Xcovamat

matrix of covariates (10 principal components from the genetic relationship matrix and 10 principal components from the gene expression correlation matrix) from the intrinsic growth rate example with the IID list as the rownames

datasets

This package provides functions to generate correlation
        matrices from SNP, gene expression, methylation or other
        'omics' datasets and to use these matrices to predict the
        phenotype of an individual by using the phenotypes of the
        remaining individuals through kriging. Kriging is a
        geostatistical method for optimal prediction or best unbiased
        linear prediction. It consists of predicting the value of a
        variable at an unobserved location as a weighted sum of the
        variable at observed locations. Intuitively, it works as a
        reverse linear regression: instead of computing correlation
        (univariate regression coefficients are simply scaled
        correlation) between a dependent variable Y and independent
        variables X, it uses known correlation between X and Y to
        predict Y.

Hae Im

OmicKriging

OmicKriging for Phenotypic Prediction

Xcovamat function

The format is:
 num [1:159, 1:20] 0.0828 0.079 0.0771 0.0807 0.0787 ...
 - attr(*, "dimnames")=List of 2
  ..$ : chr [1:159] "NA10846" "NA10847" "NA12144" "NA12145" ...
  ..$ : chr [1:20] "PC1.SNPs" "PC2.SNPs" "PC3.SNPs" "PC4.SNPs" ...

format

Im, HK. et al. (2012) Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer. PLoS Genetics 8(2):e1002525.<p></p><p>        http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.1002525</p>

Xcovamat: data

Description

Usage

Arguments

format

source

Examples