Welcome to the NEO software page

Correspondence: Jason E. Aten, Steve Horvath

 

Reference

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. April 15.

The Network Edge Orienting (NEO) method and software addresses the challenge of inferring unconfounded and directed gene networks by integrating microarray-derived gene expression data with genetic marker data and Structural Equation Model (SEM) comparison. The NEO software implements several manual and automatic methods for building multi-marker QTL to create directed networks. Networks are oriented by considering each edge separately, thus reducing error propagation. To summarize the genetic evidence in favor of a given edge orientation, we propose several edge orienting scores: the Local SEM-based Edge Orienting (LEO) score compares the fit of several competing causal graphs; the correlation-based edge orienting scores are fast approximations to the LEO scores. SEM fitting indices allow the user to assess local and overall model fit. The NEO software allows the user to carry out a robustness analysis with regard to genetic marker selection. We demonstrate NEO in both simulation and in application to the relationship between a disease gene (Cidec or Fsp27) and a weight-related gene co-expression module in liver.

The NEO software can be used to orient the edges of gene co-expression networks or quantitative trait networks if the edges can be anchored by significant QTL. R software tutorials, data, and supplementary material can be downloaded below.

Talk (ppt slides) and (pdf version)

R Software, Tutorials and Data

  1. Network Edge Orienting (NEO) Software, by Jason E. Aten. NEO is written in the R language for statistical computing. The following file (updatedneo.txt) works with recent versions of R. The original version of NEO was written for R-2.5.1. You will see warnings if you use it in recent R versions. The original NEO codebase can be downloaded from (neo.txt.zip), (neo.txt.gz), or raw (neo.txt).
  2. Written documents - please cite these if you use NEO in your work.
    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. April 15.
    2. Jason's Ph.D. Dissertation, which contains all technical details on NEO. Aten, Jason Erik. Causal not Confounded Gene Networks: Inferring Acyclic and Non-acyclic Gene Bayesian Networks in mRNA Expression Studies using Recursive V-Structures, Genetic Variation, and Orthogonal Causal Anchor Structural Equation Models. 2008. Ph.D. dissertation, UCLA Department of Biomathematics.
  3. Tutorials
    1. MiniTutorial.on.NEO2.doc
    2. GeneScreeningMiniTutorial2.doc
    3. NEOTutorialMultiEdgeSimulation .doc
    4. Insig1Tutorial. GeneIdentification.doc
    5. Tutorial_for_Statclub_Insig1_11October2007_slim.doc
  4. Methods to Reproduce Results in the paper, and additional studies not in the paper. (These are for archival/results reproduction rather than instructional purposes.)
    1. Methods. NEO.Simulate.Robustness.doc
    2. Methods .NEO. Null.Model.simulations.doc
    3. Methods. Robustness.of.Insig1.Fdft1.Dhcr7.male.female.doc
    4. Methods. Compare.Power.Single.vs.Orthomarker.doc
    5. Methods.Tutorial. confirm.Insig1.in.males.doc
    6. Methods. Insig1.doc
    7. Methods.Insig1. Supplement.doc
  5. Data sets. These are used in the Methods documents below.
    1. liver. 1146snps.23388mrna.21clinical.bxh.apoe.null.rdat.zip
    2. liver .snps.23388genes.clinical.bxh.male.and.female.rdat.zip
    3. bxd.fsp27.blue.df.imp.rdat.zip
    4. bxd.fsp27.blue.df.rdat.zip
    5. insig1_genes_and_blue_module_30oct2007.zip
    6. insig1. complete.validation.genes.bxd.set.imputed.rdat.zip
    7. insig1.bxd. downstream.robust.results.rdat.zip

The following references have used NEO.

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

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

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.

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