.

  • Link to https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/HumanBrainTranscriptome/

  • Link to paper: Nature Neuroscience

  • Link to talk:  
    PowerPoint      PDF

  1. Presson AP, Sobel EM , Papp JC , Suarez CJ , Whistler T, Rajeevan MS, Vernon SD, Horvath S (2008) Integrated weighted gene co-expression network analysis
    with an application to chronic fatigue syndrome. BMC Systems Biology 2008, 2:95

 

  1. van Nas A, Guhathakurta D, Wang SS, Yehya N, Horvath S, Zhang B, Ingram-Drake L, Chaudhuri G, Schadt EE, Drake TA, Arnold AP, Lusis AJ (2008) Elucidating the Role of Gonadal
    Hormones in Sexually Dimorphic Gene Co-Expression Networks. Endocrinology. 2008 Oct 30

  1. Hu S, Zhou M, Jiang J, Wang J, Elashoff D, Gorr S, Michie SA, Spijkervet FK, Bootsma H, KallenbergCG, Vissink A, Horvath S, Wong DT (2008) Systems biology analysis of
    sjögren’s syndrome andmucosa-associated lymphoid tissue lymphoma in parotid glands. Arthritis Rheum. 2008 Dec 30;60(1):81-92

  1. Maclennan NK, Dong J, Aten JE, Horvath S, Rahib L, Ornelas L, Dipple KM, McCabe ER (2009)Weighted gene co-expression network analysis identifies biomarkers
    in glycerol kinase deficient mice.Mol Genet Metab. 2009 May 27

  1. Farber CR, Aten JE, Farber EA, de Vera V, Gularte R, Islas-Trejo A, Wen P, Horvath S, Lucero M, Lusis AJ,Medrano JF (2009) Genetic dissection of a major mouse
    obesity QTL (Carfhg2): integration of geneexpression and causality modeling.Physiol Genomics. 2009 May 13;37(3):294-302. .

  • Description: This article provides a successful demonstration how the NEO software (Aten et al 2008) canbe used to carry out SNP marker based causal
    inference.

  • Link to paper: Physiological Genomics

  1. Li A, Horvath S (2009) Network Module Detection: Affinity Search Technique with the Multi-NodeTopological Overlap Measure. BMC Research Notes. 2009 Jul
    20;2:142

  • Description: This article describe an alternative clustering method for detecting network modules basedon the multi-node topological overlap measure. In general, we prefer module detection
    based onhierachical clustering but this paper shows that the new method (MAST) can have superior performance whendealing with clusters that were
    simulated around seeds.

  • Link to paper: BMC Research Notes

  • Link to additional information and code: MTOM page

  1. Mason MJ, Fan G, Plath K, Zhou Q, Horvath S (2009)Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonicstem cells BMC Genomics
    2009, 10:327.

  • Description: This article uses signed WGCNA to analyze multiple stem cell data. It also regresses modulemembership (kME) on epigenetic variables and transcription factor binding information.
    Analysis ofvariance allows us to determine what proportion of variation in kME is due to epigenetic regulators.

  • Link to paper: BMC Genomics

  • Link to code and data:https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/SignedNetwork/

  1. Winden KD, Oldham MC, Mirnics K, Ebert PJ, Swan CH, Levitt P, Rubenstein JL, Horvath S, Geschwind DH. (2009)The organization of the transcriptional network in
    specific neuronal classes. Mol Syst Biol. 2009;5:291. PMID: 19638972

  • Here, we perform the first systems-level analysis of microarray data from single neuronal populations using weighted gene co-expression network
    analysis to examine how neuronal transcriptome organization relates to neuronal function and diversity. We systematically validate network predictions (including those based on module eigengene based connectivity k.ME) using published genomic data and we validate network
    predictions in vivo using Rgs4 and Dlx1/2 knockout mice. We have also included two resources for further exploring the network data set, including a gene neighborhood explorer tool (MultiTOM) and a table with calculated k.ME values for all genes in all modules.

  • Link to paper: Molecular Systems Biology

  1. Saris CG, Horvath S, van Vught PW, van Es MA, Blauw HM, Fuller TF, Langfelder P, Deyoung J, Wokke JH, Veldink JH, van den Berg LH, Ophoff RA (2009)
    Weighted gene co-expression network analysis of the peripheral blood from Amyotrophic Lateral Sclerosis patients. BMC Genomics. 2009 Aug 27;10(1):405. PMID: 19712483

  • In this first large-scale blood gene expression study in ALS, two large co-expression modules were found to be associated with ALS (Lou Gehrig’s disease). Ingenuity Pathway Analysis demonstrates that
    a module based analysis leads to more significant functional enrichment results than a standard analysis based on differential analysis. This paper also illustrates the use of network screening for finding
    biomarker candidates.

  • Link to paper: BMC Genomics

  • Link to project page:

    http://genetics.ucla.edu/labs/horvath/CoexpressionNetwork/ALS/

  1. Plaisier CL, Horvath S, Huertas-Vazquez A, Cruz-Bautista I, Herrera MF, Tusie-Luna T, Aguilar-Salinas C, Pajukanta P (2009) A systems genetics approach
    implicates USF1, FADS3 and other causal candidate genes for familial combined hyperlipidemia. PloS Genetics;5(9):e1000642

  • Blockwise module detection was used to find co-expression modules based on human adipose samples. Corresponding eigengengenes were correlated with i) physiologic traits and ii) a
    disease related SNP (close to the transcription factor USF1).This led to the identification of a USF1 related module that relates to triglyceride levels and hyperlipidemia (FCHL). Next causal testing with the NEO
    software was used to determine whether the module eigengenes and intramodular hub genes are causal for triglyceride levels and FCHL. The resulting causal candidate genes were validated by relating them to
    corresponding local (cis-acting) SNP markers from a GWAS study.

  • Link to paper: PLoS Genetics

  1. de Jong S, Fuller TF, Janson E, Strengman E, Horvath S, Kas MJ, Ophoff RA (2010) Gene expression profiling in C57BL/6J and A/J mouse inbred strains
    reveals gene networks specific for brain regions independent of genetic background. BMC Genomics. 2010 Jan 11;11(1):20

  1. Miller, J.A, Horvath, S., and Geschwind, D.H. (2010) Divergence of human and mouse brain transcriptome highlights Alzheimer disease pathways. PNAS. 2010 June 25;
    Epub ahead of print. PMID: 20616000.

  1. Mumford JA, Horvath S, Oldham MC, Langfelder P, Geschwind DH, Poldrack RA (2010) Detecting network modules in fMRI time series: A weighted network analysis approach. Neuroimage. 2010 Oct 1;52(4):1465-1476. Epub 2010 May 27.PMID: 20553896.

  • Description: This article shows how weighted correlation network analysis can be used to analyze fMRI time series data.

  • Link to paper: Neuroimage

  1. Cai CC, Langfelder P, Fuller TF, Oldham MC, Luo R, van den Berg LH, Ophoff RA, Horvath S (2010) Is human blood a good surrogate for brain tissue in transcriptional studies? BMC Genomics.

  1. Dewey FE, Perez MV, Wheeler MT, Watt C, Spin J, Langfelder P, Horvath S, Hannenhalli S, Cappola TP, Ashley EA (2010) Gene Coexpression Network Topology of Cardiac Development, Hypertrophy, and Failure. Circ Cardiovasc Genet. PMID: 21127201

  1. Park CC, Gale GD, Dejong S, Ghazalpour A, Bennett B, Farber CR, Langfelder P, Lin A, Khan AH, Eskin E, Horvath S, Lusis AJ, Ophoff RA, Smith DJ (2011)
    Gene networks associated with conditional fear in mice identified using a systems genetics approach. BMC Syst Biol. PMID: 21410935

  1. Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor R, Blencowe BJ, Geschwind DH (2011) Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. PMID: 21614001

  1. Presson AP, Yoon NK, Bagryanova L, Mah V, Alavi M, Maresh EL, Rajasekaran AK, Goodglick L, Chia D, Horvath S (2011) Protein expression based multimarker analysis of breast cancer samples. BMC Cancer. PMID: 21651811

  • Link to paper: BMC Cancer

  • Description: The article demonstrates that weighted correlation network analysis applied to immunohistochemical staining (TMA) data can be used to cluster breast cancer patients into patient groups with distinct prognostic outcomes.

  1. Bocklandt S, Lin W, Sehl ME, Sanchez FJ, Sinsheimer JS, et al. 2011 Epigenetic Predictor of Age. PLoS ONE 6(6): e14821

  • Link to paper: PLoS ONE

  • Description: the article shows that weighted correlation network analysis identifies aging related modules based Illumina HumanMethylation27 microarrays of human saliva.

  1. Langfelder P, Castellani LW, Zhou Z, Paul E, Davis R, Schadt EE, Lusis AJ, Horvath S, Mehrabian M (2011) A systems genetic analysis of high density lipoprotein metabolism and network preservation across mouse models. Biochim Biophys Acta. PMID: 21807117

  1. Hilliard AT, Miller JE, Fraley ER, Horvath S, White SA (2012) Molecular microcircuitry underlies functional specification in a Basal Ganglia circuit dedicated to vocal learning. Neuron. 2012 Feb 9;73(3):537-52 PMID: 22325205

  1. Leuchter AF, Cook IA, Hunter AM, Cai C, Horvath S (2012) Resting-State Quantitative Electroencephalography Reveals Increased Neurophysiologic Connectivity in Depression. PLoS ONE 7(2): e32508. doi:10.1371

  1. De Jong S, Boks MP, Fuller TF, Strengman E, Janson E, de Kovel CG, Ori AP, Vi N, Mulder F, Blom JD, Glenthoj B, Schubart CD, Cahn W, Kahn RS, Horvath S, Ophoff RA (2012)
    A gene co-expression network in whole blood of schizophrenia patients is independent of antipsychotic-use and enriched for brain-expressed genes. PLoS ONE. 7(6):e39498. PMID: 22761806 doi:10.1371

  1. Shirasaki DI, Greiner ER, Al-Ramahi I, Gray M, Boontheung P, Geschwind DH, Botas J, Coppola G, Horvath S, Loo JA, Yang XW. (2012) Network organization of the huntingtin proteomic interactome in Mammalian brain. Neuron. 2012 Jul 12;75(1):41-57. PMID: 22794259

  1. Weiss JN, Karma A, Maclellan WR, Deng M, Rau CD, Rees CM, Wang J, Wisniewski N, Eskin E, Horvath S, Qu Z, Wang Y, Lusis AJ. (2012) “Good enough solutions” and the genetics of complex diseases. Circ Res. 2012 Aug 3;111(4):493-504. PMID: 22859671

  1. Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, van de Lagemaat LN, Smith KA, Ebbert A, Riley ZL, Abajian C, Beckmann CF, Bernard A, Bertagnolli D, Boe AF, Cartagena PM, Chakravarty MM, Chapin M, Chong J, Dalley RA, Daly BD, Dang C, Datta S, Dee N, Dolbeare TA, Faber V, Feng D, Fowler DR, Goldy J, Gregor BW, Haradon Z, Haynor DR, Hohmann JG, Horvath S, Howard RE, Jeromin A, Jochim JM, Kinnunen M, Lau C, Lazarz ET, Lee C, Lemon TA, Li L, Li Y, Morris JA, Overly CC, Parker PD, Parry SE, Reding M, Royall JJ, Schulkin J, Sequeira PA, Slaughterbeck CR, Smith SC, Sodt AJ, Sunkin SM, Swanson BE, Vawter MP, Williams D, Wohnoutka P, Zielke HR, Geschwind DH, Hof PR, Smith SM, Koch C, Grant SG, Jones AR (2012) An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012 Sep 20;489(7416):391-9. doi: 10.1038/nature11405. PMID: 22996553

  1. Horvath S, Zhang Y, Langfelder P, Kahn RS, Boks MP, van Eijk K, van den Berg LH, Ophoff RA. Aging effects on DNA methylation modules in human brain and blood tissue. Genome Biol. 2012 Oct 3;13(10):R97. PMID: 23034122

  1. Horvath S, Nazmul-Hossain AN, Pollard RP, Kroese FG, Vissink A, Kallenberg CG, Spijkervet FK, Bootsma H, Michie SA, Gorr SU, Peck AB, Cai C, Zhou H, Wong DT.(2012) Systems analysis of primary Sjogren’s syndrome pathogenesis in salivary glands identifies shared pathways in human and a mouse model. Arthritis Res Ther. 1;14(6):R238 PMID: 23116360

  1. Schwartz SM, Schwartz HT, Horvath S, Schadt E, Lee SI. A Systematic Approach to Multifactorial Cardiovascular Disease: Causal Analysis. Arterioscler Thromb Vasc Biol. 2012 Oct 18. PMID: 23087359

  1. Hilliard AT, Miller JE, Horvath S, White SA. (2012) Distinct neurogenomic States in Basal Ganglia subregions relate differently to singing behavior in songbirds. PLoS Comput Biol. 2012 Nov;8(11):e1002773. doi: 10.1371/journal.pcbi.1002773 PMID: 23144607

  1. van Eijk KR, de Jong S, Boks MP, Langeveld T, Colas F, Veldink JH, de Kovel CG, Janson E, Strengman E, Langfelder P, Kahn RS, van den Berg LH, Horvath S, Ophoff RA (2012) Genetic analysis of DNA methylation and gene expression levels in whole blood of healthy human subjects BMC Genomics. 2012 Nov 17;13(1):636 PMID: 23157493


  1. Haas BE, Horvath S, Pietiläinen KH, Cantor RM, Nikkola E, Weissglas-Volkov D, Rissanen A, Civelek M, Cruz-Bautista I, Riba L, Kuusisto J, Kaprio J, Tusie-Luna T, Laakso M, Aguilar-Salinas CA, Pajukanta P. Adipose Co-expression networks across Finns and Mexicans identify novel triglyceride-associated genes. BMC Med Genomics. 2012 Dec 6;5(1):61 PMID: 23217153


  1. Levine AJ, Miller JA, Shapshak P, Gelman B, Singer EJ, Hinkin CH, Commins D, Morgello S, Grant I, Horvath S (2013) Systems analysis of human brain gene expression: mechanisms for HIV-associated neurocognitive impairment and common pathways with Alzheimer’s disease. BMC Med Genomics. 2013 Feb 13;6(1):4 PMID: 23406646

  1. Xue Z, Huang K, Cai C, Cai L, Jiang CY, Feng Y, Liu Z, Zeng Q, Cheng L, Sun YE, Liu JY, Horvath S, Fan G. (2013)
    Genetic programs in human and mouse early embryos revealed by single-cell RNA?sequencing. Nature. 2013 Jul 28. doi: 10.1038/nature12364 PMID: 23892778

 

External Papers

Acknowledgement

2009-01-16

Please send your
suggestions and comments to: shorvath@mednet.ucla.edu</ SPAN>