Convert proximity score table to an Assay or Assay5

Usage

ProximityScoresToAssay(object, ...)

# S3 method for class 'tbl_lazy'
ProximityScoresToAssay(object, values_from = "join_count_z", ...)

# S3 method for class 'data.frame'
ProximityScoresToAssay(
  object,
  values_from = "join_count_z",
  missing_obs = NA_real_,
  return_sparse = TRUE,
  ...
)

# S3 method for class 'PNAAssay'
ProximityScoresToAssay(
  object,
  values_from = "join_count_z",
  missing_obs = NA_real_,
  ...
)

# S3 method for class 'PNAAssay5'
ProximityScoresToAssay(
  object,
  values_from = "join_count_z",
  missing_obs = NA_real_,
  ...
)

# S3 method for class 'Seurat'
ProximityScoresToAssay(
  object,
  assay = NULL,
  new_assay = NULL,
  values_from = "join_count_z",
  missing_obs = NA_real_,
  ...
)

Arguments

object: An object with proximity scores
...: Not yet implemented
values_from: A single string defining what column in the proximity score table to pick values from.
missing_obs: A numeric value or NA to replace missing observations with. Default is NA_real_.
return_sparse: A logical specifying whether to return a sparse matrix (dgCMatrix).
assay: Name of the PNAAssay or PNAAssay5 to pull proximity scores from
new_assay: Name of the Assay or Assay5 to store the wide formatted spatial metric in

Behavior

Takes an object with PNA proximity scores in long format and returns an object with proximity scores in a wide format. The proximity score table contains various spatial metrics along with p-values for each protein pair and component.

The wide format is an array-like object with dimensions (markers_1 * marker_2) x components, where each cell is filled with a value for a selected spatial metric.

Note that that observations that are missing from the proximity score table can be replaced with 0's in the wide array by setting missing_obs_val = 0, which might be required by various functions in Seurat. However, this may not be the desired behavior as a value of 0 usually doesn't mean that the observation is missing.

Different outputs are returned depending on the input object type:

tibble/data.frame: returns a matrix with marker pairs in rows and components in columns
PNAAssay/PNAAssay5: returns an Assay or Assay5 with marker pairs in rows and components in columns
Seurat object: returns the Seurat object with a new Assay or Assay5 with marker pairs in rows and components in columns

As many methods provided in Seurat operates on Assay/Assay5 objects, it can sometimes be convenient to make this conversion if you wish to use these methods on spatial metrics in the proximity score table. For instance, if we want to compute a UMAP on the proximity scores with RunUMAP, we need the values to be formatted in an Assay/Assay5. This also makes it possible to use various visualization functions such as VlnPlot or FeaturePlot to show the distribution of proximity scores.

Convert proximity score table to an Assay or Assay5

Usage

Arguments

Behavior

See also