jackstraw (version 1.3.17)

jackstraw_alstructure: Non-Parametric Jackstraw for ALStructure

Description

Test association between the observed variables and population structure estimated by ALStructure.

Usage

jackstraw_alstructure(
  dat,
  r,
  FUN,
  r1 = NULL,
  s = NULL,
  B = NULL,
  covariate = NULL,
  verbose = TRUE
)

Value

jackstraw_alstructure returns a list consisting of

p.value

m p-values of association tests between variables and their LFs

obs.stat

m observed deviances

null.stat

s*B null deviances

Arguments

dat

a genotype matrix with m rows as variables and n columns as observations.

r

a number of significant LFs.

FUN

a function to ALStructure

r1

a numeric vector of LFs of interest (implying you are not interested in all r LFs).

s

a number of ``synthetic'' null variables. Out of m variables, s variables are independently permuted.

B

a number of resampling iterations. There will be a total of s*B null statistics.

covariate

a data matrix of covariates with corresponding n observations (do not include an intercept term).

verbose

a logical specifying to print the computational progress.

Author

Neo Christopher Chung

Details

This function uses ALStructure from Cabreros and Storey (2019). A deviation dev in logistic regression (the full model with r LFs vs. the intercept-only model) is used to assess association. This function also requires the Bioconductor gcatest package to be installed.

References

Chung and Storey (2015) Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics, 31(4): 545-554 tools:::Rd_expr_doi("10.1093/bioinformatics/btu674")

See Also

jackstraw_pca jackstraw

Examples

if (FALSE) {
# load genotype data to analyze (not shown) into this variable
X
# choose the number of ancestries
r <- 3

# load alstructure package (install from https://github.com/StoreyLab/alstructure)
library(alstructure)
# define the function this way, a function of the genotype matrix only
FUN <- function(x) t( alstructure(x, d_hat = r)$Q_hat )

# calculate p-values (and other statistics) for each SNP
out <- jackstraw_alstructure( X, r, FUN )
}