fastJT {fastJT}

Compute the Jonckheere-Terpstra Test Statistics

Description

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 statistics employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.

Usage

fastJT(Y, X, outTopN=15L, numThreads=1L, standardized=TRUE)  	

Arguments

Y

A matrix of continuous values, representing dependent variables, e.g. marker levels or other observed values. Row names should be sample IDs, and column names should be variable names. Required.

X

A matrix of integer values, representing independent variables, e.g. SNP counts or other classification features. Row names should be sample IDs, and column names should be feature IDs. Required.

outTopN

An integer to indicate the number of top independent variables to be reported for each dependent variable, based on the standardized Jonckheere-Terpstra test statistics. Optional. The default value is 15L. If set to NA, all results are returned.

numThreads

A integer to indicate the number of threads used in the computation. Optional. The default value is 1L (sequential computation).

standardized

A boolean to specify whether to return standardized Jonckheere-Terpstra statistics (TRUE) or non-standardized statistics (FALSE). Optional. The default value is TRUE.

Value

A list with two objects

J

A matrix of the standardized/non-standardized Jonckheere-Terpstra test statistics, depending on the value of the standardized argument.

XIDs

If outTopN was specified, this object is a matrix of the column IDs of X corresponding to the top standardized Jonckheere-Terpstra test statistics for each dependent variable. Otherwise this is a vector of the column IDs of X.

Note

Rows (samples) are assumed to be in the same order in X and Y.

References

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

Examples

# Generate dummy data	
num_sample <- 100
num_marker <- 10
num_SNP		 <- 500
set.seed(12345)
Mark <- matrix(rnorm(num_sample*num_marker), num_sample, num_marker)
Geno <- matrix(rbinom(num_sample*num_SNP, 2, 0.5), num_sample, num_SNP)
colnames(Mark) <- paste0("Mrk:",1:num_marker)
colnames(Geno) <- paste0("SNP:",1:num_SNP)

res <- fastJT(Y=Mark, X=Geno, outTopN=5)
res
res <- fastJT(Y=Mark, X=Geno, outTopN=NA)
head(res)