| Type: | Package |
| Title: | Composite Kernel Association Test for Pharmacogenetics Studies |
| Version: | 0.1.0 |
| Author: | Hong Zhang and Judong Shen |
| Maintainer: | Hong Zhang <hzhang@wpi.edu> |
| Description: | Composite Kernel Association Test (CKAT) is a flexible and robust kernel machine based approach to jointly test the genetic main effect and gene-treatment interaction effect for a set of single-nucleotide polymorphisms (SNPs) in pharmacogenetics (PGx) assessments embedded within randomized clinical trials. |
| License: | GPL-2 |
| Imports: | stats, CompQuadForm |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.1.0 |
| NeedsCompilation: | no |
| Packaged: | 2019-10-07 01:39:20 UTC; consi |
| Repository: | CRAN |
| Date/Publication: | 2019-10-09 09:20:02 UTC |
Composite kernel association test for SNP-set analysis in pharmacogenetics (PGx) studies.
Description
Composite kernel association test for SNP-set analysis in pharmacogenetics (PGx) studies.
Usage
CKAT(G, Tr, X, y, trait = "continuous", ker = "linear", grids = c(0,
0.5, 1), n_a = 1000, method = "liu", subdiv = 10^6)
Arguments
G |
- genotype matrix. |
Tr |
- treatment vector, 0 indicates placebo, 1 indicates treatment. |
X |
- non-genetic covariates data matrix. |
y |
- response vector. Currently continuous and binary responses are supported. Survival response will be added soon. |
trait |
- response indicator. trait = "continuous" or "binary". |
ker |
- kernel. ker = "linear", "IBS", "Inter" (interaction kernel) and "RBF" (radial basis function kernel). |
grids |
- grids of the candidate weights. |
n_a |
- the number of intervals for manual integration (when integrate function fails). Default n_a = 1000. |
method |
- method for getting density of A (see details in the reference). Default method is Liu's method. |
subdiv |
- parameter of Davies' method. Default value is 1E6. |
Value
pvals - p-values of each individual association test.
finalp - final p-value of the CKAT test.
Examples
nsamples = 500; nsnps = 10
X = rnorm(nsamples,0,1)
Tr = sample(0:1,nsamples,replace=TRUE)
G = matrix(rbinom(nsamples*nsnps, 1, 0.05), nrow = nsamples, ncol = nsnps)
GxT = G*Tr
Y0 = 0.5*X + Tr + rnorm(nsamples)
CKAT(G, Tr, X, Y0, grids=c(0,0.5,1))