jtGWAS (version 1.5.1)

jtGWAS-package: Efficient Jonckheere-Terpstra Test Statistics

Description

The core of this his Rcpp-based package is a function to compute standardized Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, e.g. genome-wide analysis. It implements OpenMP, allowing the option of computing on multiple threads. Supporting functions are also provided to calculate p-values and summarize results.

Details

Package:	jtGWAS
Type:	Package
Version:	1.5.1
Date:	2017-08-14
License:	GPL-3

Please see the example function calls below, or refer to the individual function documentation or the included vignette for more information. The package vignette serves as a tutorial for using this package. The technical details are provided in the reference cited below. Specifically, the calculation of the standardized test statistic employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.

References

Hollander, M. and Wolfe, D. A. (1999) Nonparametric Statistical Methods. New York: Wiley, 2nd edition.

See Also

Rcpp

Examples

# NOT RUN {
# Generate dummy data	
num_patient <- 100
num_marker  <- 10
num_SNP     <- 500
set.seed(12345)
X_pat_mak <- matrix(rnorm(num_patient*num_marker), num_patient, num_marker)
G_pat_SNP <- matrix(rbinom(num_patient*num_SNP, 2, 0.5), num_patient, num_SNP)
colnames(X_pat_mak) <- colnames(X_pat_mak, do.NULL=FALSE, prefix="Mrk:")
colnames(G_pat_SNP) <- colnames(G_pat_SNP, do.NULL=FALSE, prefix="SNP:")
   
res <- jtGWAS(X_pat_mak, G_pat_SNP, outTopN=15)
res
# }