diff --git a/R/localOutliers.R b/R/localOutliers.R
index a0d2012..90e1a40 100644
--- a/R/localOutliers.R
+++ b/R/localOutliers.R
@@ -39,14 +39,34 @@
 #' # Identifying the mitochondrial transcripts in our SpatialExperiment.
 #' is.mito <- rownames(spe)[grepl("^MT-", rownames(spe))]
 #'
-#' # Calculating QC features for each spot using scuttle
-#' spe <- scuttle::addPerCellQC(spe, subsets = list(Mito = is.mito))
+#' # Calculating QC metrics for each spot using scuttle
+#' spe <- scuttle::addPerCellQCMetrics(spe, subsets = list(Mito = is.mito))
+#' colnames(colData(spe))
 #'
-# Identifying local outliers suing SpotSweepR
+#' # Identifying local outliers using SpotSweeper
 #' spe <- localOutliers(spe,
-#'     metric = "detected",
-#'     direction = "lower"
+#'                      metric = "sum",
+#'                      direction = "lower",
+#'                      log = TRUE
 #' )
+#'
+#' spe <- localOutliers(spe,
+#'                      metric = "detected",
+#'                      direction = "lower",
+#'                      log = TRUE
+#' )
+#'
+#' spe <- localOutliers(spe,
+#'                      metric = "subsets_Mito_percent",
+#'                      direction = "higher",
+#'                      log = FALSE
+#' )
+#'
+#' # combine all outliers into "local_outliers" column
+#' spe$local_outliers <- as.logical(spe$sum_outliers) |
+#'   as.logical(spe$detected_outliers) |
+#'   as.logical(spe$subsets_Mito_percent_outliers)
+#'
 localOutliers <- function(
         spe, metric = "detected",
         direction = "lower", n_neighbors = 36, samples = "sample_id",
diff --git a/tests/testthat/test-localOutliers.R b/tests/testthat/test-localOutliers.R
index 04fd770..4e56d36 100644
--- a/tests/testthat/test-localOutliers.R
+++ b/tests/testthat/test-localOutliers.R
@@ -1,18 +1,57 @@
 # run examples from localOutliers() function documentation
-example(localOutliers, echo = FALSE)
+library(SpatialExperiment)
 
-test_that("example objects have correct class", {
-  # matrices??
-})
+# load example data
+spe <- STexampleData::Visium_humanDLPFC()
 
-test_that("example objects have correct dimensions", {
-  expect_equal(dim(spe), c(X, XXXXX))
-})
+# change from gene id to gene names
+rownames(spe) <- rowData(spe)$gene_name
 
-test_that("example objects gives correct number of outliers", {
-  expect_equal(length(colnames(colData(spe))), XY)
-})
+# drop out-of-tissue spots
+spe <- spe[, spe$in_tissue == 1]
+spe <- spe[, !is.na(spe$ground_truth)]
+
+# Identifying the mitochondrial transcripts in our SpatialExperiment.
+is.mito <- rownames(spe)[grepl("^MT-", rownames(spe))]
+
+# Calculating QC metrics for each spot using scuttle
+spe <- scuttle::addPerCellQCMetrics(spe, subsets = list(Mito = is.mito))
+colnames(colData(spe))
+
+# Identifying local outliers using SpotSweeper
+spe <- localOutliers(spe,
+                     metric = "sum",
+                     direction = "lower",
+                     log = TRUE
+)
 
-test_that("combined SPE gives contains correct colData", {
+spe <- localOutliers(spe,
+                     metric = "detected",
+                     direction = "lower",
+                     log = TRUE
+)
+
+spe <- localOutliers(spe,
+                     metric = "subsets_Mito_percent",
+                     direction = "higher",
+                     log = FALSE
+)
+
+# combine all outliers into "local_outliers" column
+spe$local_outliers <- as.logical(spe$sum_outliers) |
+  as.logical(spe$detected_outliers) |
+  as.logical(spe$subsets_Mito_percent_outliers)
+
+
+
+# === Tests ===
+test_that("example objects have correct class", {
+  expect_s4_class(spe, "SpatialExperiment")
+})
 
+test_that("correct outliers were found", {
+  expect_equal(sum(as.logical(spe$sum_outliers)), 7)
+  expect_equal(sum(as.logical(spe$detected_outliers)), 9)
+  expect_equal(sum(as.logical(spe$subsets_Mito_percent_outliers)), 2)
+  expect_equal(sum(as.logical(spe$local_outliers)), 11)
 })