3. 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 http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/MouseWeight/

• Link to paper: PLoS Genetics

• Link to talk: PowerPoint  PDF

4. 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

• Description: Differential network analysis (also see Ghazalpour etc. 2006)

• Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/DifferentialNetworkAnalysis

• Link to paper: Mammalian Genome

• Link to talk: PowerPoint  PDF

5. "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 http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/InflammatoryModule

• Link to paper: PNAS Webpage  PNAS PDF

6. 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 http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/HumanChimp/

• Link to paper: PNAS

• Link to talk: PDF

7. 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

• Link to paper: Genome Biology

8. 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

• Link to paper: Journal of Neuroscience

9. 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.

• Link to paper: Nature

10. 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 http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/HumanBrainTranscriptome/

• Link to paper: Nature Neuroscience

• Link to talk:  PowerPoint  PDF

11. 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

• Link to http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/CFS/

• Link to paper: BMC Systems Biology

12. 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

13. 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

14. 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

• Description: This article uses signed WGCNA to find modules and key genetic drivers. Signed WGCNA leadsto networks that keep track of the sign of the co-expression information. Also it uses SNP marker basedcausal testing with NEO.

• Link to paper: Molecular Genetics and Metabolism

15. 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

16. 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

17. 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:http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/SignedNetwork/

18. 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

19. 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

20. 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