ppca2Covplot: Covariance matrix visualisation

In HGray384/pcaNet: Probabilistic principal components analysis - covariance estimation and network reconstruction

Description

Heatmap visualisation of the covariance matrix estimated within PPCA.

Usage

ppca2Covplot(ppcaOutput)

Arguments

ppcaOutput

list – the output object from running any of the PPCA functions in this package.

Value

plot of the estimated covariance matrix

Examples

#' # simulate a dataset from a zero mean factor model X = Wz + epsilon
# start off by generating a random binary connectivity matrix
n.factors <- 5
n.genes <- 200
# with dense connectivity
# set.seed(20)
conn.mat <- matrix(rbinom(n = n.genes*n.factors,
                          size = 1, prob = 0.7), c(n.genes, n.factors))

# now generate a loadings matrix from this connectivity
loading.gen <- function(x){
  ifelse(x==0, 0, rnorm(1, 0, 1))
}

W <- apply(conn.mat, c(1, 2), loading.gen)

# generate factor matrix
n.samples <- 100
z <- replicate(n.samples, rnorm(n.factors, 0, 1))

# generate a noise matrix
sigma.sq <- 0.1
epsilon <- replicate(n.samples, rnorm(n.genes, 0, sqrt(sigma.sq)))

# by the ppca equations this gives us the data matrix
X <- W%*%z + epsilon
WWt <- tcrossprod(W)
Sigma <- WWt + diag(sigma.sq, n.genes)

# select 10% of entries to make missing values
missFrac <- 0.1
inds <- sample(x = 1:length(X),
               size = ceiling(length(X)*missFrac),
               replace = FALSE)

# replace them with NAs in the dataset
missing.dataset <- X
missing.dataset[inds] <- NA

# run ppca
ppf <- pca_full(missing.dataset, ncomp=5, algorithm="vb", maxiters=5,
bias=TRUE, rotate2pca=FALSE, loglike=TRUE, verbose=TRUE)

# plot the matrix
ppca2Covplot(ppf)

HGray384/pcaNet documentation built on Nov. 14, 2020, 11:11 a.m.