pca_pen {markerpen} | R Documentation |
Penalized Principal Component Analysis for Marker Gene Selection
Description
This function solves the optimization problem
where means all eigenvalues of
are
between 0 and 1,
means all elements of
are nonnegative,
and
is a penalty function defined in the article
(see the References section).
Usage
pca_pen(
S,
gr,
lambda,
w = 1.5,
alpha = 0.01,
maxit = 1000,
eps = 1e-04,
verbose = 0
)
Arguments
S |
The sample correlation matrix of gene expression. |
gr |
Indices of genes that are treated as markers in the prior information. |
lambda |
Tuning parameter to control the sparsity of eigenvectors. |
w |
Tuning parameter to control the weight on prior information.
Larger |
alpha |
Step size of the optimization algorithm. |
maxit |
Maximum number of iterations. |
eps |
Tolerance parameter for convergence. |
verbose |
Level of verbosity. |
Value
A list containing the following components:
- projection
The estimated projection matrix.
- evecs
The estimated eigenvectors.
- niter
Number of iterations used in the optimization process.
- err_v
The optimization error in each iteration.
References
Qiu, Y., Wang, J., Lei, J., & Roeder, K. (2020). Identification of cell-type-specific marker genes from co-expression patterns in tissue samples.
Examples
set.seed(123)
n = 200 # Sample size
p = 500 # Number of genes
s = 50 # Number of true signals
# The first s genes are true markers, and others are noise
Sigma = matrix(0, p, p)
Sigma[1:s, 1:s] = 0.9
diag(Sigma) = 1
# Simulate data from the covariance matrix
x = matrix(rnorm(n * p), n) %*% chol(Sigma)
# Sample correlation matrix
S = cor(x)
# Indices of prior marker genes
# Note that we have omitted 10 true markers, and included 10 false markers
gr = c(1:(s - 10), (s + 11):(s + 20))
# Run the algorithm
res = pca_pen(S, gr, lambda = 0.1, verbose = 1)
# See if we can recover the true correlation structure
image(res$projection, asp = 1)