Calculate Spatial External Metrics — getSpatialExternalMetrics • poem

A generic function to calculate spatial external metrics. It can be applied to raw components (true, pred, location) or directly to a SpatialExperiment object.

Usage

getSpatialExternalMetrics(
  object = NULL,
  true,
  pred,
  location = NULL,
  k = 6,
  alpha = 0.5,
  level = "class",
  metrics = NULL,
  fuzzy_true = TRUE,
  fuzzy_pred = FALSE,
  ...
)

# S4 method for class 'missing'
getSpatialExternalMetrics(
  object = NULL,
  true,
  pred,
  location = NULL,
  k = 6,
  alpha = 0.5,
  level = "class",
  metrics = NULL,
  fuzzy_true = TRUE,
  fuzzy_pred = FALSE,
  ...
)

# S4 method for class 'SpatialExperiment'
getSpatialExternalMetrics(
  object = NULL,
  true,
  pred,
  location = NULL,
  k = 6,
  alpha = 0.5,
  level = "class",
  metrics = NULL,
  fuzzy_true = TRUE,
  fuzzy_pred = FALSE,
  ...
)

Arguments

object: The main input. Can be a SpatialExperiment object or missing (when using true, pred, and location directly).
true: When object is missing: a vector containing the labels of the true classes. Must be a vector of characters, integers, numerics, or a factor, but not a list. When object is a SpatialExperiment object: the column name in colData(object) containing the true labels.
pred: When object is missing: a vector containing the labels of the predicted clusters. Must be a vector of characters, integers, numerics, or a factor, but not a list. When object is a SpatialExperiment object: the column name in colData(object) containing the predicted labels.
location: A matrix or data.frame of coordinates
k: The number of neighbors used when calculating the fuzzy class memberships for fuzzy metrics, or when calculating the weighted accuracy.
alpha: The parameter to control to what extend the spot itself contribute to the class composition calculation. "equal" means it is weighted the same as other neighbors. A numeric value between 0 and 1 means the weight of the frequency contribution for the spot itself, and the frequency contribution for its knn is then 1-alpha. By default 0.5.
level: The level to calculate the metrics. Options include "element", "class" and "dataset".
metrics: The metrics to compute. See details.
fuzzy_true: Logical; whether to compute fuzzy class memberships for true.
fuzzy_pred: Logical; whether to compute fuzzy class memberships for pred.
...: Additional arguments passed to specific methods.

Value

A data.frame of metrics based on the specified input.

Details

The allowed values for metrics depend on the value of level:

If level = "element", the allowed metrics are: "nsSPC", "NPC","SpatialSPC".
If level = "class", the allowed metrics are: "nsWH", "nsAWH", "nsWC","nsAWC".
If level = "dataset", the allowed metrics are: "nsRI", "nsARI","nsWH","nsAWH", "nsWC","nsAWC", "nsAccuracy","SpatialRI","SpatialARI".

Examples

# Example with individual components
data(sp_toys)
data <- sp_toys
getSpatialExternalMetrics(true=data$label, pred=data$p1, 
location=data[,c("x", "y")], k=6, level="class")
#> Comparing between a fuzzy truth and a hard prediction...
#> Standard error of the mean NDC across permutations:0.00104
#>        nsWH     nsAWH      nsWC     nsAWC class cluster
#> 1        NA        NA 0.8078698 0.5932487     1      NA
#> 2        NA        NA 1.0000000 1.0000000     2      NA
#> 3 1.0000000 1.0000000        NA        NA    NA       1
#> 4 0.8323893 0.6507836        NA        NA    NA       2

# Example with SpatialExperiment object
se_object <- SpatialExperiment::SpatialExperiment(assays=matrix(NA, 
                                             ncol = nrow(data[,c("x", "y")]), 
                                             nrow = ncol(data[,c("x", "y")])), 
                               spatialCoords=as.matrix(data[,c("x", "y")]))
SummarizedExperiment::colData(se_object) <- 
                      cbind(SummarizedExperiment::colData(se_object), 
                            data.frame(true=data$label, pred=data$p1))
getSpatialExternalMetrics(object=se_object, true="true", pred="pred", k=6, 
                          level="class")
#> Comparing between a fuzzy truth and a hard prediction...
#> Standard error of the mean NDC across permutations:0.00102
#>        nsWH     nsAWH      nsWC     nsAWC class cluster
#> 1        NA        NA 0.8078698 0.5969466     1      NA
#> 2        NA        NA 1.0000000 1.0000000     2      NA
#> 3 1.0000000 1.0000000        NA        NA    NA       1
#> 4 0.8323893 0.6502319        NA        NA    NA       2