diff --git a/CHANGELOG.md b/CHANGELOG.md
index 699ccfa..b1435c1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -8,6 +8,10 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+### Fixed
+
+- Proximity score violin plots now show every cell for each plotted marker, with proximity scores that were filtered out shown as 0.
+
 ### Changed
 
 - Colocalization heatmap text now explains that proteins are selected by mean abundance (up to 40 markers), globally across samples or within each cell type.
diff --git a/R/components.R b/R/components.R
index feb1d90..d48fe78 100644
--- a/R/components.R
+++ b/R/components.R
@@ -1628,7 +1628,8 @@ component_proximity_selected <-
 #' Create the component for proximity per marker
 #'
 #' This function creates plots visualizing the proximity scores for each marker
-#' across different samples and conditions.
+#' across different samples and conditions. Every component is shown for every
+#' plotted marker, with proximity scores that were filtered out set to 0.
 #'
 #' @param es_data An `es_data` object containing proximity scores and sample aliases.
 #' @param sample_palette A color palette for the samples.
@@ -1651,7 +1652,7 @@ component_proximity_per_marker <- function(
 
   plot_data <-
     proximity_scores %>%
-    filter(marker_1 == marker_2) %>%
+    complete_proximity_scores(only_self = TRUE) %>%
     {
       if (test_mode) {
         filter(., marker_1 %in% head(levels(marker_1), 10))
diff --git a/R/processing.R b/R/processing.R
index 5bc173b..52966ca 100644
--- a/R/processing.R
+++ b/R/processing.R
@@ -139,7 +139,8 @@ filter_proximity_scores <- function(
 #' Complete Proximity Scores
 #'
 #' This function completes the proximity scores by ensuring that each marker pair has a score for each sample component,
-#' filling missing values with 0.
+#' filling missing values with 0. Components are collected before `only_self` is applied, so a component is
+#' represented even when all of its scores for the kept marker pairs were filtered out.
 #'
 #' @param proximity_scores A data frame of proximity scores.
 #' @param only_self A boolean indicating whether to filter for self-comparisons only (default is TRUE).
@@ -156,18 +157,33 @@ complete_proximity_scores <-
     pixelatorR:::assert_class(proximity_scores, "tbl_df")
     pixelatorR:::assert_single_value(only_self, "bool")
 
+    components <-
+      proximity_scores %>%
+      ungroup() %>%
+      distinct(
+        sample_component, sample_alias, condition,
+        seurat_clusters, celltype
+      )
+
     if (only_self) {
       proximity_scores <-
         proximity_scores %>%
         filter(marker_1 == marker_2)
     }
 
-    proximity_scores %>%
+    fill_values <-
+      list(log2_ratio = 0, join_count_z = 0) %>%
+      keep(names(.) %in% colnames(proximity_scores))
+
+    completed <-
+      proximity_scores %>%
       complete(
-        nesting(sample_component, sample_alias, condition, seurat_clusters, celltype),
+        components,
         nesting(marker_1, marker_2),
-        fill = list(log2_ratio = 0)
+        fill = fill_values
       )
+
+    return(completed)
   }
 
 #' Summarize proximity scores per sample and condition
diff --git a/man/complete_proximity_scores.Rd b/man/complete_proximity_scores.Rd
index bb254d2..284bac7 100644
--- a/man/complete_proximity_scores.Rd
+++ b/man/complete_proximity_scores.Rd
@@ -16,5 +16,6 @@ A data frame of completed proximity scores with missing values filled.
 }
 \description{
 This function completes the proximity scores by ensuring that each marker pair has a score for each sample component,
-filling missing values with 0.
+filling missing values with 0. Components are collected before \code{only_self} is applied, so a component is
+represented even when all of its scores for the kept marker pairs were filtered out.
 }
diff --git a/man/component_proximity_per_marker.Rd b/man/component_proximity_per_marker.Rd
index d9011f2..41c330a 100644
--- a/man/component_proximity_per_marker.Rd
+++ b/man/component_proximity_per_marker.Rd
@@ -25,5 +25,6 @@ A list containing plots for each marker.
 }
 \description{
 This function creates plots visualizing the proximity scores for each marker
-across different samples and conditions.
+across different samples and conditions. Every component is shown for every
+plotted marker, with proximity scores that were filtered out set to 0.
 }
diff --git a/tests/testthat/test_components.R b/tests/testthat/test_components.R
index 91abde1..10d65cf 100644
--- a/tests/testthat/test_components.R
+++ b/tests/testthat/test_components.R
@@ -294,163 +294,142 @@ for (data_type in data_types) {
       component[[1]]$data,
       switch(data_type,
         default =
-          structure(list(
-            sample_alias = structure(c(
-              1L, 1L, 1L, 1L, 1L,
-              1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
-            ), levels = c("S1", "S2"), class = "factor"),
-            celltype = c(
-              "B", "B", "Mono & DC", "NK", "Platelets", "T",
-              "T", "B", "B", "B", "Mono & DC", "NK", "Platelets", "T"
-            ),
-            marker_1 = structure(c(
-              1L, 1L, NA, NA, NA, 1L, 1L, 1L, 1L,
-              1L, NA, NA, NA, 1L
-            ), levels = c("CD11b", "B2M", "HLA-ABC"), class = "factor"), marker_2 = structure(c(
-              1L, 1L, NA,
-              NA, NA, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L
-            ), levels = c(
-              "CD11b",
-              "B2M", "HLA-ABC"
-            ), class = "factor"), join_count = c(
-              0, 0,
-              NA, NA, NA, 0, 0, 0, 0, 0, NA, NA, NA, 0
-            ), join_count_expected_mean = c(
-              0.2,
-              0.12, NA, NA, NA, 0.04, 0, 0.2, 0, 0.12, NA, NA, NA, 0.04
-            ), join_count_expected_sd = c(
-              0.449466574975495, 0.32659863237109,
-              NA, NA, NA, 0.196946385566932, 0, 0.449466574975495, 0, 0.32659863237109,
-              NA, NA, NA, 0.196946385566932
-            ), join_count_z = c(
-              -0.2, -0.12,
-              NA, NA, NA, -0.04, 0, -0.2, 0, -0.12, NA, NA, NA, -0.04
-            ),
-            join_count_p = c(
-              0.420740290560897, 0.452241573979416, NA,
-              NA, NA, 0.484046563147169, 0.5, 0.420740290560897, 0.5, 0.452241573979416,
-              NA, NA, NA, 0.484046563147169
-            ), log2_ratio = c(
-              0, 0, NA,
-              NA, NA, 0, 0, 0, 0, 0, NA, NA, NA, 0
-            ), sample_component = c(
-              "S1_e2055911bf1693bd",
-              "S1_dc8d14e01732603e", NA, NA, NA, "S1_99aa508551f33cd3",
-              "S1_2ee5115016f6d3bf", "S2_e2055911bf1693bd", "S2_2ee5115016f6d3bf",
-              "S2_dc8d14e01732603e", NA, NA, NA, "S2_99aa508551f33cd3"
-            ),
-            count_1 = c(
-              49L, 21L, NA, NA, NA, 19L, 12L, 49L, 12L, 21L,
-              NA, NA, NA, 19L
-            ), count_2 = c(
-              49L, 21L, NA, NA, NA, 19L,
-              12L, 49L, 12L, 21L, NA, NA, NA, 19L
-            ), p1 = c(
-              0.016327890703099,
-              0.010989010989011, NA, NA, NA, 0.00842945874001775, 0.0184331797235023,
-              0.016327890703099, 0.0184331797235023, 0.010989010989011,
-              NA, NA, NA, 0.00842945874001775
-            ), p2 = c(
-              0.016327890703099,
-              0.010989010989011, NA, NA, NA, 0.00842945874001775, 0.0184331797235023,
-              0.016327890703099, 0.0184331797235023, 0.010989010989011,
-              NA, NA, NA, 0.00842945874001775
-            ), condition = c(
-              "good", "good",
-              NA, NA, NA, "good", "good", "good", "good", "good", NA, NA,
-              NA, "good"
-            ), seurat_clusters = c(
-              "1", "1", NA, NA, NA, "1",
-              "1", "1", "1", "1", NA, NA, NA, "1"
-            )
-          ), row.names = c(
-            NA,
-            -14L
-          ), class = c("tbl_df", "tbl", "data.frame")),
+          structure(list(sample_alias = structure(c(1L, 1L, 1L, 1L, 1L,
+          1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+          1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
+          2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), levels = c("S1",
+          "S2"), class = "factor"), celltype = c("B", "B", "B", "B", "B",
+          "B", "B", "B", "B", "B", "B", "B", "Mono & DC", "NK", "Platelets",
+          "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T", "B", "B",
+          "B", "B", "B", "B", "B", "B", "B", "B", "B", "Mono & DC", "NK",
+          "Platelets", "T", "T", "T", "T", "T", "T", "T", "T", "T", "T",
+          "T", "T"), sample_component = c("S1_128b348aca07cb57", "S1_218e57362e5c1a3d",
+          "S1_6d7587035b64afd5", "S1_a0525046264c9183", "S1_a9fec42b5a90b80a",
+          "S1_c716219b3865936c", "S1_d375cd40d5330b42", "S1_dc8d14e01732603e",
+          "S1_e2055911bf1693bd", "S1_e5bd60fc7282a0f8", "S1_f0dceb55e0820e2d",
+          "S1_ff94b67d225a95b9", NA, NA, NA, "S1_2ee5115016f6d3bf", "S1_500dadc305f7fd2d",
+          "S1_7e9a3f5ef0362b6d", "S1_844fa3a723a26b8a", "S1_887179ebbb0ff0a2",
+          "S1_99aa508551f33cd3", "S1_9f31266d8f62d933", "S1_b720a4c4f4bfac3e",
+          "S1_c57ec812b0a4d6e9", "S1_c8d43c718f2181fc", "S1_d0f9201e37a9e091",
+          "S2_2ee5115016f6d3bf", "S2_a0525046264c9183", "S2_a9fec42b5a90b80a",
+          "S2_c57ec812b0a4d6e9", "S2_c716219b3865936c", "S2_c8d43c718f2181fc",
+          "S2_d0f9201e37a9e091", "S2_d375cd40d5330b42", "S2_dc8d14e01732603e",
+          "S2_e2055911bf1693bd", "S2_ff94b67d225a95b9", NA, NA, NA, "S2_128b348aca07cb57",
+          "S2_218e57362e5c1a3d", "S2_500dadc305f7fd2d", "S2_6d7587035b64afd5",
+          "S2_7e9a3f5ef0362b6d", "S2_844fa3a723a26b8a", "S2_887179ebbb0ff0a2",
+          "S2_99aa508551f33cd3", "S2_9f31266d8f62d933", "S2_b720a4c4f4bfac3e",
+          "S2_e5bd60fc7282a0f8", "S2_f0dceb55e0820e2d"), condition = c("good",
+          "good", "good", "good", "good", "good", "good", "good", "good",
+          "good", "good", "good", NA, NA, NA, "good", "good", "good", "good",
+          "good", "good", "good", "good", "good", "good", "good", "good",
+          "good", "good", "good", "good", "good", "good", "good", "good",
+          "good", "good", NA, NA, NA, "good", "good", "good", "good", "good",
+          "good", "good", "good", "good", "good", "good", "good"), seurat_clusters = c("1",
+          "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", NA, NA,
+          NA, "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
+          "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", NA, NA, NA,
+          "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1"),
+              marker_1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+              1L, 1L, 1L, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+              1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA,
+              NA, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), levels = c("CD11b",
+              "B2M", "HLA-ABC"), class = "factor"), marker_2 = structure(c(1L,
+              1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L,
+              1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
+              1L, 1L, 1L, 1L, 1L, 1L, NA, NA, NA, 1L, 1L, 1L, 1L, 1L, 1L,
+              1L, 1L, 1L, 1L, 1L, 1L), levels = c("CD11b", "B2M", "HLA-ABC"
+              ), class = "factor"), join_count = c(NA, NA, NA, NA, NA,
+              NA, NA, 0, 0, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA,
+              0, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 0,
+              0, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0, NA, NA,
+              NA, NA), join_count_expected_mean = c(NA, NA, NA, NA, NA,
+              NA, NA, 0.12, 0.2, NA, NA, NA, NA, NA, NA, 0, NA, NA, NA,
+              NA, 0.04, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA,
+              NA, 0.12, 0.2, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
+              0.04, NA, NA, NA, NA), join_count_expected_sd = c(NA, NA,
+              NA, NA, NA, NA, NA, 0.32659863237109, 0.449466574975495,
+              NA, NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, 0.196946385566932,
+              NA, NA, NA, NA, NA, 0, NA, NA, NA, NA, NA, NA, NA, 0.32659863237109,
+              0.449466574975495, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
+              NA, 0.196946385566932, NA, NA, NA, NA), join_count_z = c(0,
+              0, 0, 0, 0, 0, 0, -0.12, -0.2, 0, 0, 0, NA, NA, NA, 0, 0,
+              0, 0, 0, -0.04, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, -0.12,
+              -0.2, 0, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, -0.04, 0, 0, 0,
+              0), join_count_p = c(NA, NA, NA, NA, NA, NA, NA, 0.452241573979416,
+              0.420740290560897, NA, NA, NA, NA, NA, NA, 0.5, NA, NA, NA,
+              NA, 0.484046563147169, NA, NA, NA, NA, NA, 0.5, NA, NA, NA,
+              NA, NA, NA, NA, 0.452241573979416, 0.420740290560897, NA,
+              NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.484046563147169,
+              NA, NA, NA, NA), log2_ratio = c(0, 0, 0, 0, 0, 0, 0, 0, 0,
+              0, 0, 0, NA, NA, NA, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+              0, 0, 0, 0, 0, 0, 0, 0, 0, 0, NA, NA, NA, 0, 0, 0, 0, 0,
+              0, 0, 0, 0, 0, 0, 0), count_1 = c(NA, NA, NA, NA, NA, NA,
+              NA, 21L, 49L, NA, NA, NA, NA, NA, NA, 12L, NA, NA, NA, NA,
+              19L, NA, NA, NA, NA, NA, 12L, NA, NA, NA, NA, NA, NA, NA,
+              21L, 49L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19L,
+              NA, NA, NA, NA), count_2 = c(NA, NA, NA, NA, NA, NA, NA,
+              21L, 49L, NA, NA, NA, NA, NA, NA, 12L, NA, NA, NA, NA, 19L,
+              NA, NA, NA, NA, NA, 12L, NA, NA, NA, NA, NA, NA, NA, 21L,
+              49L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 19L, NA,
+              NA, NA, NA), p1 = c(NA, NA, NA, NA, NA, NA, NA, 0.010989010989011,
+              0.016327890703099, NA, NA, NA, NA, NA, NA, 0.0184331797235023,
+              NA, NA, NA, NA, 0.00842945874001775, NA, NA, NA, NA, NA,
+              0.0184331797235023, NA, NA, NA, NA, NA, NA, NA, 0.010989010989011,
+              0.016327890703099, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,
+              NA, 0.00842945874001775, NA, NA, NA, NA), p2 = c(NA, NA,
+              NA, NA, NA, NA, NA, 0.010989010989011, 0.016327890703099,
+              NA, NA, NA, NA, NA, NA, 0.0184331797235023, NA, NA, NA, NA,
+              0.00842945874001775, NA, NA, NA, NA, NA, 0.0184331797235023,
+              NA, NA, NA, NA, NA, NA, NA, 0.010989010989011, 0.016327890703099,
+              NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.00842945874001775,
+              NA, NA, NA, NA)), row.names = c(NA, -52L), class = c("tbl_df",
+          "tbl", "data.frame")),
         hashing =
-          structure(list(
-            sample_alias = structure(c(
-              1L, 1L, 1L, 1L, 1L,
-              3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L
-            ), levels = c("S1", "S2", "S11", "S12"), class = "factor"), celltype = c(
-              "B",
-              "Mono & DC", "NK", "Platelets", "T", "B", "B", "Mono & DC", "NK",
-              "Platelets", "T", "B", "Mono & DC", "NK", "Platelets", "T", "B",
-              "Mono & DC", "NK", "Platelets", "T"
-            ), marker_1 = structure(c(
-              1L,
-              NA, NA, NA, 1L, 1L, 1L, NA, NA, NA, NA, 1L, NA, NA, NA, NA, 1L,
-              NA, NA, NA, 1L
-            ), levels = c("CD45", "B2M", "HLA-DR-DP-DQ"), class = "factor"),
-            marker_2 = structure(c(
-              1L, NA, NA, NA, 1L, 1L, 1L, NA, NA,
-              NA, NA, 1L, NA, NA, NA, NA, 1L, NA, NA, NA, 1L
-            ), levels = c(
-              "CD45",
-              "B2M", "HLA-DR-DP-DQ"
-            ), class = "factor"), join_count = c(
-              153,
-              NA, NA, NA, 345, 214, 258, NA, NA, NA, NA, 17, NA, NA, NA,
-              NA, 1, NA, NA, NA, 18
-            ), join_count_expected_mean = c(
-              143.82,
-              NA, NA, NA, 325.28, 162.28, 245.94, NA, NA, NA, NA, 15.12,
-              NA, NA, NA, NA, 1.08, NA, NA, NA, 19.49
-            ), join_count_expected_sd = c(
-              12.001161559944,
-              NA, NA, NA, 19.0899412934105, 13.1978571875678, 16.6192173052278,
+          structure(list(sample_alias = structure(c(1L, 1L, 1L, 1L, 1L,
+          3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L
+          ), levels = c("S1", "S2", "S11", "S12"), class = "factor"), celltype = c("B",
+          "Mono & DC", "NK", "Platelets", "T", "B", "B", "Mono & DC", "NK",
+          "Platelets", "T", "B", "Mono & DC", "NK", "Platelets", "T", "B",
+          "Mono & DC", "NK", "Platelets", "T"), sample_component = c("S1_d2146defe08567d3",
+          NA, NA, NA, "S1_68189b2c75de4098", "S11_19b04397ed7f04ba", "S11_fe556695f452a4bb",
+          NA, NA, NA, NA, "S12_63fab986007319b4", NA, NA, NA, "S12_3d23c6539cbead8d",
+          "S2_5bfdf506169806f2", NA, NA, NA, "S2_85f5bcdc05a7b286"), condition = c("good",
+          NA, NA, NA, "good", "good", "good", NA, NA, NA, NA, "good", NA,
+          NA, NA, "good", "good", NA, NA, NA, "good"), seurat_clusters = c("1",
+          NA, NA, NA, "1", "1", "1", NA, NA, NA, NA, "1", NA, NA, NA, "1",
+          "1", NA, NA, NA, "1"), marker_1 = structure(c(1L, NA, NA, NA,
+          1L, 1L, 1L, NA, NA, NA, NA, 1L, NA, NA, NA, 1L, 1L, NA, NA, NA,
+          1L), levels = c("CD45", "B2M", "HLA-DR-DP-DQ"), class = "factor"),
+              marker_2 = structure(c(1L, NA, NA, NA, 1L, 1L, 1L, NA, NA,
+              NA, NA, 1L, NA, NA, NA, 1L, 1L, NA, NA, NA, 1L), levels = c("CD45",
+              "B2M", "HLA-DR-DP-DQ"), class = "factor"), join_count = c(153,
+              NA, NA, NA, 345, 258, 214, NA, NA, NA, NA, 17, NA, NA, NA,
+              NA, 1, NA, NA, NA, 18), join_count_expected_mean = c(143.82,
+              NA, NA, NA, 325.28, 245.94, 162.28, NA, NA, NA, NA, 15.12,
+              NA, NA, NA, NA, 1.08, NA, NA, NA, 19.49), join_count_expected_sd = c(12.001161559944,
+              NA, NA, NA, 19.0899412934105, 16.6192173052278, 13.1978571875678,
               NA, NA, NA, NA, 3.47365263465745, NA, NA, NA, NA, 0.849004170076968,
-              NA, NA, NA, 4.54938113229357
-            ), join_count_z = c(
-              0.764925957720613,
-              NA, NA, NA, 1.03300474825489, 3.91881797665758, 0.725665943137187,
-              NA, NA, NA, NA, 0.541217040887393, NA, NA, NA, NA, -0.0800000000000001,
-              NA, NA, NA, -0.327517074668311
-            ), join_count_p = c(
-              0.222157817923894,
-              NA, NA, NA, 0.150800838294423, 4.44921405915763e-05, 0.234021792365565,
+              NA, NA, NA, 4.54938113229357), join_count_z = c(0.764925957720613,
+              NA, NA, NA, 1.03300474825489, 0.725665943137187, 3.91881797665758,
+              NA, NA, NA, NA, 0.541217040887393, NA, NA, NA, 0, -0.0800000000000001,
+              NA, NA, NA, -0.327517074668311), join_count_p = c(0.222157817923894,
+              NA, NA, NA, 0.150800838294423, 0.234021792365565, 4.44921405915763e-05,
               NA, NA, NA, NA, 0.294178996582506, NA, NA, NA, NA, 0.468118627986013,
-              NA, NA, NA, 0.371638415373185
-            ), log2_ratio = c(
-              0.0892673380970873,
-              NA, NA, NA, 0.0849142415955236, 0.399125588969599, 0.0690646698420441,
-              NA, NA, NA, NA, 0.169076606803992, NA, NA, NA, NA, -0.111031312388744,
-              NA, NA, NA, -0.114737184040853
-            ), sample_component = c(
-              "S1_d2146defe08567d3",
-              NA, NA, NA, "S1_68189b2c75de4098", "S11_fe556695f452a4bb",
-              "S11_19b04397ed7f04ba", NA, NA, NA, NA, "S12_63fab986007319b4",
-              NA, NA, NA, NA, "S2_5bfdf506169806f2", NA, NA, NA, "S2_85f5bcdc05a7b286"
-            ), count_1 = c(
-              934L, NA, NA, NA, 1274L, 906L, 1582L, NA,
-              NA, NA, NA, 345L, NA, NA, NA, NA, 77L, NA, NA, NA, 409L
-            ),
-            count_2 = c(
-              934L, NA, NA, NA, 1274L, 906L, 1582L, NA, NA,
-              NA, NA, 345L, NA, NA, NA, NA, 77L, NA, NA, NA, 409L
-            ), p1 = c(
-              0.365271802894016,
-              NA, NA, NA, 0.594493700419972, 0.46749226006192, 0.366373320981936,
+              NA, NA, NA, 0.371638415373185), log2_ratio = c(0.0892673380970873,
+              NA, NA, NA, 0.0849142415955236, 0.0690646698420441, 0.399125588969599,
+              NA, NA, NA, NA, 0.169076606803992, NA, NA, NA, 0, -0.111031312388744,
+              NA, NA, NA, -0.114737184040853), count_1 = c(934L, NA, NA,
+              NA, 1274L, 1582L, 906L, NA, NA, NA, NA, 345L, NA, NA, NA,
+              NA, 77L, NA, NA, NA, 409L), count_2 = c(934L, NA, NA, NA,
+              1274L, 1582L, 906L, NA, NA, NA, NA, 345L, NA, NA, NA, NA,
+              77L, NA, NA, NA, 409L), p1 = c(0.365271802894016, NA, NA,
+              NA, 0.594493700419972, 0.366373320981936, 0.46749226006192,
               NA, NA, NA, NA, 0.109211775878443, NA, NA, NA, NA, 0.0275985663082437,
-              NA, NA, NA, 0.116690442225392
-            ), p2 = c(
-              0.365271802894016,
-              NA, NA, NA, 0.594493700419972, 0.46749226006192, 0.366373320981936,
+              NA, NA, NA, 0.116690442225392), p2 = c(0.365271802894016,
+              NA, NA, NA, 0.594493700419972, 0.366373320981936, 0.46749226006192,
               NA, NA, NA, NA, 0.109211775878443, NA, NA, NA, NA, 0.0275985663082437,
-              NA, NA, NA, 0.116690442225392
-            ), condition = c(
-              "good", NA,
-              NA, NA, "good", "good", "good", NA, NA, NA, NA, "good", NA,
-              NA, NA, NA, "good", NA, NA, NA, "good"
-            ), seurat_clusters = c(
-              "1",
-              NA, NA, NA, "1", "1", "1", NA, NA, NA, NA, "1", NA, NA, NA,
-              NA, "1", NA, NA, NA, "1"
-            )
-          ), row.names = c(NA, -21L), class = c(
-            "tbl_df",
-            "tbl", "data.frame"
-          ))
+              NA, NA, NA, 0.116690442225392)), row.names = c(NA, -21L), class = c("tbl_df",
+          "tbl", "data.frame"))
       )
     )
 
diff --git a/tests/testthat/test_processing.R b/tests/testthat/test_processing.R
index 7006ac5..bdd9897 100644
--- a/tests/testthat/test_processing.R
+++ b/tests/testthat/test_processing.R
@@ -389,3 +389,75 @@ test_that("Proximity ANOVAs work as expected", {
     ))
   )
 })
+
+test_that("Proximity score completion works as expected", {
+  proximity_scores <- tibble(
+    sample_component = c("c1", "c1", "c2"),
+    sample_alias = factor(c("S1", "S1", "S2"), levels = c("S1", "S2")),
+    condition = "unstim",
+    seurat_clusters = "1",
+    celltype = "T",
+    marker_1 = factor(c("CD3", "CD3", "CD4"), levels = c("CD3", "CD4")),
+    marker_2 = factor(c("CD3", "CD4", "CD4"), levels = c("CD3", "CD4")),
+    log2_ratio = c(0.5, 0.2, 0.1),
+    join_count_z = c(1.2, 0.3, 0.8)
+  )
+
+  expect_equal(
+    complete_proximity_scores(proximity_scores),
+    structure(list(sample_component = c("c1", "c1", "c2", "c2"), sample_alias = structure(c(
+      1L,
+      1L, 2L, 2L
+    ), levels = c("S1", "S2"), class = "factor"), condition = c(
+      "unstim",
+      "unstim", "unstim", "unstim"
+    ), seurat_clusters = c(
+      "1", "1",
+      "1", "1"
+    ), celltype = c("T", "T", "T", "T"), marker_1 = structure(c(
+      1L,
+      2L, 1L, 2L
+    ), levels = c("CD3", "CD4"), class = "factor"), marker_2 = structure(c(
+      1L,
+      2L, 1L, 2L
+    ), levels = c("CD3", "CD4"), class = "factor"), log2_ratio = c(
+      0.5,
+      0, 0, 0.1
+    ), join_count_z = c(1.2, 0, 0, 0.8)), row.names = c(
+      NA,
+      -4L
+    ), class = c("tbl_df", "tbl", "data.frame"))
+  )
+
+  expect_equal(
+    complete_proximity_scores(proximity_scores, only_self = FALSE),
+    structure(list(sample_component = c(
+      "c1", "c1", "c1", "c2", "c2",
+      "c2"
+    ), sample_alias = structure(c(1L, 1L, 1L, 2L, 2L, 2L), levels = c(
+      "S1",
+      "S2"
+    ), class = "factor"), condition = c(
+      "unstim", "unstim", "unstim",
+      "unstim", "unstim", "unstim"
+    ), seurat_clusters = c(
+      "1", "1",
+      "1", "1", "1", "1"
+    ), celltype = c(
+      "T", "T", "T", "T", "T",
+      "T"
+    ), marker_1 = structure(c(1L, 1L, 2L, 1L, 1L, 2L), levels = c(
+      "CD3",
+      "CD4"
+    ), class = "factor"), marker_2 = structure(c(
+      1L, 2L, 2L, 1L,
+      2L, 2L
+    ), levels = c("CD3", "CD4"), class = "factor"), log2_ratio = c(
+      0.5,
+      0.2, 0, 0, 0, 0.1
+    ), join_count_z = c(1.2, 0.3, 0, 0, 0, 0.8)), row.names = c(
+      NA,
+      -6L
+    ), class = c("tbl_df", "tbl", "data.frame"))
+  )
+})