Plot network of correlated features — spatial

spatial_network() Creates a network for a given SpatialExperiment object (spe) based on the correlation of features. Will return a fully connect graph

spatial_network(
  spe,
  assay_names,
  feature_names = NULL,
  query_feature = NULL,
  target_features = list(),
  method = "spearman"
)

Arguments

spe: SpatialExperiment (SPE) object
assay_names: Name of assay in the spe object that contains data to be plotted. If only one it will focus on features in the first assay. If more than one it will compute correlations across all features.
feature_names: will describe which value to use for node names. IF missing will use rownames
query_feature: A single feature to query for correlation. If there are no target_features will build a network between this feature all other nodes. If missing, correlation will be computed between all nodes in target_features.
target_features: List of features to compute correlation across. If query_feature is not NULL, will compute correlation only between these features and the query_feature. Otherwise, it will compute correlation between all features in this list. DEFAULT: all features in spe.
method: to use for correlation, default is spearman

Value

a tidygraph object that can be used for plotting or analysis

Details

Using the feature values, the algorithm computes the correlation between them across space, filtes by lowest_thresh (in the interest of compute time) and then selects the most correlated edges using the top_edges parameter.

Only the largest connected component is plotted, but the entire graph is returned as an tidygraph object.

Examples


data(pancMeta)
data(smallPancData)
data(protMeta)
img0.spe <- convert_to_spe(smallPancData$Image_0,
  pancMeta,
  protMeta,
  feature_meta_colname = "pancProts",
  image_files = system.file("extdata", "Image_0.png", package = "spammR"),
  spatial_coords_colnames = c("x_pixels", "y_pixels"),
  sample_id = "Image0",
  image_ids = "with_grid"
)
#> Note: Only mapping metadata for 2986 features out of 3000 data points
#get 10 most variable features
vp <- sort(apply(assay(img0.spe),1,var),decreasing = TRUE)[1:10]
##now call the network
res <- spatial_network(img0.spe,'proteomics', target_features=names(vp))
#> Joining with `by = join_by(rowval)`