diff --git a/R/filter.R b/R/filter.R
index 7678868..3433f51 100644
--- a/R/filter.R
+++ b/R/filter.R
@@ -192,7 +192,7 @@ filter_counts <- function(renee_ds) {
   log_counts <- log((as.matrix(df.filt[, samples_to_include] + 0.5)))
   rownames(log_counts) <- df.filt[, 1]
 
-  pca.df <- plot_pca(log_counts,
+  pcaPlot <- plot_pca(log_counts,
     sample_metadata,
     samples_to_include,
     samples_to_rename_manually,
diff --git a/R/pca.R b/R/pca.R
index db8337c..403119d 100644
--- a/R/pca.R
+++ b/R/pca.R
@@ -1,4 +1,4 @@
-#' Perform and plot a Principal Coordinate Analysis
+#' Perform and plot a Principal Components Analysis
 #'
 #' @param log_counts log-transformed filtered counts
 #' @inheritParams filter_counts
@@ -49,7 +49,7 @@ plot_pca <- function(log_counts,
 
   # plot PCA
   pcaPlot <- ggplot(pca.df, aes(x = xdata, y = ydata, text = sample)) +
-    geom_point(aes(color = group), text = sample, size = point_size_for_pca) +
+    geom_point(aes(color = group), size = point_size_for_pca) +
     theme_bw() +
     theme(
       legend.position = legend_position_for_pca,
diff --git a/tests/testthat/test-pca.R b/tests/testthat/test-pca.R
new file mode 100644
index 0000000..92c916f
--- /dev/null
+++ b/tests/testthat/test-pca.R
@@ -0,0 +1,59 @@
+log_counts <- structure(
+  c(
+    4.68203758078952, 4.85622577980595, 4.78525385564269,
+    4.49631900610197, 4.06045359796882, 4.23984554358195, 4.5945977606547,
+    4.64452454872198, 5.01435137189661, 4.97202092328668, 4.63530665635318,
+    4.2685504137767, 4.55963475259408, 4.48551456875509, 4.8871583679813,
+    4.80572893488115, 4.62485692334614, 3.28500783834613, 4.27910086185478,
+    4.44991091582054, 5.1543875974913, 5.04097648388479, 3.93341992672261,
+    3.60201340344135, 4.38819757498796, 4.52837375303351, 5.04374399504142,
+    5.16774070067169, 4.88762227733767, 3.4002608223214, 4.86037842060906,
+    5.24497940734463, 4.91946017962122, 5.04632856830695, 4.96825190546972,
+    4.23362208987531, 4.11790170891073, 4.05303076635639, 4.92868500786216,
+    4.94726383191083, 3.80832003906478, 4.59490650795107, 4.85738793032812,
+    4.40655427188147, 4.22382743940071, 4.58770826515446, 4.96057466313368,
+    4.99824283163569, 4.5437556841171, 4.97124300205374, 4.93192370480255,
+    4.13835014251084, 5.0939015418123, 5.03285949305828
+  ),
+  dim = c(6L, 9L),
+  dimnames = list(
+    c("Mrpl15_32", "Lypla1_34", "Tcea1_36", "Atp6v1h_44", "Rb1cc1_54", "Pcmtd1_68"),
+    c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")
+  )
+)
+sample_meta <- structure(
+  list(
+    Sample = c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3"),
+    Group = c("A", "A", "A", "B", "B", "B", "C", "C", "C"),
+    Replicate = c(1, 2, 3, 1, 2, 3, 1, 2, 3),
+    Batch = c(1, 2, 2, 1, 1, 2, 1, 2, 2),
+    Label = c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3")
+  ),
+  row.names = c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3"),
+  class = "data.frame"
+)
+
+test_that("plot_pca layers are expected", {
+  p <- plot_pca(log_counts,
+    sample_meta,
+    samples_to_include = c("A1", "A2", "A3", "B1", "B2", "B3", "C1", "C2", "C3"),
+    samples_to_rename_manually = NULL,
+    groups_column = "Group",
+    labels_column = "Label",
+    color_values = c(
+      "#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
+      "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"
+    ),
+    principal_component_on_x_axis = 1,
+    principal_component_on_y_axis = 2,
+    legend_position_for_pca = "top",
+    point_size_for_pca = 1,
+    add_labels_to_pca = TRUE,
+    label_font_size = 3,
+    label_offset_y_ = 2,
+    label_offset_x_ = 2
+  )
+
+  expect_s3_class(p$layers[[1]], "ggproto")
+  expect_s3_class(p$layers[[1]]$geom, "GeomPoint")
+})