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DESCRIPTION 100644 1 kb
NAMESPACE 100644 1 kb 100644 3 kb
# GeneGeneInteR The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multi-dimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. ## Installation To install and load the package in R ```ruby library(devtools) install_github("MathieuEmily/GeneGeneInteR") library(GeneGeneInteR) ``` ## A detailed example Importation of genotypes with ImportFile function Supported format are pedfile, PLINK, VCF (4.0) file, or genotypes imputed by IMPUTE2. ```ruby #### Example of ped format with 17 genes ped <- system.file("extdata/example.ped", package="GeneGeneInteR") info <- system.file("extdata/", package="GeneGeneInteR") posi <- system.file("extdata/example.txt", package="GeneGeneInteR") dta <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t") ## Importation of the phenotype resp <- system.file("extdata/response.txt", package="GeneGeneInteR") Y <- read.csv(resp, header=FALSE) ``` Prior to the statistical analysis, dataset can be modified by applying filters to the SNPs (snpMatrixScour function) or by imputing missing genotypes (imputeSnpMatrix function). A subset of genes can also be selected with the select.snps function. ```ruby ## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 are removed. No filtering is applied regarding missing data (call.rate=1). data <- snpMatrixScour(data$snpX, = data$, min.maf = 0.05, min.eq = 1e-3, call.rate = 1) ## Imputation of the missing genotypes dta <- imputeSnpMatrix(data$snpX,data$ ## Selection of a subset of 12 genes dta <- selectSnps(dta$snpX, dta$, c("bub3","CDSN","Gc","GLRX","PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1")) ``` Gene-based gene-gene interaction analysis can be performed by testing each pair of genes in the datatset (function GGI). 10 methods are implemented in the GeneGeneInteR package to test a pair of genes: - 6 Gene-Gene multidimensional methods - Principal Components Analysis - **PCA** - Canonical Correlation Analysis - **CCA** - Kernel Canonical Correlation Analysis - **KCCA** - Composite Linkage Disequilibrium - **CLD** - Partial Least Square Path Modeling - **PLSPM** - Gene-Based Information Gain Method - **GBIGM** - 4 Gene-Gene interaction methods based on SNP-SNP interaction testing: - Minimum p-value test - **minP** - Gene Association Test using Extended Simes procedure - **GATES** - Truncated Tail Strength test - **tTS** - Truncated p-value Product test - **tProd** . ```ruby ## Testing for all pair of genes with the CLD method GGI.res <- GGI(Y=Y, snpX=dta$snpX,$,method="CLD") ``` Visualization of the results can be performed through either a matrix display (GGI.plot) or a network output ( ```ruby ## Plot of the results with default values plot(GGI.res) ## Plot of the results with a threshold and an ordering of the genes. plot(GGI.res,threshold=0.1,hclust.order=TRUE) ## Example of network with default threshold 0.05 plot(GGI.res,method="network") ## Example of network with threshold 0.01 where genes with no interaction are not plotted (plot.nointer=FALSE) plot(GGI.res,threshold=0.1,plot.nointer=FALSE,method="network") ```