The Spatial Annotation Screening algorithm

Screens the sample for numeric variables that stand in meaningful, spatial relation to annotated structures/areas, spatial annotations. For a detailed explanation on how to define the parameters distance, resolution, angle_span and n_bins_angle see details section.

spatialAnnotationScreening(
  object,
  ids,
  variables,
  core,
  distance = "dte",
  resolution = recSgsRes(object),
  angle_span = c(0, 360),
  unit = getDefaultUnit(object),
  bcs_exclude = character(0),
  sign_var = "fdr",
  sign_threshold = 0.05,
  force_comp = FALSE,
  skip_comp = FALSE,
  model_add = NULL,
  model_subset = NULL,
  model_remove = NULL,
  estimate_R2 = TRUE,
  control = NULL,
  n_random = 10000,
  rm_zero_infl = TRUE,
  seed = 123,
  add_image = TRUE,
  verbose = NULL,
  ...
)

Arguments

object

An object of class SPATA2 or, in case of S4 generics, objects of classes for which a method has been defined.

ids

Character vector. Specifies the IDs of the spatial annotations of interest.

variables

Character vector. The numeric variables to be included in the screening process. Makre sure that the correct matrix is active in the respective assays.

distance

Distance measure. Specifies the distance from the border of the spatial annotation to the horizon in the periphery up to which the screening is conducted. Defaults to a distance that covers the whole tissue section the spatial annotation is located on using distToEdge(). (This distance must not be exceeded.)

resolution

Distance measure. The resolution with which the expression gradient is inferred. Defaults are platform specific. See more in detail section of recSgsRes().

angle_span

Numeric vector of length 2. Confines the area screened by an angle span relative to the center of its closest spatial annotation.

unit

Character value. Specifies the desired unit in which distance measures or area measures are provided. Run validUnitsOfLength() or validUnitsofArea() for valid input options.

bcs_exclude

Character value containing the barcodes of observations to be excluded from the analysis.

sign_var

Either p_value or fdr. Defaults to fdr.

sign_threshold

The significance threshold. Defaults to 0.05.

model_add

Named list. Every slot in the list must be either a formula containing a function that takes a numeric vector as input and returns a numeric vector with the same length as its input vector. Or a numeric vector with the same length as the input vector. Test models with showModels().

model_subset

Character value. Used as a regex to subset models. Use validModelNames() to obtain all model names that are known to SPATA2 and showModels() to visualize them.

model_remove

Character value. Used as a regex to remove models are not supposed to be included.

control

A list of arguments as taken from stats::loess.control(). Default setting is stored in SPATA2::sgs_loess_control.

n_random

Number of random permutations for the significance testing of step 2.

seed

Numeric value. Sets the random seed.

verbose

Logical. If TRUE, informative messages regarding the computational progress will be printed.

(Warning messages will always be printed.)

...

Used to absorb deprecated arguments or functions.

Value

An object of class SpatialAnnotationScreening.

Tutorials

Extensive tutorials for how to use this function can be found on our website https://themilolab.github.io/SPATA2/ .

See also

createGroupAnnotations(), createImageAnnotations(), createNumericAnnotations() for how to create spatial annotations.

getCoordsDfSA() for how to obtain spatial relation of data points to a spatial annotation.

getSasDf() for how to obtain inferred expression gradients as used in spatial annotation screening.

plotSasLineplot() for visualization of inferred expression gradients.

Examples

library(SPATA2)

data("example_data")

object <- example_data$object_UKF313T_diet

object <- identifyTissueOutline(object)

ids <- getSpatAnnIds(object, tags = c("necrotic", "compr"), test = "all")

# opt 1 prefiltering by SPARKX is recommended, but not required
object <- runSPARKX(object)
genes <- getSparkxGenes(object, threshold_pval = 0.05)

# opt 2
genes <- getGenes(object)

sas_out <-
 spatialAnnotationScreening(
   object = object,
   ids = ids,
   variables = genes,
   core = FALSE
   )