Description Usage Arguments Value Examples
For GSA of SNP data, the following two-step procedure is implemented (see Biernacka et al[1] for more details on the method). Step 1: Principal components analysis for SNPs within a gene is completed with the components needed to explain 80 percent of the variation retained. Using these components, a gene-level association test is completed to determine the association of the gene with the phenotype. Step 2: The gene-level p values for genes within a given gene set are combined using the Gamma Method, a variation of Fisher's Method, to determine the association of the gene set with the phenotype. The GSA function for SNP data allow quantitative, binary and time-to-event phenotypes (i.e., linear models, logistic models, Cox proportional hazard models).
1 2 3 |
formula |
formula for model, include phenotype and covars. SNPs will be added by function |
data |
All data including matrix of genetic markers, each marker represented by the dosage of some allele, could also be CNV, treated as continuous and covariates |
snpprefix |
prefix for SNP variable, defaults to "snp" |
gene |
vector disignating the gene each marker belongs to, must be in same order as SNPs |
PCpctVar |
numeric indicating the percent of variation (in percent) in the genetic markers that is to be explained by PCs |
gammaShape |
numeric indicating the gamma shape parameter to be used for p-value summarization |
STT |
numeric indicating soft truncation threshold to be used, will calculate gamma parameter (must be <= 0.4) |
pheno.type |
type of phenotype, case-control results in logistic regression, quantitative results in OLS, and survival results in cox model |
perm |
boolean indicating whether permutation p-value are to be used for the gamma summary method |
n.perm |
numeric indicating number of permutations to be used |
seed |
numeric to set RNG for reproducability |
This functions returns a list.
gamma.pvalue |
Gamma P value |
perm.pvalue |
Gamma permutation p value, if specified. Else NA |
gene.info |
Info for each gene |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ###Case Control (logistic) example
data(testdata)
data(gene_example)
PCgamma(pheno~strata(study)+age,
data=testdata,gene=gene_example,pheno.type="case.control",
STT = 0.2, gammaShape = NULL,
perm=FALSE, n.perm = 10, seed = 12212012)
##Here is a survival example
set.seed(1234)
time_example <- rnorm(150, m=50, sd=10)
event_example <- rbinom(150, 1, 0.3)
testdata <- cbind(testdata,time_example,event_example)
PCgamma(Surv(time_example,event_example)~strata(study)+age,
data=testdata,gene=gene_example,pheno.type="survival",
STT = 0.2, gammaShape = NULL,
perm=FALSE, n.perm = 10, seed = 12212012)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.