jtGWAS {jtGWAS} | R Documentation |
A method to compute the Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, with optional multi-threaded execution. 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.
jtGWAS(X, G, outTopN=15L, numThreads=1L, standardized=TRUE)
X |
A matrix of dependent variables, e.g. marker levels. Row names are sample IDs, and column names are marker IDs. Required. |
G |
A matrix of independent variables, e.g. SNP counts. Row names are sample IDs, and column names are SNP IDs. Required. |
outTopN |
An integer to indicate the number of top hits to be reported for each marker, based on the standardized Jonckheere-Terpstra test statistics. Optional, and the default value is |
numThreads |
A integer to indicate the number of threads used in the computation. Optional, and the default value is |
standardized |
A boolean to specify whether to return standardized statistics ( |
A list
with two objects
J |
A matrix of the standardized/non-standardized Jonckheere-Terpstra test statistics,
depending on the value of the |
gSnipID |
If |
Rows (samples) are assumed to be in the same order in X
and G
.
Hollander, M. and Wolfe, D. A. (1999) Nonparametric Statistical Methods. New York: Wiley, 2nd edition.
# 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=5) res res <- jtGWAS(X_pat_mak, G_pat_SNP, outTopN=NA) head(res)