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.
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).
Written documents - please cite these if you use NEO in your
work.
Also contains the
BxH-ApoE-null data set that the tutorial uses.
Methods to Reproduce Results in the paper, and additional studies
not in the paper. (These are for archival/results reproduction rather
than instructional purposes.)
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.
Simulation models 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.
Application of NEO software and LEO scoring: Ingsig1 and
downstream genes - long log of analysis leading to Table with Fourteen
positive control genes for Insig1->gene, female BxH-ApoE null data.
Warning: This is a very long (~300 pages) analysis log.
Data sets. These are used in the Methods documents below.
Data set for the robustness analysis of the "downstream of
Insig1 genes" in the BxD.
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
