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.
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")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()