generate_embeddings: Generate embeddings.

In patrickCNMartin/Vesalius: Vesalius: Dissecting Tissue Anatomy from Spatial Transcriptomic Data

Generate embeddings.

Description

Generate image embddings from spatial omics data.

Usage

generate_embeddings(
  vesalius_assay,
  dim_reduction = "PCA",
  normalization = "log_norm",
  use_counts = "raw",
  dimensions = 30,
  tensor_resolution = 1,
  filter_grid = 1,
  filter_threshold = 1,
  nfeatures = 2000,
  features = NULL,
  min_cutoff = "q5",
  remove_lsi_1 = TRUE,
  verbose = TRUE
)

Arguments

vesalius_assay	vesalius_assay object.
dim_reduction	string describing which dimensionality reduction method should be used. One of the following: "PCA", "PCA_L", "UMAP", "LSI", "LSI_UMAP".
normalization	string describing which normalisation method to use. One of the following "log_norm", "SCT", "TFIDF", "none".
use_counts	string describing which counts should be used for the generating emebddings. Default = "raw". See details.
dimensions	numeric describing the number of Principle Components or Latent space dimension to use. Default dimensions = 30
tensor_resolution	numeric (range 0 - 1) describing the compression ratio to be applied to the final image. Default = 1
filter_grid	numeric (range 0 - 1) size of the grid used when filtering outlier beads. Defined as a proportion of total image size. Default = 0.1
filter_threshold	numeric (range 0 -1) describing the quantile threshold at which tiles should be retained (seed details)
nfeatures	numeric describing the number of variable features to use.
features	custom set of features to generate embeddings
min_cutoff	only used when dimensionality reduction method is LSI or LSI_UMAP cutoff for feature to be included in the VariableFeatures for the object.
remove_lsi_1	logical only used when dimensionality reduction method is LSI or LSI_UMAP indicating if the first LSI component should be removed from further analysis as it usually captures sequencing depth (technical variation)
verbose	logical output progress message or not. Default = TRUE

Details

The core principle behind vesalius is to convert spatial omics data into an image. generate_embeddings allows you to convert your omics data into a stack of gray scale images. The stack hight will be equal to the number of dimenions you selected excluding UMAP embeddings as only 3 dimesnions are availbale.

If tiles have not yet been generated (see generate_tiles), pixel will be generated from the spatial coordinates provided. If tiles are already present, 'generate_embeddings' will skip the tile creation step.

The algorithm is broadly applicable to many spatial omics assays. Vesalius provides a 3 nornalization methods log_norm, SCTransform, and TF-IDF.

The 'use_counts' argument specifies which count matrix should be used for normalization. This argument is only necessary if you use a custom normalised count matrix. In this case, set this argument to the name you gave your count matrix (see add_counts) and 'generate_embeddings' will skip the normalization and use your custom count matrix to generate image embeddings.

Note that it is also possible to add custom embeddings by using the add_embeddings function.

Embeddings can be created using a custom set of genes. These genes can be provided to the 'features' argument in the form of a character vector. Note that this will not filter the count matrix hence you will still have access to the whole matrix for downsteam analysis.

Value

vesalius_assay

Examples

## Not run: 
data(vesalius)
# First we build a simple object
ves <- build_vesalius_object(coordinates, counts)
# We can do a simple run 
ves <- generate_embeddings(ves)
# maybe we want to try a different method
# both will be stored in the object
ves <- generate_embeddings(ves, dim_reduction = "UMAP")


## End(Not run)

patrickCNMartin/Vesalius documentation built on April 17, 2025, 11:31 p.m.