removed neighbor argument from localOutliers to fix bug occuring in s…

…ome datasets.

DESCRIPTION

@@ -5,7 +5,7 @@ Date: 2024-11-08
 Authors@R: c(
     person("Michael", "Totty", ,"[email protected]", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0002-9292-8556")),
-               person("Stephanie", "Hicks", ,email = "[email protected]", role = c("aut"),
+    person("Stephanie", "Hicks", ,email = "[email protected]", role = c("aut"),
            comment = c(ORCID = "0000-0002-7858-0231")),
     person("Boyi", "Guo", ,email = "[email protected]", role = c("aut"),
            comment = c(ORCID = "0000-0003-2950-2349")))

NEWS.md

@@ -13,9 +13,6 @@
 
 - **Parallelization**: Added a `workers` argument for parallel processing using `BiocParallel` in both `localOutlier` and `localVariance` functions. This allows for faster computation, particularly on larger datasets.
 
-- **`neighborhood` Argument**: Introduced a new `neighborhood` parameter to the `localOutlier` function to support neighborhoods beyond spatial coordinates. Users can now specify alternative neighborhood matrices, such as reduced dimensions from PCA or UMAP, enabling local outlier detection in non-spatial datasets such as snRNA-seq.
-
-
 ### Deprecations
 - **`plotQC` Function Deprecated**: The `plotQC` function is now deprecated. While it remains available for backward compatibility, users are encouraged to transition to `plotQCmetrics`. Calling `plotQC` will display a warning, reminding users of the deprecation.
 

R/localOutliers.R

@@ -14,7 +14,6 @@
 #' @param log Logical indicating whether to log1p transform the features
 #' (default is TRUE)
 #' @param cutoff Cutoff for outlier detection (default is 3)
-#' @param neighborhood Matrix of spatial coordinates for neighborhood calculation
 #' @param workers Number of workers for parallel processing (default is 1)
 #'
 #' @return SpatialExperiment or SingleCellExperiment object with updated colData containing outputs
@@ -51,15 +50,13 @@
 #' spe <- localOutliers(spe,
 #'                      metric = "sum",
 #'                      direction = "lower",
-#'                      log = TRUE,
-#'                      neighborhood = spatialCoords(spe)
+#'                      log = TRUE
 #' )
 #'
 localOutliers <- function(
     spe, metric = "detected",
     direction = "lower", n_neighbors = 36, samples = "sample_id",
-    log = TRUE, cutoff = 3, neighborhood = spatialCoords(spe_subset),
-    workers=1) {
+    log = TRUE, cutoff = 3, workers=1) {
 
   # ===== Validity checks =====
   # Check if 'spe' is a valid object with required components
@@ -109,7 +106,7 @@ localOutliers <- function(
     columnData <- colData(spe_subset)
 
     # Find nearest neighbors
-    dnn <- BiocNeighbors::findKNN(neighborhood,
+    dnn <- BiocNeighbors::findKNN(spatialCoords(spe_subset),
                                   k = n_neighbors, warn.ties = FALSE,
                                   BPPARAM = BiocParallel::MulticoreParam(workers=workers))$index