Poly-Omic Prediction of Complex TRaits
Multithreaded cross validation routine for Omic Kriging.
Loads sample phenotype and covariate data into data frame.
Compute gene expression correlation matrix.
Run Principal Component Analysis (PCA) using the irlba package.
Run Principal Component Analysis (PCA) using base R svd() function.
Run omic kriging on a set of correlation matrices and a given phenotyp...
Read the GRM binary file.
Write GRM binary files.
It provides functions to generate a correlation matrix from a genetic dataset and to use this matrix 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.