diff --git a/R/metrics_qc.R b/R/metrics_qc.R
index 0413bfc5..69225b56 100644
--- a/R/metrics_qc.R
+++ b/R/metrics_qc.R
@@ -1,128 +1,135 @@
-
 #' Quality Control for Spatial Data
 #'
 #' This function identify spots in a
 #' [SpatialExperiment-class][SpatialExperiment::SpatialExperiment-class] (SPE)
 #'  with outlier quality control values: low `sum_umi` or `sum_gene`, or high
 #'  `expr_chrM_ratio`, utilizing `scran::isOutlier()`. Also identifies in-tissue
-#'  edge spots and distance to the edge for each spot.   
+#'  edge spots and distance to the edge for each spot.
 #'
 #' @param spe a [SpatialExperiment][SpatialExperiment::SpatialExperiment-class]
 #'
 #' @return A [SpatialExperiment][SpatialExperiment::SpatialExperiment-class]
 #' with added quiality control information added to the colData.
-#' 
+#'
 #' @export
 #'
 #' @examples
-#'  if (enough_ram()) {
+#' if (enough_ram()) {
 #'     ## Obtain the necessary data
 #'     if (!exists("spe")) spe <- fetch_data("spe")
-#'     
+#'
 #'     ## fake out tissue spots in example data (TODO add pre-qc data)
 #'     spe_qc <- spe
 #'     spe_qc$in_tissue[spe_qc$array_col < 10] <- FALSE
-#'     
+#'
 #'     ## adds QC metrics to colData of the spe
 #'     spe_qc <- metrics_qc(spe_qc)
 #'     colData(spe_qc)
-#'          
+#'
 #'     ## visualize edge spots
 #'     vis_clus(spe_qc, sampleid = "151507", clustervar = "edge_spot")
 #'     vis_gene(spe_qc, sampleid = "151507", geneid = "edge_distance", minCount = -1)
-#'     
+#'
 #'     ## visualize scran QC flags
-#'     
+#'
 #'     vis_clus(spe_qc, sample_id = "151507", clustervar = "scran_low_lib_size")
-#'     
-#'     scater::plotColData(spe_qc[,spe_qc$in_tissue], x = "sample_id", y = "sum_umi", colour_by = "scran_low_lib_size")
-#'     
+#'
+#'     scater::plotColData(spe_qc[, spe_qc$in_tissue], x = "sample_id", y = "sum_umi", colour_by = "scran_low_lib_size")
+#'
 #'     vis_clus(spe_qc, sampleid = "151507", clustervar = "scran_discard")
 #'     vis_clus(spe_qc, sampleid = "151507", clustervar = "scran_low_lib_size_edge")
-#'     
-#'     }
-#'     #'     
+#' }
+#' #'
 #'  @importFrom dplyr group_by summarize left_join select mutate
 #'  @importFrom SummarizedExperiment colData
-#'  @importFrom scater isOutlier 
+#'  @importFrom scater isOutlier
 metrics_qc <- function(spe) {
-  
-  stopifnot("in_tissue" %in% colnames(colData(spe)))
-  stopifnot("sum_umi" %in% colnames(colData(spe)))
-  stopifnot("sum_gene" %in% colnames(colData(spe)))
-  stopifnot("expr_chrM_ratio" %in% colnames(colData(spe)))
+    stopifnot("in_tissue" %in% colnames(colData(spe)))
+    stopifnot("sum_umi" %in% colnames(colData(spe)))
+    stopifnot("sum_gene" %in% colnames(colData(spe)))
+    stopifnot("expr_chrM_ratio" %in% colnames(colData(spe)))
+
+    spe_in <- spe[, spe$in_tissue]
+
+    ## QC in-tissue spots
+    qc_df <- data.frame(
+        log2sum = log2(spe_in$sum_umi),
+        log2detected = log2(spe_in$sum_gene),
+        subsets_Mito_percent = spe_in$expr_chrM_ratio * 100,
+        sample_id = spe_in$sample_id
+    )
+
+    qcfilter <- data.frame(
+        low_lib_size = scater::isOutlier(qc_df$log2sum, type = "lower", log = TRUE, batch = qc_df$sample_id),
+        low_n_features = scater::isOutlier(qc_df$log2detected, type = "lower", log = TRUE, batch = qc_df$sample_id),
+        high_subsets_Mito_percent = scater::isOutlier(qc_df$subsets_Mito_percent, type = "higher", batch = qc_df$sample_id)
+    ) |>
+        dplyr::mutate(discard = (low_lib_size | low_n_features) | high_subsets_Mito_percent)
+
+    ## Add qcfilter cols to colData(spe) after factoring
+    ## discard
+    spe$scran_discard <- NA
+    spe$scran_discard[which(spe$in_tissue)] <- qcfilter$discard
+    spe$scran_discard <- factor(spe$scran_discard, levels = c("TRUE", "FALSE"))
+
+    ## low_lib_size
+    spe$scran_low_lib_size <- NA
+    spe$scran_low_lib_size[which(spe$in_tissue)] <- qcfilter$low_lib_size
+    spe$scran_low_lib_size <- factor(spe$scran_low_lib_size,
+        levels = c("TRUE", "FALSE")
+    )
+    ## low_n_features
+    spe$scran_low_n_features <- NA
+    spe$scran_low_n_features[which(spe$in_tissue)] <- qcfilter$low_n_features
+    spe$sscran_low_n_features <- factor(spe$scran_low_n_features,
+        levels = c("TRUE", "FALSE")
+    )
+
+    ## high mito percent
+    spe$scran_high_subsets_Mito_percent <- NA
+    spe$scran_high_subsets_Mito_percent[which(spe$in_tissue)] <-
+        qcfilter$high_subsets_Mito_percent
+    spe$scran_high_subsets_Mito_percent <-
+        factor(spe$scran_high_subsets_Mito_percent, levels = c("TRUE", "FALSE"))
+
+    ## Find edge spots
+    spot_coords <- colData(spe_in) |>
+        as.data.frame() |>
+        select(in_tissue, sample_id, array_row, array_col) |>
+        group_by(sample_id, array_row) |>
+        mutate(
+            edge_col = array_col == min(array_col) | array_col == max(array_col),
+            col_distance = pmin(
+                abs(array_col - min(array_col)),
+                abs(array_col - max(array_col))
+            )
+        ) |>
+        group_by(sample_id, array_col) |>
+        mutate(
+            edge_row = array_row == min(array_row) | array_row == max(array_row),
+            row_distance = pmin(
+                abs(array_row - min(array_row)),
+                abs(array_row - max(array_row))
+            )
+        ) |>
+        group_by(sample_id) |>
+        mutate(
+            edge_spot = edge_row | edge_col,
+            edge_distance = pmin(row_distance, col_distance)
+        )
+
+
+    ## Add Edge info to spe
+    spe$edge_spot <- NA
+    spe$edge_spot[which(spe$in_tissue)] <- spot_coords$edge_spot
+    spe$edge_spot <- factor(spe$edge_spot, levels = c("TRUE", "FALSE"))
+
+    spe$edge_distance <- NA
+    spe$edge_distance[which(spe$in_tissue)] <- spot_coords$edge_distance
+
+    spe$scran_low_lib_size_edge <- NA
+    spe$scran_low_lib_size_edge[which(spe$in_tissue)] <- qcfilter$low_lib_size & spot_coords$edge_spot
+    spe$scran_low_lib_size_edge <- factor(spe$scran_low_lib_size_edge, levels = c("TRUE", "FALSE"))
 
-  spe_in <- spe[, spe$in_tissue]
-  
-  ## QC in-tissue spots
-  qc_df <- data.frame(
-    log2sum = log2(spe_in$sum_umi),
-    log2detected = log2(spe_in$sum_gene),
-    subsets_Mito_percent = spe_in$expr_chrM_ratio*100,
-    sample_id = spe_in$sample_id
-  )
-  
-  qcfilter <- data.frame(
-    low_lib_size = scater::isOutlier(qc_df$log2sum, type = "lower", log = TRUE, batch = qc_df$sample_id),
-    low_n_features = scater::isOutlier(qc_df$log2detected, type = "lower", log = TRUE, batch = qc_df$sample_id),
-    high_subsets_Mito_percent = scater::isOutlier(qc_df$subsets_Mito_percent, type = "higher", batch = qc_df$sample_id)
-  ) |> 
-    dplyr::mutate(discard  = (low_lib_size | low_n_features) | high_subsets_Mito_percent) 
-  
-  ## Add qcfilter cols to colData(spe) after factoring
-  ## discard
-  spe$scran_discard <- NA
-  spe$scran_discard[which(spe$in_tissue)] <- qcfilter$discard
-  spe$scran_discard <- factor(spe$scran_discard, levels = c("TRUE", "FALSE"))
-  
-  ## low_lib_size
-  spe$scran_low_lib_size <- NA
-  spe$scran_low_lib_size[which(spe$in_tissue)]  <- qcfilter$low_lib_size
-  spe$scran_low_lib_size <- factor(spe$scran_low_lib_size, 
-                                   levels = c("TRUE", "FALSE"))
-  ## low_n_features
-  spe$scran_low_n_features <- NA
-  spe$scran_low_n_features[which(spe$in_tissue)] <- qcfilter$low_n_features
-  spe$sscran_low_n_features<- factor(spe$scran_low_n_features, 
-                                     levels = c("TRUE", "FALSE"))
-  
-  ## high mito percent
-  spe$scran_high_subsets_Mito_percent <- NA
-  spe$scran_high_subsets_Mito_percent[which(spe$in_tissue)]  <- 
-    qcfilter$high_subsets_Mito_percent
-  spe$scran_high_subsets_Mito_percent <- 
-    factor(spe$scran_high_subsets_Mito_percent, levels = c("TRUE", "FALSE"))
-  
-  ## Find edge spots
-  spot_coords <- colData(spe_in) |>
-    as.data.frame() |>
-    select(in_tissue, sample_id, array_row, array_col) |>
-    group_by(sample_id, array_row) |>
-    mutate(edge_col = array_col == min(array_col) | array_col == max(array_col),
-           col_distance = pmin(abs(array_col - min(array_col)),
-                               abs(array_col - max(array_col)))
-           ) |>
-    group_by(sample_id, array_col) |>
-    mutate(edge_row = array_row == min(array_row) | array_row == max(array_row),
-           row_distance = pmin(abs(array_row - min(array_row)),
-                               abs(array_row - max(array_row)))
-           ) |>
-    group_by(sample_id) |>
-    mutate(edge_spot = edge_row | edge_col,
-           edge_distance = pmin(row_distance, col_distance))
-  
-  
-  ## Add Edge info to spe
-  spe$edge_spot <- NA
-  spe$edge_spot[which(spe$in_tissue)] <- spot_coords$edge_spot
-  spe$edge_spot <- factor(spe$edge_spot, levels = c("TRUE", "FALSE"))
-  
-  spe$edge_distance <- NA
-  spe$edge_distance[which(spe$in_tissue)]  <- spot_coords$edge_distance
-  
-  spe$scran_low_lib_size_edge <- NA
-  spe$scran_low_lib_size_edge[which(spe$in_tissue)] <- qcfilter$low_lib_size & spot_coords$edge_spot
-  spe$scran_low_lib_size_edge <- factor(spe$scran_low_lib_size_edge, levels = c("TRUE", "FALSE"))
-  
-  return(spe)
-}
\ No newline at end of file
+    return(spe)
+}
diff --git a/man/metrics_qc.Rd b/man/metrics_qc.Rd
index 76ff25a4..5f4850de 100644
--- a/man/metrics_qc.Rd
+++ b/man/metrics_qc.Rd
@@ -21,34 +21,33 @@ with outlier quality control values: low \code{sum_umi} or \code{sum_gene}, or h
 edge spots and distance to the edge for each spot.
 }
 \examples{
- if (enough_ram()) {
+if (enough_ram()) {
     ## Obtain the necessary data
     if (!exists("spe")) spe <- fetch_data("spe")
-    
+
     ## fake out tissue spots in example data (TODO add pre-qc data)
     spe_qc <- spe
     spe_qc$in_tissue[spe_qc$array_col < 10] <- FALSE
-    
+
     ## adds QC metrics to colData of the spe
     spe_qc <- metrics_qc(spe_qc)
     colData(spe_qc)
-         
+
     ## visualize edge spots
     vis_clus(spe_qc, sampleid = "151507", clustervar = "edge_spot")
     vis_gene(spe_qc, sampleid = "151507", geneid = "edge_distance", minCount = -1)
-    
+
     ## visualize scran QC flags
-    
+
     vis_clus(spe_qc, sample_id = "151507", clustervar = "scran_low_lib_size")
-    
-    scater::plotColData(spe_qc[,spe_qc$in_tissue], x = "sample_id", y = "sum_umi", colour_by = "scran_low_lib_size")
-    
+
+    scater::plotColData(spe_qc[, spe_qc$in_tissue], x = "sample_id", y = "sum_umi", colour_by = "scran_low_lib_size")
+
     vis_clus(spe_qc, sampleid = "151507", clustervar = "scran_discard")
     vis_clus(spe_qc, sampleid = "151507", clustervar = "scran_low_lib_size_edge")
-    
-    }
-    #'     
+}
+#'
  @importFrom dplyr group_by summarize left_join select mutate
  @importFrom SummarizedExperiment colData
- @importFrom scater isOutlier 
+ @importFrom scater isOutlier
 }
diff --git a/tests/testthat/test-metrics_qc.R b/tests/testthat/test-metrics_qc.R
index 56924cd0..e8473813 100644
--- a/tests/testthat/test-metrics_qc.R
+++ b/tests/testthat/test-metrics_qc.R
@@ -1,12 +1,11 @@
 test_that(
-  "metrics_qc returns modified spe",
-  {
-    if (!exists("spe")) spe <- fetch_data("spe")
-    
-    #run metrics spe
-    spe_qc <- metrics_qc(spe)
-    expect_equal(ncol(spe), ncol(spe_qc)) ## same number of spots
-    expect_equal(ncol(colData(spe)) + 8, ncol(colData(spe_qc))) ## add 8 QC cols to colData
-    
-  }
+    "metrics_qc returns modified spe",
+    {
+        if (!exists("spe")) spe <- fetch_data("spe")
+
+        # run metrics spe
+        spe_qc <- metrics_qc(spe)
+        expect_equal(ncol(spe), ncol(spe_qc)) ## same number of spots
+        expect_equal(ncol(colData(spe)) + 8, ncol(colData(spe_qc))) ## add 8 QC cols to colData
+    }
 )