regularise_image denoise Vesalius images via variance regularization
Usage
regularise_image(
vesalius_assay,
dimensions = seq(1, 3),
embedding = "last",
lambda = 1,
niter = 100,
verbose = TRUE
)Arguments
- vesalius_assay
a vesalius_assay object
- dimensions
numeric vector of latent space dimensions to use.
- embedding
character string describing which embedding should be used.
- lambda
numeric - positive real numbers describing regularization parameter (see details)
- niter
numeric - number of variance regularization iterations (Default = 100)
- verbose
logical - progress message output.
Details
Image regularization can be seen as a form of image denoising. Details on each method can be found in the tvR package under the denoise2 function https://cran.r-project.org/web/packages/tvR/tvR.pdf.
A higher value for lambda will results in a smoother image. It should be noted that in the context of spatial omics the more sparse the points in the data (the more space between coordinates), the more you will need to increase the value of lambda to obtain better denoising.
Examples
if (FALSE) { # \dontrun{
data(vesalius)
# First we build a simple object
ves <- build_vesalius_object(coordinates, counts)
# We can do a simple run
ves <- build_vesalius_embeddings(ves)
# simple regularisation
ves <- regularise_image(ves, embedding = "PCA")
} # }