README.md

In PCAmatchR: Match Cases to Controls Based on Genotype Principal Components

PCAmatchR

Overview

PCAmatchR optimally matches a set of population-based controls to cases. PCAmatchR converts user-provided principal components (PC) into a Mahalanobis distance metric for selecting a set of well-matched controls for each case.

PCAmatchR takes as input user defined PCs and eigenvalues and directly outputs optimal case and control matches.

Important Note

The optmatch code is not contained in this package. In order to use PCAmatchR, users must manually install and load the optmatch package (>=0.9-1) separately and accept its license. Manual loading is necessary due to software license issues. If the optmatch package is not loaded, the PCAmatchR main function, match_maker(), will fail and display an error message. For more information about the optmatch package, please see the reference below.

Installation

To install the release version from CRAN:

install.packages("PCAmatchR")

To install the development version from GitHub:

devtools::install_github("machiela-lab/PCAmatchR")

Available functions

Function Description match_maker Main function. Weighted matching of controls to cases using PCA results. plot_maker Easily make a plot of matches from match_maker output.

Available sample data sets

Data set Description PCs_1000G First 20 principal components of 2504 individuals from Phase 3 of 1000 Genomes Project. eigenvalues_1000G A sample data set containing the first 20 eigenvalues. eigenvalues_all_1000G A sample data set containing all of the eigenvalues. ## Usage wzxhzdk:0

Reference

Hansen BB, Klopfer SO. Optimal full matching and related designs via network flows. Journal of computational and Graphical Statistics. 2006 Sep 1;15(3):609-27.

PCAmatchR documentation built on Dec. 1, 2022, 1:33 a.m.