In YuWang28/acPCoA: Adjustment for Confounding Factors using Principal Coordinate Analysis

knitr::opts_chunk$set(
  collapse = TRUE,
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acPCoA

AC-PCoA is a method proposed by Yu Wang etc., which reduces the data dimension while extracting the information from different distance measures using principal coordinate analysis (PCoA), and adjusts the confounding factors across multiple data sets
by minimizing the associations between the lower dimensional representations and the confounding variables. Application of the proposed method is further extended to the scenario of classification and prediction.

Installation

To install "acPCoA", first you need to install the P package "acPCA" from https://github.com/linzx06/AC-PCA

Then, you can install the released version of "acPCoA" from github with

#install.packages("devtools")
#library(devtools)
#install_github("YuWang28/acPCoA")

Example

This is a basic example which shows you how to implement acPCoA for visualization after confounding factor adjustment:

library(acPCoA)
library(ggplot2)
X <- data_mbqc_groupA$DistMat.BC;
Y <- data_mbqc_groupA$ConfounderMat;
result_acPCoA <- acPCoA(DistanceMatrix=X, ConfounderMatrix=Y, nPC=2, lambda=seq(0, 20, 0.05), kernel="linear")
ggplot(as.data.frame(result_acPCoA$Xv),aes(x=V1,y=V2,color=data_mbqc_groupA$Specimen))+geom_point()

YuWang28/acPCoA documentation built on Dec. 18, 2021, 8:20 p.m.