Description Usage Arguments Value Author(s) References
View source: R/MultiScale.dp.kernel.R
Estimates posterior probability of a difference between two sample groups for each genomic variable (e.g., methylation site), using shared kernels and gene-dependent prior probabilities of equality.
1 | MultiScale.dp.kernel(X,Class,UniqGene,Gene,mu,Sigma,Concentration=0.5,alpha=1,KK=10,NumDraws=1000)
|
X |
A matrix in which rows represent variables (e.g., methylation sites) and columns represent samples. The entries of the matrix must be continuous between 0 and 1. |
Class |
A vector giving a class label for each sample. |
UniqGene |
Vector giving the gene labels. |
Gene |
Vector giving the gene label for each variable (length must be equal to the number of rows in X). |
mu |
Vector of kernel means. |
Sigma |
Vector of kernel standard deviations |
Concentration |
Dirichlet hyperparameter for kernel weights |
alpha |
Dirichlet process concentration parameter for the gene-level probabilities |
KK |
Stick-breaking threshold for Dirichlet process |
NumDraws |
Number of MCMC draws for posterior inference. |
Returns an object with the following values, averaged over the MCMC iterations:
pG |
Vector giving the gene-level prior for association for each gene |
posts |
Vector giving the posterior probability of association for each marker |
Eric F. Lock
Lock, E. F. & Dunson, D. B. (2016). Bayesian genome- and epigenome-wide association studies with gene level dependence. Preprint.
Lock, E. F. & Dunson, D. B. (2015). Shared kernel Bayesian screening. Biometrika, 102 (4): 829-842.
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