acPCoA: Perform AC-PCoA for confounding factor adjustement based on...

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

Description

Perform AC-PCoA for confounding factor adjustement based on Principle Coordinate Analysis

Usage

acPCoA(
  DistanceMatrix,
  ConfounderMatrix,
  nPC = 2,
  lambdas = seq(0, 20, 0.05),
  kernel = "linear",
  bandwidth = NULL,
  anov = T,
  perc = 0.05
)

Arguments

DistanceMatrix	the n by n data distance matrix, where n is the number of samples. The (i,j)-th entry stands for the distance (dissimilarity) between the i-th sample and the j-th sample.
ConfounderMatrix	the n by q confounder matrix, where n is the number of samples, q is the number of confounding factors.
nPC	number of principal components to compute
lambdas	the tuning parameter, non-negative.
kernel	the kernel to use: "linear", "gaussian".
bandwidth	bandwidth h for Gaussian kernel. Optional.
anov	True or False. Whether the penalty term has the between groups sum of squares interpretation. Default is True.
perc	the best lambda is defined to be the smallest lambda with R(lambda)<=perc (if anov=T), or R(lambda)<=perc*R(lambda=0) (if anov=F) in the nPC principal components.

Value

The principal components and the projected data

v	the principal components, p by nPC matrix
Xv	the projected data, i.e. X times v
eigenvalueX	eigenvalues for the PCs
varianceX	variance explained by the PCs
varianceX_perc	percentage of total variance in X explained by the PCs. If eval=F, NA is returned.
eigenvalueXperm	eigenvalues for the PCs, permutation. If eval=F, NA is returned.
varianceXperm	variance explained by the PCs, permutation. If eval=F, NA is returned.
sigPC	the significant PCs. If eval=F, NA is returned.
...	Input parameters for the function

Examples

## Not run: 
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()

## End(Not run)

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