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
Network Edge Orienting (NEO) Software, originally
written by Jason E. Aten. NEO is written in the R language for statistical computing. The
following file contains the most recent version (which was updated by
Scott Richie and Peter Langfelder). neoDecember2015.txt
less recent version can be found here (updatedneo.txt).
It 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),
or raw (neo.txt).
Written documents - please cite these if you use NEO in
NEO software and LEO
scoring: simulation of multiple SNP phenotypes and robustness of
detection. Here we provide R code that illustrates the NEO software's
performance on simulated data for the robustness analysis.
studying the dependence of the LEO.NB scores on the number of SNPs and
SNP selection method. Here we provide R code that shows how we carried out
simulations to evaluate the LEO.NB scores.
Data set for the
robustness analysis of the "downstream of Insig1 genes" in the
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
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