computeGX

path to "omics" dataset (i.e. column labels in a transcriptome dataset: FID, IID, gene1, gene2, ...)

genefile

outheader

path to a file listing the subset of individuals to include in the correlation matrix calculation (2 columns labeled: FID, IID)

idfile

This function computes the correlation matrix of all individuals in an "omics" dataset (e.g. transcriptome, metabolome, methylome, etc.).

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.

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computeGX: Compute the correlation matrix for an "omics" dataset

Description

Usage

Arguments

Value

References