diff --git a/.Rbuildignore b/.Rbuildignore
index c6056136..cb4457b1 100644
--- a/.Rbuildignore
+++ b/.Rbuildignore
@@ -10,3 +10,6 @@
 ^codecov\.yml$
 ^vignettes/$
 ^vignettes/avid\.Rmd$
+splenocytes.zip
+splenocytes
+^inst/bench$
diff --git a/.github/workflows/check-standard.yaml b/.github/workflows/check-standard.yaml
index a3ac6182..b39f0e90 100644
--- a/.github/workflows/check-standard.yaml
+++ b/.github/workflows/check-standard.yaml
@@ -22,7 +22,7 @@ jobs:
           - {os: windows-latest, r: 'release'}
           - {os: ubuntu-latest,   r: 'devel', http-user-agent: 'release'}
           - {os: ubuntu-latest,   r: 'release'}
-          - {os: ubuntu-latest,   r: 'oldrel-1'}
+          # - {os: ubuntu-latest,   r: 'oldrel-1'}
 
     env:
       GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
diff --git a/DESCRIPTION b/DESCRIPTION
index 63b40b72..3181f1ca 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,21 +1,22 @@
 Package: djvdj
 Title: A collection of single-cell V(D)J tools
-Version: 0.0.0.9000
+Version: 0.99.0
 Authors@R: c(
     person("Ryan", "Sheridan", , "ryan.sheridan@cuanschutz.edu", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0003-4012-3147")),
-    person("Jay", "Hesselberth", , "jay.hesselberth@gmail.com", role = "ctb"),
+    person("Jay", "Hesselberth", , "jay.hesselberth@cuanschutz.com", role = "ctb"),
     person("Rui", "Fu", , "raysinensis@gmail.com", role = "ctb"),
-    person("Kent", "Riemondy", , "kent.riemondy@ucdenver.edu", role = "ctb"),
-    person("RNA Bioscience Initiative", role = "fnd")
+    person("Kent", "Riemondy", , "kent.riemondy@cuanschutz.edu", role = "ctb"),
+    person("RNA Bioscience Initiative", role = c("fnd", "cph"))
   )
-Description: djvdj provides a range of tools to analyze single-cell V(D)J
-    data.
+Description: djvdj provides import, manipulation, analysis, and plotting tools
+  for analyzing single-cell V(D)J gene expression data, integrating with both
+  Seurat and SigleCellExperiment-based workflows.
 License: MIT + file LICENSE
 URL: https://rnabioco.github.io/djvdj/, https://github.com/rnabioco/djvdj/
 BugReports: https://github.com/rnabioco/djvdj/issues
 Depends: 
-    R (>= 4.0.0)
+    R (>= 4.2.0)
 Imports: 
     abdiv,
     Biostrings,
@@ -29,6 +30,8 @@ Imports:
     ggrepel,
     ggseqlogo,
     glue,
+    graphics,
+    grid,
     igraph (>= 1.3.0),
     iNEXT,
     MASS,
@@ -44,6 +47,7 @@ Imports:
     stringr,
     tibble,
     tidyr,
+    utils,
     uwot
 Suggests: 
     covr,
@@ -54,13 +58,14 @@ Suggests:
     RColorBrewer,
     rmarkdown,
     roxygen2,
-    testthat
+    testthat (>= 3.0.0)
 VignetteBuilder: 
     knitr
-Remotes:
-    tidyverse/magrittr
-Config/Needs/website:pkgdown, rnabioco/rbitemplate
+Config/Needs/website:
+  pkgdown,
+  rnabioco/rbitemplate
+biocViews: DataImport, GeneExpression, SingleCell, RNASeq, ImmunoOncology
 Encoding: UTF-8
-LazyData: true
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.1
+Config/testthat/edition: 3
diff --git a/NEWS.md b/NEWS.md
index e4362e93..111e272f 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,3 +1,3 @@
-# djvdj 0.0.0.9000
+# djvdj 0.99.0
 
-* Added a `NEWS.md` file to track changes to the package.
+* Initial release.
diff --git a/R/calc-abundance.R b/R/calc-abundance.R
index 366cabff..9e9eff32 100644
--- a/R/calc-abundance.R
+++ b/R/calc-abundance.R
@@ -130,7 +130,7 @@ calc_frequency <- function(input, data_col, cluster_col = NULL, prefix = paste0
   res <- dplyr::mutate(
     res,
     .freq = dplyr::n_distinct(!!sym(cell_col)),
-    .pct  = (.data$.freq / .data$.n_cells) * 100
+    .pct  = (.freq / .n_cells) * 100
   )
 
   # Identify shared labels
@@ -188,7 +188,7 @@ calc_frequency <- function(input, data_col, cluster_col = NULL, prefix = paste0
   )
 
   # Format labels
-  labs <- dplyr::group_by(labs, .data$grp)
+  labs <- dplyr::group_by(labs, grp)
   labs <- dplyr::mutate(
     labs,
     lab = paste0(unique(range(x)), collapse = "-")
@@ -442,7 +442,7 @@ plot_clonal_abundance <- function(input, cluster_col = NULL,
   if (n_clones > 0) {
     res <- res +
       ggrepel::geom_text_repel(
-        ggplot2::aes(label = .data$.lab),
+        ggplot2::aes(label = .lab),
         data          = top_clones,
         nudge_x       = 500,
         direction     = "y",
@@ -524,7 +524,7 @@ plot_frequency <- function(input, data_col, cluster_col = NULL,
   plt_dat <- dplyr::group_by(plt_dat, !!sym(data_col))
 
   rnk <- dplyr::summarize(plt_dat, mn = mean(!!sym(abun_col)))
-  rnk <- dplyr::arrange(rnk, desc(.data$mn))
+  rnk <- dplyr::arrange(rnk, desc(mn))
   rnk <- pull(rnk, data_col)
 
   plt_dat <- dplyr::ungroup(plt_dat)
diff --git a/R/calc-diversity.R b/R/calc-diversity.R
index e2c26f35..f15b4631 100644
--- a/R/calc-diversity.R
+++ b/R/calc-diversity.R
@@ -34,7 +34,7 @@
 #'   method   = abdiv::simpson
 #' )
 #'
-#' head(res@meta.data, 1)
+#' head(slot(res, 'meta.data'), 1)
 #'
 #' # Group cells based on meta.data column before calculating diversity
 #' res <- calc_diversity(
@@ -43,7 +43,7 @@
 #'   cluster_col = "orig.ident"
 #' )
 #'
-#' head(res@colData, 1)
+#' head(slot(res, 'colData'), 1)
 #'
 #' # Add a prefix to the new columns
 #' # this is useful if multiple diversity calculations are stored in the
@@ -54,7 +54,7 @@
 #'   prefix   = "bcr_"
 #' )
 #'
-#' head(res@meta.data, 1)
+#' head(slot(res, 'meta.data'), 1)
 #'
 #' # Calculate multiple metrics
 #' res <- calc_diversity(
@@ -66,7 +66,7 @@
 #'   )
 #' )
 #'
-#' head(res@colData, 1)
+#' head(slot(res, 'colData'), 1)
 #'
 #' # Return a data.frame instead of adding the results to the input object
 #' res <- calc_diversity(
@@ -186,18 +186,18 @@ calc_diversity <- function(input, data_col, cluster_col = NULL,
       sam,
       met       = .y,
       diversity = list(.calc_div(!!sym(data_col), met = .x)),
-      stderr    = purrr::map_dbl(.data$diversity, pull, "std.error"),
-      diversity = purrr::map_dbl(.data$diversity, pull, "statistic")
+      stderr    = purrr::map_dbl(diversity, pull, "std.error"),
+      diversity = purrr::map_dbl(diversity, pull, "statistic")
     )
   })
 
   div_cols <- "diversity"
 
   if (n_boots > 1) div_cols <- c(div_cols, "stderr")
-  else             div <- dplyr::select(div, -.data$stderr)
+  else             div <- dplyr::select(div, -stderr)
 
   div <- tidyr::pivot_longer(div, all_of(div_cols))
-  div <- tidyr::unite(div, "name", .data$met, .data$name)
+  div <- tidyr::unite(div, "name", met, name)
   div <- tidyr::pivot_wider(div)
 
   # Format results
@@ -372,12 +372,12 @@ plot_diversity <- function(input, data_col, cluster_col = NULL,
 
   re <- "^(.+)_(diversity|stderr)$"
 
-  plt_dat <- tidyr::extract(plt_dat, .data$name, into = c("met", "type"), re)
+  plt_dat <- tidyr::extract(plt_dat, name, into = c("met", "type"), re)
 
   plt_dat <- tidyr::pivot_wider(
     plt_dat,
-    names_from  = .data$type,
-    values_from = .data$value
+    names_from  = type,
+    values_from = value
   )
 
   # Set plot levels
@@ -439,8 +439,8 @@ plot_diversity <- function(input, data_col, cluster_col = NULL,
       ggplot2::geom_linerange(
         aes(
           !!sym(cluster_col),
-          ymin = .data$diversity - .data$stderr,
-          ymax = .data$diversity + .data$stderr
+          ymin = diversity - stderr,
+          ymax = diversity + stderr
         )
       )
   }
@@ -560,13 +560,13 @@ plot_rarefaction <- function(input, data_col, cluster_col = NULL,
 
   plt_dat <- dplyr::mutate(
     plt_dat,
-    method = dplyr::recode(.data$Method, "Observed" = "Rarefaction"),
+    method = dplyr::recode(Method, "Observed" = "Rarefaction"),
     method = stringr::str_to_lower(method),
-    Order.q = met_labs[as.character(.data$Order.q)]
+    Order.q = met_labs[as.character(Order.q)]
   )
 
   if (!is.null(cluster_col)) {
-    plt_dat <- dplyr::rename(plt_dat, !!sym(cluster_col) := .data$Assemblage)
+    plt_dat <- dplyr::rename(plt_dat, !!sym(cluster_col) := Assemblage)
   }
 
   plt_dat <- .set_lvls(plt_dat, cluster_col, plot_lvls)
@@ -576,13 +576,13 @@ plot_rarefaction <- function(input, data_col, cluster_col = NULL,
   # Plot standard error
   res <- ggplot2::ggplot(
     plt_dat,
-    ggplot2::aes(.data$m, .data$qD, linetype = method)
+    ggplot2::aes(m, qD, linetype = method)
   ) +
     ggplot2::guides(linetype = ggplot2::guide_legend(title = NULL))
 
   if (n_boots > 1) {
     gg_aes <- ggplot2::aes(
-      x = .data$m, ymin = .data$qD.LCL, ymax = .data$qD.UCL
+      x = m, ymin = qD.LCL, ymax = qD.UCL
     )
 
     if (!is.null(cluster_col))   gg_aes$fill <- sym(cluster_col)
diff --git a/R/calc-gene-usage.R b/R/calc-gene-usage.R
index 52eed287..5ce8b3ba 100644
--- a/R/calc-gene-usage.R
+++ b/R/calc-gene-usage.R
@@ -127,7 +127,7 @@ calc_gene_usage <- function(input, data_cols, cluster_col = NULL, chain = NULL,
     res <- tidyr::pivot_wider(
       res,
       names_from  = all_of(clst_nm),
-      values_from = .data$freq,
+      values_from = freq,
       values_fill = 0
     )
 
@@ -154,8 +154,8 @@ calc_gene_usage <- function(input, data_cols, cluster_col = NULL, chain = NULL,
     )
   }
 
-  res <- dplyr::mutate(res, pct = (.data$freq / .data$n_cells) * 100)
-  res <- dplyr::arrange(res, desc(.data$pct))
+  res <- dplyr::mutate(res, pct = (freq / n_cells) * 100)
+  res <- dplyr::arrange(res, desc(pct))
 
   res
 }
@@ -617,12 +617,12 @@ plot_gene_usage <- function(input, data_cols, cluster_col = NULL,
   res <- dplyr::mutate(res, rnk = row_number(desc(!!sym(dat_col))))
 
   if (is.null(clst_col)) {
-    res <- dplyr::filter(res, !!gn_2 %in% top_2 | .data$rnk == 1)
+    res <- dplyr::filter(res, !!gn_2 %in% top_2 | rnk == 1)
 
   } else {
     res <- dplyr::filter(
       res,
-      !!gn_2 %in% top_2[[dplyr::cur_group()[[clst_col]]]] | .data$rnk == 1
+      !!gn_2 %in% top_2[[dplyr::cur_group()[[clst_col]]]] | rnk == 1
     )
   }
 
diff --git a/R/calc-similarity.R b/R/calc-similarity.R
index 8303695e..16d522aa 100644
--- a/R/calc-similarity.R
+++ b/R/calc-similarity.R
@@ -128,7 +128,7 @@ calc_similarity <- function(input, data_col, cluster_col, method = abdiv::jaccar
   vdj <- tidyr::pivot_wider(
     vdj,
     names_from  = all_of(cluster_col),
-    values_from = .data$n,
+    values_from = n,
     values_fill = 0
   )
 
@@ -158,20 +158,20 @@ calc_similarity <- function(input, data_col, cluster_col, method = abdiv::jaccar
   })
 
   # Combine with inverse combinations
-  res_i <- dplyr::rename(res, Var1 = .data$Var2, Var2 = .data$Var1)
+  res_i <- dplyr::rename(res, Var1 = Var2, Var2 = Var1)
   res   <- dplyr::bind_rows(res, res_i, res_s)
 
   # Format data.frame
   clmns <- sort(unique(res$Var2))
-  res   <- dplyr::arrange(res, .data$Var2)
+  res   <- dplyr::arrange(res, Var2)
 
   res <- tidyr::pivot_wider(
     res,
-    names_from  = .data$Var1,
-    values_from = .data$sim
+    names_from  = Var1,
+    values_from = sim
   )
 
-  res <- dplyr::select(res, !!sym(cluster_col) := .data$Var2, all_of(clmns))
+  res <- dplyr::select(res, !!sym(cluster_col) := Var2, all_of(clmns))
 
   # Return matrix
   if (return_mat) {
@@ -241,6 +241,7 @@ calc_similarity <- function(input, data_col, cluster_col, method = abdiv::jaccar
 #' circos plot
 #' @importFrom abdiv jaccard
 #' @seealso [calc_similarity()], [calc_mds()], [plot_mds()]
+#' @return ggplot object
 #'
 #' @examples
 #' # Plot repertoire overlap
@@ -492,6 +493,7 @@ calc_mds <- function(input, data_col, cluster_col, method = abdiv::jaccard,
 #' @param sep Separator used for storing per-chain V(D)J data for each cell
 #' @param ... Additional arguments to pass to [ggplot2::geom_point()]
 #' @seealso [calc_mds()], [calc_similarity()], [plot_similarity()], [MASS::isoMDS()]
+#' @return ggplot object
 #' @export
 plot_mds <- function(input, data_col, cluster_col,
                      method = abdiv::jaccard, chain = NULL,
diff --git a/R/cluster-seqs.R b/R/cluster-seqs.R
index 5800c6de..419f8905 100644
--- a/R/cluster-seqs.R
+++ b/R/cluster-seqs.R
@@ -97,7 +97,7 @@ cluster_sequences <- function(input, data_col = "cdr3", chain = NULL,
 
   make_adj_df <- function(mat) {
     res <- tibble::as_tibble(mat, rownames = "Var1")
-    res <- tidyr::pivot_longer(res, -.data$Var1, values_to = "Var2")
+    res <- tidyr::pivot_longer(res, -Var1, values_to = "Var2")
     res
   }
 
@@ -105,8 +105,8 @@ cluster_sequences <- function(input, data_col = "cdr3", chain = NULL,
 
   adj_df <- dplyr::mutate(
     adj_df,
-    Var1 = seqs[as.integer(.data$Var1)],
-    Var2 = seqs[.data$Var2]
+    Var1 = seqs[as.integer(Var1)],
+    Var2 = seqs[Var2]
   )
 
   # Create adjacency graph
@@ -188,6 +188,7 @@ cluster_sequences <- function(input, data_col = "cdr3", chain = NULL,
 #' @param ... Additional parameters to pass to [ggseqlogo::geom_logo()]
 #' @importFrom stringr str_trunc
 #' @seealso [cluster_sequences()]
+#' @return ggplot object
 #' @export
 plot_motifs <- function(input, data_col = "cdr3", cluster_col = NULL,
                         chain, plot_colors = NULL,
diff --git a/R/filter-vdj.R b/R/filter-vdj.R
index c761bf76..ab937af0 100644
--- a/R/filter-vdj.R
+++ b/R/filter-vdj.R
@@ -112,7 +112,7 @@ filter_vdj <- function(input, filt, data_cols = NULL,
   }
 
   keep_rows <- vdj$.KEEP
-  vdj       <- dplyr::select(vdj, -.data$.KEEP)
+  vdj       <- dplyr::select(vdj, -.KEEP)
 
   # If vectors in keep_rows are all length 1, filter cells
   if (length_one) {
diff --git a/R/import-vdj.R b/R/import-vdj.R
index f7fd745e..e7651890 100644
--- a/R/import-vdj.R
+++ b/R/import-vdj.R
@@ -193,7 +193,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
     bcs <- .get_meta(input)[[CELL_COL]]
 
     prfx_df <- .extract_cell_prefix(bcs, strip_bcs = FALSE)
-    prfx_df <- dplyr::distinct(prfx_df, .data$prfx, .data$sfx)
+    prfx_df <- dplyr::distinct(prfx_df, prfx, sfx)
 
     prfxs <- prfx_df$prfx
     sfxs  <- prfx_df$sfx
@@ -274,8 +274,8 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
     contigs <- purrr::map(
       contigs,
       mutate,
-      contig_sfx = unlist(.str_extract_all(.data$contig_id, "_contig_[0-9]+$")),
-      contig_id  = paste0(.data$barcode, .data$contig_sfx),
+      contig_sfx = unlist(.str_extract_all(contig_id, "_contig_[0-9]+$")),
+      contig_id  = paste0(barcode, contig_sfx),
       contig_sfx = NULL
     )
 
@@ -368,7 +368,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
       "these contigs will be removed."
     )
 
-    contigs <- dplyr::filter(contigs, !is.na(.data$clonotype_id))
+    contigs <- dplyr::filter(contigs, !is.na(clonotype_id))
   }
 
   # Select V(D)J columns to keep
@@ -402,7 +402,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   vdj_cols  <- c(vdj_cols, "paired")
 
   if (filter_paired) {
-    contigs <- dplyr::filter(contigs, .data$paired)
+    contigs <- dplyr::filter(contigs, paired)
   }
 
   # Order chains and CDR3 sequences
@@ -411,7 +411,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   # the clonotype ID
   contigs <- dplyr::arrange(
     contigs,
-    .data$barcode, .data$chains, .data$cdr3_nt
+    barcode, chains, cdr3_nt
   )
 
   # Extract isotypes from c_gene for IGH chain (for BCR data only)
@@ -438,13 +438,13 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   )
 
   # Reorder columns
-  meta <- dplyr::relocate(meta, .data$paired, .after = "full_length")
+  meta <- dplyr::relocate(meta, paired, .after = "full_length")
   meta <- dplyr::relocate(meta, all_of(len_cols), .after = last(cdr3_cols))
-  meta <- dplyr::relocate(meta, .data$n_chains, .after = "chains")
+  meta <- dplyr::relocate(meta, n_chains, .after = "chains")
   meta <- dplyr::relocate(meta, all_of(gene_cols), .after = last(len_cols))
 
   if (vdj_class %in% c("BCR", "Multi")) {
-    meta <- dplyr::relocate(meta, .data$isotype, .after = "c_gene")
+    meta <- dplyr::relocate(meta, isotype, .after = "c_gene")
   }
 
   # Check for duplicated cell barcodes
@@ -487,7 +487,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
   # Filter to only include cells with valid clonotype_id
   # cells with missing clonotype have a clonotype_id of 'None'
-  res <- dplyr::filter(res, .data$clonotype_id != "None")
+  res <- dplyr::filter(res, clonotype_id != "None")
 
   if (nrow(res) == 0) {
     warning("No valid clonotypes present, check input data.")
@@ -539,8 +539,8 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
     d <- dplyr::rename(
       d,
-      chains       = .data$chain,
-      clonotype_id = .data$raw_clonotype_id
+      chains       = chain,
+      clonotype_id = raw_clonotype_id
     )
 
     d
@@ -595,15 +595,15 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   )
 
   # Filter for contigs in cells
-  res <- dplyr::filter(res, .data$is_cell)
+  res <- dplyr::filter(res, is_cell)
 
   # Replace 'None' with FALSE for QC columns
   res <- .replace_none(res, chk_none)
 
   res <- dplyr::rename(
     res,
-    chains       = .data$chain,
-    clonotype_id = .data$raw_clonotype_id
+    chains       = chain,
+    clonotype_id = raw_clonotype_id
   )
 
   # Format cell barcode prefixes
@@ -633,7 +633,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   prfx_df <- .extract_cell_prefix(bcs, strip_bcs = TRUE)
 
   # Match old and new prefixes
-  new <- dplyr::distinct(prfx_df, .data$prfx, .data$sfx)
+  new <- dplyr::distinct(prfx_df, prfx, sfx)
 
   if (nrow(new) != length(cell_prfxs)) {
     stop(
@@ -650,9 +650,9 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   # Format cell barcodes
   prfx_df <- dplyr::mutate(
     prfx_df,
-    prfx = ifelse(is.na(.data$new_prfx), .data$prfx, .data$new_prfx),
-    sfx  = ifelse(is.na(.data$new_sfx), .data$sfx, .data$new_sfx),
-    bc   = paste0(.data$prfx, .data$bc, .data$sfx)
+    prfx = ifelse(is.na(new_prfx), prfx, new_prfx),
+    sfx  = ifelse(is.na(new_sfx), sfx, new_sfx),
+    bc   = paste0(prfx, bc, sfx)
   )
 
   df_in[[bc_col]] <- prfx_df$bc
@@ -678,8 +678,8 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   if (strip_bcs) {
     res <- dplyr::mutate(
       res,
-      bc = stringr::str_remove(.data$bc, paste0("^", .data$prfx)),
-      bc = stringr::str_remove(.data$bc, paste0(.data$sfx, "$"))
+      bc = stringr::str_remove(bc, paste0("^", prfx)),
+      bc = stringr::str_remove(bc, paste0(sfx, "$"))
     )
   }
 
@@ -786,7 +786,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   res <- purrr::map(
     res,
     mutate,
-    barcode = unlist(.str_extract_all(.data$contig_id, id_re))
+    barcode = unlist(.str_extract_all(contig_id, id_re))
   )
 
   # Format cell barcode prefixes
@@ -801,8 +801,8 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   res <- purrr::map(
     res,
     mutate,
-    contig_sfx = unlist(.str_extract_all(.data$contig_id, "_contig_[0-9]+$")),
-    contig_id  = paste0(.data$barcode, .data$contig_sfx),
+    contig_sfx = unlist(.str_extract_all(contig_id, "_contig_[0-9]+$")),
+    contig_id  = paste0(barcode, contig_sfx),
     contig_sfx = NULL,
     barcode    = NULL
   )
@@ -824,30 +824,30 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
   bam_info <- dplyr::filter(
     bam_info,
-    grepl("_contig_[0-9]+$", .data$contig_id)
+    grepl("_contig_[0-9]+$", contig_id)
   )
 
   # Get 0-based coordinates for mutations
   # set width of deletion coordinates as 0
   res <- dplyr::mutate(
     bam_info,
-    n    = .str_extract_all(.data$cigar, "[0-9]+(?=[^0-9])"),
-    type = .str_extract_all(.data$cigar, "(?<=[0-9])[^0-9]{1}")
+    n    = .str_extract_all(cigar, "[0-9]+(?=[^0-9])"),
+    type = .str_extract_all(cigar, "(?<=[0-9])[^0-9]{1}")
   )
 
-  res <- tidyr::unnest(res, c(.data$n, .data$type))
-  res <- dplyr::group_by(res, .data$contig_id)
+  res <- tidyr::unnest(res, c(n, type))
+  res <- dplyr::group_by(res, contig_id)
 
   res <- dplyr::mutate(
     res,
-    n     = as.numeric(.data$n),
-    idx   = ifelse(.data$type != "D", .data$n, 0),
-    end   = cumsum(.data$idx),
-    start = lag(.data$end, default = 0)
+    n     = as.numeric(n),
+    idx   = ifelse(type != "D", n, 0),
+    end   = cumsum(idx),
+    start = lag(end, default = 0)
   )
 
   res <- dplyr::ungroup(res)
-  res <- dplyr::filter(res, .data$type != "=")
+  res <- dplyr::filter(res, type != "=")
 
   res <- dplyr::select(
     res,
@@ -871,7 +871,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
   res <- dplyr::select(
     airr,
-    contig_id = .data$sequence_id,
+    contig_id = sequence_id,
     dplyr::matches(coord_cols_re, perl = TRUE)
   )
 
@@ -879,15 +879,15 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
     stop("V(D)J coordinates not found, check ", airr_file)
   }
 
-  res <- tidyr::pivot_longer(res, -.data$contig_id)
-  res <- dplyr::filter(res, !is.na(.data$value))
-  res <- tidyr::extract(res, .data$name, c("seg", "pos"), coord_cols_re)
-  res <- tidyr::pivot_wider(res, names_from = .data$pos)
+  res <- tidyr::pivot_longer(res, -contig_id)
+  res <- dplyr::filter(res, !is.na(value))
+  res <- tidyr::extract(res, name, c("seg", "pos"), coord_cols_re)
+  res <- tidyr::pivot_wider(res, names_from = pos)
 
   res <- dplyr::mutate(
     res,
-    start = .data$start - 1,
-    len   = .data$end - .data$start
+    start = start - 1,
+    len   = end - start
   )
 
   res <- dplyr::select(
@@ -908,15 +908,15 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
   mut_coords <- dplyr::mutate(
     mut_coords,
-    type = dplyr::recode(.data$type, !!!mut_key)
+    type = dplyr::recode(type, !!!mut_key)
   )
 
   # If no vdj_coords, return mutation totals
   if (identical(vdj_coords, NA)) {
     res <- all_muts %>%
       tidyr::pivot_wider(
-        names_from  = .data$type,
-        values_from = .data$n,
+        names_from  = type,
+        values_from = n,
         values_fill = 0
       )
 
@@ -924,7 +924,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
       res,
       across(
         starts_with("all_"),
-        ~ .x / .data$len,
+        ~ .x / len,
         .names = "{.col}_freq"
       )
     )
@@ -944,73 +944,73 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
   vdj_muts <- dplyr::filter(
     vdj_muts,
-    .data$start < .data$end.seg &
-    .data$end   > .data$start.seg
+    start < end.seg &
+    end   > start.seg
   )
 
   vdj_muts <- dplyr::mutate(
     vdj_muts,
-    len = .data$len.seg,
+    len = len.seg,
 
     new_start = ifelse(
-      .data$start >= .data$start.seg, .data$start, .data$start.seg
+      start >= start.seg, start, start.seg
     ),
 
     new_end = ifelse(
-      .data$end <= .data$end.seg, .data$end, .data$end.seg
+      end <= end.seg, end, end.seg
     ),
 
     new_end = ifelse(
-      .data$type == mut_key[["D"]], .data$new_end + 1, .data$new_end
+      type == mut_key[["D"]], new_end + 1, new_end
     ),
 
     n = ifelse(
-      .data$type != mut_key[["D"]], .data$new_end - .data$new_start, .data$n
+      type != mut_key[["D"]], new_end - new_start, n
     )
   )
 
   # Identify junction indels
-  jxn_muts <- filter(vdj_muts, .data$type %in% unname(mut_key[c("I", "D")]))
+  jxn_muts <- filter(vdj_muts, type %in% unname(mut_key[c("I", "D")]))
 
   jxn_muts <- mutate(
     jxn_muts,
     seg = case_when(
-      .data$seg == "v" & .data$end.seg   == .data$new_end   ~ "vd",
-      .data$seg == "d" & .data$start.seg == .data$new_start ~ "vd",
-      .data$seg == "d" & .data$end.seg   == .data$new_end   ~ "dj",
-      .data$seg == "j" & .data$start.seg == .data$new_start ~ "dj",
+      seg == "v" & end.seg   == new_end   ~ "vd",
+      seg == "d" & start.seg == new_start ~ "vd",
+      seg == "d" & end.seg   == new_end   ~ "dj",
+      seg == "j" & start.seg == new_start ~ "dj",
       TRUE ~ as.character(NA)
     )
   )
 
-  jxn_muts <- dplyr::filter(jxn_muts, !is.na(.data$seg))
-  jxn_muts <- dplyr::select(jxn_muts, -.data$len)
+  jxn_muts <- dplyr::filter(jxn_muts, !is.na(seg))
+  jxn_muts <- dplyr::select(jxn_muts, -len)
 
   vdj_muts <- bind_rows(vdj_muts, jxn_muts)
 
   # Summarize mutation counts
   vdj_muts <- dplyr::group_by(
     vdj_muts,
-    .data$contig_id, .data$len, .data$type, .data$seg
+    contig_id, len, type, seg
   )
 
-  vdj_muts <- dplyr::summarize(vdj_muts, n = sum(.data$n), .groups = "drop")
+  vdj_muts <- dplyr::summarize(vdj_muts, n = sum(n), .groups = "drop")
 
   # Summarize total mutations and total length per contig
   # for each mutation type, sum total for v, d, j, and c segments, exclude jxns
-  all_muts <- dplyr::filter(vdj_muts, !.data$seg %in% c("vd", "dj"))
-  all_muts <- dplyr::group_by(all_muts, .data$contig_id, .data$type)
+  all_muts <- dplyr::filter(vdj_muts, !seg %in% c("vd", "dj"))
+  all_muts <- dplyr::group_by(all_muts, contig_id, type)
 
   all_muts <- dplyr::summarize(
     all_muts,
-    n       = sum(.data$n),
-    len     = sum(.data$len),
+    n       = sum(n),
+    len     = sum(len),
     seg     = "all",
     .groups = "drop"
   )
 
   vdj_muts <- dplyr::bind_rows(vdj_muts, all_muts)
-  res      <- tidyr::unite(vdj_muts, "type", .data$seg, .data$type, sep = "_")
+  res      <- tidyr::unite(vdj_muts, "type", seg, type, sep = "_")
 
   # Set final output columns
   freq_cols <- mut_cols <- c("v", "d", "j", "c", "all")
@@ -1026,22 +1026,22 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   freq_cols <- purrr::map_chr(freq_cols, paste0, "_", mut_key[["X"]])
 
   # Calculate mismatch frequency
-  freq <- dplyr::filter(res, .data$type %in% freq_cols)
+  freq <- dplyr::filter(res, type %in% freq_cols)
 
   freq <- dplyr::mutate(
     freq,
-    n    = round(.data$n / .data$len, 6),
-    type = paste0(.data$type, "_freq"),
+    n    = round(n / len, 6),
+    type = paste0(type, "_freq"),
     len  = NULL
   )
 
   res <- dplyr::bind_rows(res, freq)
-  res <- dplyr::select(res, -.data$len)
+  res <- dplyr::select(res, -len)
 
   res <- tidyr::pivot_wider(
     res,
-    names_from  = .data$type,
-    values_from = .data$n,
+    names_from  = type,
+    values_from = n,
     values_fill = 0
   )
 
@@ -1219,12 +1219,12 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 #' @noRd
 .identify_paired <- function(df_in) {
 
-  res <- dplyr::group_by(df_in, .data$barcode)
+  res <- dplyr::group_by(df_in, barcode)
 
   res <- dplyr::mutate(
     res,
-    paired = (all(c("TRA", "TRB") %in% .data$chains)) |
-      ("IGH" %in% .data$chains & any(c("IGL", "IGK") %in% .data$chains))
+    paired = (all(c("TRA", "TRB") %in% chains)) |
+      ("IGH" %in% chains & any(c("IGL", "IGK") %in% chains))
   )
 
   res <- dplyr::ungroup(res)
@@ -1258,7 +1258,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
   iso_df[iso_col] <- isos
 
-  iso_df <- dplyr::distinct(iso_df, .data$barcode, .data$c_gene)
+  iso_df <- dplyr::distinct(iso_df, barcode, c_gene)
   iso_df <- stats::na.omit(iso_df)
 
   dups <- iso_df$barcode
@@ -1267,7 +1267,7 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
   # Add isotypes to meta.data
   iso_df <- mutate(
     iso_df,
-    isotype = ifelse(.data$barcode %in% dups, "Multi", !!sym(iso_col))
+    isotype = ifelse(barcode %in% dups, "Multi", !!sym(iso_col))
   )
 
   isos <- purrr::set_names(
@@ -1277,8 +1277,8 @@ import_vdj <- function(input = NULL, vdj_dir = NULL, prefix = "", filter_chains
 
   res <- mutate(
     df_in,
-    isotype = unname(isos[.data$barcode]),
-    isotype = tidyr::replace_na(.data$isotype, "None")
+    isotype = unname(isos[barcode]),
+    isotype = tidyr::replace_na(isotype, "None")
   )
 
   res
@@ -1378,7 +1378,7 @@ define_clonotypes <- function(input, data_cols, clonotype_col = "clonotype_id",
 
       dplyr::across(
         dplyr::all_of(data_cols),
-        ~ paste0(.x[.data$.clone_idx], collapse = ""),
+        ~ paste0(.x[.clone_idx], collapse = ""),
         .names = ".clone_{.col}"
       ),
 
@@ -1395,12 +1395,12 @@ define_clonotypes <- function(input, data_cols, clonotype_col = "clonotype_id",
   vdj <- dplyr::mutate(
     vdj,
     .new_clone = paste(!!!syms(data_cols), sep = ""),
-    .new_id    = rank(.data$.new_clone, ties.method = "min"),
+    .new_id    = rank(.new_clone, ties.method = "min"),
 
     !!sym(clonotype_col) := ifelse(
-      .data$.new_clone == "",
+      .new_clone == "",
       "None",
-      paste0("clonotype", .data$.new_id)
+      paste0("clonotype", .new_id)
     )
   )
 
@@ -1440,17 +1440,17 @@ define_clonotypes <- function(input, data_cols, clonotype_col = "clonotype_id",
 #       contig_id = bam_lst[[1]]$qname
 #     )
 #
-#     res <- dplyr::filter(res, grepl("_contig_[0-9]+$", .data$contig_id))
+#     res <- dplyr::filter(res, grepl("_contig_[0-9]+$", contig_id))
 #
 #     # Add indel columns
 #     res <- dplyr::mutate(
 #       res,
-#       n_insertion = .extract_pat(.data$cigar, "[0-9]+(?=I)"),
-#       n_deletion  = .extract_pat(.data$cigar, "[0-9]+(?=D)"),
-#       n_mismatch  = .extract_pat(.data$cigar, "[0-9]+(?=X)"),
+#       n_insertion = .extract_pat(cigar, "[0-9]+(?=I)"),
+#       n_deletion  = .extract_pat(cigar, "[0-9]+(?=D)"),
+#       n_mismatch  = .extract_pat(cigar, "[0-9]+(?=X)"),
 #     )
 #
-#     res <- dplyr::select(res, -.data$cigar)
+#     res <- dplyr::select(res, -cigar)
 #
 #     res
 #   }
diff --git a/R/utils-plots.R b/R/utils-plots.R
index b7e44dd0..e3f5702c 100644
--- a/R/utils-plots.R
+++ b/R/utils-plots.R
@@ -632,7 +632,7 @@ trim_lab <- function(x, max_len = 25, ellipsis = "...") {
 
   # Only plot percent for histogram
   if (identical(units, "percent") && identical(method, "histogram")) {
-    plt_aes <- ggplot2::aes(y = .data$..count.. / sum(.data$..count..) * 100)
+    plt_aes <- ggplot2::aes(y = ..count.. / sum(..count..) * 100)
   }
 
   plt_aes$x <- sym(x)
@@ -728,8 +728,8 @@ trim_lab <- function(x, max_len = 25, ellipsis = "...") {
 
     res <- dplyr::mutate(
       res,
-      !!ft := ifelse(.data$pct > mn, !!ft, NA),
-      !!ft := ifelse(.data$pct <= mn, min(!!ft, na.rm = TRUE), !!ft)
+      !!ft := ifelse(pct > mn, !!ft, NA),
+      !!ft := ifelse(pct <= mn, min(!!ft, na.rm = TRUE), !!ft)
     )
   }
 
@@ -738,12 +738,12 @@ trim_lab <- function(x, max_len = 25, ellipsis = "...") {
 
     res <- dplyr::mutate(
       res,
-      !!ft := ifelse(.data$pct < mx, !!ft, NA),
-      !!ft := ifelse(.data$pct >= mx, max(!!ft, na.rm = TRUE), !!ft)
+      !!ft := ifelse(pct < mx, !!ft, NA),
+      !!ft := ifelse(pct >= mx, max(!!ft, na.rm = TRUE), !!ft)
     )
   }
 
-  res <- dplyr::select(res, -.data$pct)
+  res <- dplyr::select(res, -pct)
 
   res
 }
diff --git a/inst/bench/large-data.R b/inst/bench/large-data.R
new file mode 100644
index 00000000..c58919ea
--- /dev/null
+++ b/inst/bench/large-data.R
@@ -0,0 +1,69 @@
+library(dplyr)
+library(tidyr)
+library(purrr)
+library(tibble)
+library(stringr)
+library(SingleCellExperiment)
+library(djvdj)
+
+### TESTING LARGE DATA ###
+#
+# # generating test data
+# load("data/avid/so_avid.rda")
+#
+# test_vdj <- so_avid %>%
+#   import_vdj("data/avid/bcr/")
+#
+# test_vdj <- test_vdj@meta.data
+#
+# walk(1:5, ~ {
+#   test_vdj <<- bind_rows(test_vdj, test_vdj)
+# })
+#
+# test_vdj <- test_vdj %>%
+#   mutate(clonotype_id = str_c(clonotype_id, row_number(clonotype_id) %% 5))
+#
+# # testing functions
+# test_vdj %>%
+#   calc_abundance(cluster_col = "seurat_clusters")
+#
+# test_vdj %>%
+#   plot_abundance(
+#     cluster_col = "seurat_clusters",
+#     type = "line",
+#     n_clonotypes = 2
+#   )
+#
+# test_vdj %>%
+#   plot_similarity(
+#     cluster_col = "seurat_clusters"
+#   )
+#
+# test_vdj %>%
+#   plot_diversity(
+#     cluster_col = "seurat_clusters",
+#   )
+#
+# clmns <- c(
+#   "v_gene", "d_gene", "chains",
+#   "umis", "reads", "cdr3_length",
+#   "cdr3_nt_length", "productive",
+#   "full_length"
+# )
+#
+# tictoc::tic()
+# x <- fetch_vdj(test_vdj, clmns)
+# tictoc::toc()
+#
+# tictoc::tic()
+# y <- summarize_vdj(test_vdj, clmns)
+# tictoc::toc()
+#
+# tictoc::tic()
+# x <- fetch_vdj(test_vdj, clonotype_col = NULL)
+# tictoc::toc()
+#
+# tictoc::tic()
+# y <- fetch_vdj(test_vdj, clonotype_col = "clonotype_id")
+# tictoc::toc()
+
diff --git a/man/calc_diversity.Rd b/man/calc_diversity.Rd
index 583abcbb..d01b7b66 100644
--- a/man/calc_diversity.Rd
+++ b/man/calc_diversity.Rd
@@ -64,7 +64,7 @@ res <- calc_diversity(
   method   = abdiv::simpson
 )
 
-head(res@meta.data, 1)
+head(slot(res, 'meta.data'), 1)
 
 # Group cells based on meta.data column before calculating diversity
 res <- calc_diversity(
@@ -73,7 +73,7 @@ res <- calc_diversity(
   cluster_col = "orig.ident"
 )
 
-head(res@colData, 1)
+head(slot(res, 'colData'), 1)
 
 # Add a prefix to the new columns
 # this is useful if multiple diversity calculations are stored in the
@@ -84,7 +84,7 @@ res <- calc_diversity(
   prefix   = "bcr_"
 )
 
-head(res@meta.data, 1)
+head(slot(res, 'meta.data'), 1)
 
 # Calculate multiple metrics
 res <- calc_diversity(
@@ -96,7 +96,7 @@ res <- calc_diversity(
   )
 )
 
-head(res@colData, 1)
+head(slot(res, 'colData'), 1)
 
 # Return a data.frame instead of adding the results to the input object
 res <- calc_diversity(
diff --git a/man/djvdj-package.Rd b/man/djvdj-package.Rd
index 6e45a80b..5ad85b94 100644
--- a/man/djvdj-package.Rd
+++ b/man/djvdj-package.Rd
@@ -8,7 +8,7 @@
 \description{
 \if{html}{\figure{logo.png}{options: style='float: right' alt='logo' width='120'}}
 
-djvdj provides a range of tools to analyze single-cell V(D)J data.
+djvdj provides import, manipulation, analysis, and plotting tools for analyzing single-cell V(D)J gene expression data, integrating with both Seurat and SigleCellExperiment-based workflows.
 }
 \seealso{
 Useful links:
@@ -24,10 +24,10 @@ Useful links:
 
 Other contributors:
 \itemize{
-  \item Jay Hesselberth \email{jay.hesselberth@gmail.com} [contributor]
+  \item Jay Hesselberth \email{jay.hesselberth@cuanschutz.com} [contributor]
   \item Rui Fu \email{raysinensis@gmail.com} [contributor]
-  \item Kent Riemondy \email{kent.riemondy@ucdenver.edu} [contributor]
-  \item RNA Bioscience Initiative [funder]
+  \item Kent Riemondy \email{kent.riemondy@cuanschutz.edu} [contributor]
+  \item RNA Bioscience Initiative [funder, copyright holder]
 }
 
 }
diff --git a/man/plot_mds.Rd b/man/plot_mds.Rd
index 5cb0fc58..f5638b10 100644
--- a/man/plot_mds.Rd
+++ b/man/plot_mds.Rd
@@ -51,6 +51,9 @@ all chains.}
 
 \item{...}{Additional arguments to pass to \code{\link[ggplot2:geom_point]{ggplot2::geom_point()}}}
 }
+\value{
+ggplot object
+}
 \description{
 Perform multidimensional scaling and plot results
 }
diff --git a/man/plot_motifs.Rd b/man/plot_motifs.Rd
index bb2cbd36..3e3f1c2f 100644
--- a/man/plot_motifs.Rd
+++ b/man/plot_motifs.Rd
@@ -52,6 +52,9 @@ align sequences at the 5' or 3' end when plotting.}
 
 \item{...}{Additional parameters to pass to \code{\link[ggseqlogo:geom_logo]{ggseqlogo::geom_logo()}}}
 }
+\value{
+ggplot object
+}
 \description{
 Create sequence logos for clusters
 }
diff --git a/man/plot_similarity.Rd b/man/plot_similarity.Rd
index 01772572..c42bbd6a 100644
--- a/man/plot_similarity.Rd
+++ b/man/plot_similarity.Rd
@@ -68,6 +68,9 @@ be shown.}
 \code{\link[ComplexHeatmap:Heatmap]{ComplexHeatmap::Heatmap()}} for heatmap, \code{\link[circlize:chordDiagram]{circlize::chordDiagram()}} for
 circos plot}
 }
+\value{
+ggplot object
+}
 \description{
 Plot cluster similarity
 }
diff --git a/tests/testthat.R b/tests/testthat.R
index 39954cb3..cbdcfffc 100644
--- a/tests/testthat.R
+++ b/tests/testthat.R
@@ -1,74 +1,12 @@
+# This file is part of the standard setup for testthat.
+# It is recommended that you do not modify it.
+#
+# Where should you do additional test configuration?
+# Learn more about the roles of various files in:
+# * https://r-pkgs.org/tests.html
+# * https://testthat.r-lib.org/reference/test_package.html#special-files
+
 library(testthat)
-library(dplyr)
-library(tidyr)
-library(purrr)
-library(tibble)
-library(stringr)
-library(SingleCellExperiment)
 library(djvdj)
 
 test_check("djvdj")
-
-
-
-### TESTING LARGE DATA ###
-#
-# # generating test data
-# load("data/avid/so_avid.rda")
-#
-# test_vdj <- so_avid %>%
-#   import_vdj("data/avid/bcr/")
-#
-# test_vdj <- test_vdj@meta.data
-#
-# walk(1:5, ~ {
-#   test_vdj <<- bind_rows(test_vdj, test_vdj)
-# })
-#
-# test_vdj <- test_vdj %>%
-#   mutate(clonotype_id = str_c(clonotype_id, row_number(clonotype_id) %% 5))
-#
-# # testing functions
-# test_vdj %>%
-#   calc_abundance(cluster_col = "seurat_clusters")
-#
-# test_vdj %>%
-#   plot_abundance(
-#     cluster_col = "seurat_clusters",
-#     type = "line",
-#     n_clonotypes = 2
-#   )
-#
-# test_vdj %>%
-#   plot_similarity(
-#     cluster_col = "seurat_clusters"
-#   )
-#
-# test_vdj %>%
-#   plot_diversity(
-#     cluster_col = "seurat_clusters",
-#   )
-#
-# clmns <- c(
-#   "v_gene", "d_gene", "chains",
-#   "umis", "reads", "cdr3_length",
-#   "cdr3_nt_length", "productive",
-#   "full_length"
-# )
-#
-# tictoc::tic()
-# x <- fetch_vdj(test_vdj, clmns)
-# tictoc::toc()
-#
-# tictoc::tic()
-# y <- summarize_vdj(test_vdj, clmns)
-# tictoc::toc()
-#
-# tictoc::tic()
-# x <- fetch_vdj(test_vdj, clonotype_col = NULL)
-# tictoc::toc()
-#
-# tictoc::tic()
-# y <- fetch_vdj(test_vdj, clonotype_col = "clonotype_id")
-# tictoc::toc()
-
diff --git a/tests/testthat/test-calc-diversity.R b/tests/testthat/test-calc-diversity.R
index 33b84717..b2a83ccf 100644
--- a/tests/testthat/test-calc-diversity.R
+++ b/tests/testthat/test-calc-diversity.R
@@ -1,3 +1,6 @@
+data(vdj_so)
+data(vdj_sce)
+
 # Test data
 df_1 <- vdj_so@meta.data
 
diff --git a/tests/testthat/test-calc-frequency.R b/tests/testthat/test-calc-frequency.R
index 15c760b4..48d718aa 100644
--- a/tests/testthat/test-calc-frequency.R
+++ b/tests/testthat/test-calc-frequency.R
@@ -1,3 +1,6 @@
+data(vdj_so)
+data(vdj_sce)
+
 # Test data
 df_1 <- vdj_so@meta.data
 
diff --git a/tests/testthat/test-calc-gene-usage.R b/tests/testthat/test-calc-gene-usage.R
index 9dcc125d..566df5bf 100644
--- a/tests/testthat/test-calc-gene-usage.R
+++ b/tests/testthat/test-calc-gene-usage.R
@@ -1,3 +1,6 @@
+data(vdj_so)
+data(vdj_sce)
+
 # Test data
 df_1 <- vdj_so@meta.data
 
diff --git a/tests/testthat/test-calc-similarity.R b/tests/testthat/test-calc-similarity.R
index fccbb5e0..ecbff94e 100644
--- a/tests/testthat/test-calc-similarity.R
+++ b/tests/testthat/test-calc-similarity.R
@@ -1,3 +1,6 @@
+data(vdj_so)
+data(vdj_sce)
+
 # Test data
 df_1 <- vdj_so@meta.data
 
diff --git a/tests/testthat/test-cluster-seqs.R b/tests/testthat/test-cluster-seqs.R
index 2e4ea976..bc872a56 100644
--- a/tests/testthat/test-cluster-seqs.R
+++ b/tests/testthat/test-cluster-seqs.R
@@ -1,3 +1,5 @@
+data(vdj_so)
+data(vdj_sce)
 
 test_cols <- c(
   "#E69F00", "#56B4E9", "#009E73",
diff --git a/tests/testthat/test-filter-vdj.R b/tests/testthat/test-filter-vdj.R
index 3036e3af..3e476070 100644
--- a/tests/testthat/test-filter-vdj.R
+++ b/tests/testthat/test-filter-vdj.R
@@ -1,3 +1,6 @@
+data(vdj_so)
+data(vdj_sce)
+
 # Check NAs in result
 check_nas <- function(df_in) {
 
diff --git a/tests/testthat/test-import-vdj.R b/tests/testthat/test-import-vdj.R
index 9d97bb12..99e52e9d 100644
--- a/tests/testthat/test-import-vdj.R
+++ b/tests/testthat/test-import-vdj.R
@@ -1,3 +1,8 @@
+data(vdj_so)
+data(vdj_sce)
+data(tiny_so)
+data(tiny_sce)
+
 # Test data
 ctigs <- c(
   system.file("extdata/bcr_1/outs", package = "djvdj"),
diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R
index 951985e7..c8b48f89 100644
--- a/tests/testthat/test-utils.R
+++ b/tests/testthat/test-utils.R
@@ -1,3 +1,6 @@
+data(vdj_so)
+data(vdj_sce)
+
 # Test data
 df_1 <- vdj_so@meta.data
 
diff --git a/vignettes/abundance.Rmd b/vignettes/abundance.Rmd
index 7d19e201..38ef3b3e 100644
--- a/vignettes/abundance.Rmd
+++ b/vignettes/abundance.Rmd
@@ -81,7 +81,8 @@ so_vdj <- so %>%
 When `cluster_col` is specified, an additional meta.data column ('shared') will be added indicating whether the clonotype is shared between multiple clusters.
 
 ```{r}
-so_vdj@meta.data %>%
+# or use `so_vdj@meta.data`
+slot(so_vdj, 'meta.data') %>%
   head(2)
 ```
 
@@ -217,3 +218,12 @@ so %>%
     plot_colors = c(MD4 = "#fec44f", BL6 = "#3182bd")
   )
 ```
+
+<details style="margin-bottom:10px;">
+<summary>
+    Session info
+</summary>
+```{r}
+sessionInfo()
+```
+</details>
diff --git a/vignettes/clustering.Rmd b/vignettes/clustering.Rmd
index c03482f3..1c3bc66b 100644
--- a/vignettes/clustering.Rmd
+++ b/vignettes/clustering.Rmd
@@ -213,3 +213,12 @@ so_vdj %>%
     axis.ticks.x = element_blank()
   )
 ```
+
+<details style="margin-bottom:10px;">
+<summary>
+    Session info
+</summary>
+```{r}
+sessionInfo()
+```
+</details>
diff --git a/vignettes/diversity.Rmd b/vignettes/diversity.Rmd
index f4d2bcca..4ca3112a 100644
--- a/vignettes/diversity.Rmd
+++ b/vignettes/diversity.Rmd
@@ -194,3 +194,12 @@ so %>%
     n_boots     = 0
   )
 ```
+
+<details style="margin-bottom:10px;">
+<summary>
+    Session info
+</summary>
+```{r}
+sessionInfo()
+```
+</details>
diff --git a/vignettes/gene-usage.Rmd b/vignettes/gene-usage.Rmd
index 29d8cee9..895caf07 100644
--- a/vignettes/gene-usage.Rmd
+++ b/vignettes/gene-usage.Rmd
@@ -183,3 +183,12 @@ so %>%
     scale       = TRUE
   )
 ```
+
+<details style="margin-bottom:10px;">
+<summary>
+    Session info
+</summary>
+```{r}
+sessionInfo()
+```
+</details>
diff --git a/vignettes/import_vdj.Rmd b/vignettes/import_vdj.Rmd
index 9cb3a7fe..6b05e26c 100644
--- a/vignettes/import_vdj.Rmd
+++ b/vignettes/import_vdj.Rmd
@@ -60,7 +60,8 @@ so_vdj <- so %>%
 `import_vdj()` adds a variety of per-chain metrics to the object meta.data. Information for each chain identified for the cell is separated by a semicolon. The separator used for storing and parsing per-chain V(D)J data can be specified using the `sep` argument included for most djvdj functions. `NA`s will be included for cells that lack V(D)J data. 
 
 ```{r}
-so_vdj@meta.data %>%
+# or use `so_vdj@meta.data`
+slot(so_vdj, 'meta.data') %>%
   head(2)
 ```
 
@@ -206,7 +207,8 @@ so_vdj <- so %>%
 The additional columns added to the meta.data will include the number of insertions, deletions, and mismatches (ending in 'ins', 'del', or 'mis') for each V(D)J segment (prefixed with 'v', 'd', 'j', or 'c'). Columns containing junction information will be prefixed with either 'vd' or 'dj'. Columns ending in 'freq' show the event frequency which is calculated as the number of events divided by the length of the region.
 
 ```{r}
-so_vdj@meta.data %>%
+# or use `so_vdj@meta.data`
+slot(so_vdj, 'meta.data') %>%
   head(2)
 ```
 
@@ -235,7 +237,8 @@ so_vdj <- so %>%
 This results in two sets of new columns being added to the meta.data. When performing downstream analysis using other djvdj functions, be sure to specify the correct columns, i.e. 'bcr_clonotype_id' or 'tcr_clonotype_id'.
 
 ```{r}
-so_vdj@meta.data %>%
+# or use `so_vdj@meta.data`
+slot(so_vdj, 'meta.data') %>%
   head(3)
 ```
 
@@ -254,3 +257,12 @@ vdj_dirs <- c(
 # This will load V(D)J data and return a data.frame
 df_vdj <- import_vdj(vdj_dir = vdj_dirs)
 ```
+
+<details style="margin-bottom:10px;">
+<summary>
+    Session info
+</summary>
+```{r}
+sessionInfo()
+```
+</details>
diff --git a/vignettes/mutate.Rmd b/vignettes/mutate.Rmd
index a703dece..b16a68c7 100644
--- a/vignettes/mutate.Rmd
+++ b/vignettes/mutate.Rmd
@@ -73,7 +73,8 @@ res <- so %>%
     all(c("IGH", "IGK", "IGL") %in% chains)
   )
 
-res@meta.data %>%
+# or use `res@meta.data`
+slot(res, 'meta.data') %>%
   filter(!is.na(clonotype_id)) %>%
   select(chains, cdr3) %>%
   head(3)
@@ -85,7 +86,8 @@ In this example we are removing V(D)J data for all chains except IGH.
 res <- so %>%
   filter_vdj(chains == "IGH")
 
-res@meta.data %>%
+# or use `res@meta.data`
+slot(res, 'meta.data') %>%
   filter(!is.na(clonotype_id)) %>%
   select(chains, cdr3) %>%
   head(3)
@@ -105,7 +107,8 @@ res <- so %>%
     col_names = "median_{.col}"
   )
 
-res@meta.data %>%
+# or use `res@meta.data`
+slot(res, 'meta.data') %>%
   select(all_del, all_ins, median_all_del, median_all_ins) %>%
   head(2)
 ```
@@ -120,7 +123,8 @@ res <- so %>%
     col_names = "unique_chains"
   )
 
-res@meta.data %>%
+# or use `res@meta.data`
+slot(res, 'meta.data') %>%
   filter(n_chains > 2) %>%
   select(chains, unique_chains) %>%
   head(2)
@@ -140,8 +144,17 @@ res <- so %>%
     total_indels = sum(all_ins, all_del)
   )
 
-res@meta.data %>%
+# or use `res@meta.data`
+slot(res, 'meta.data') %>%
   select(all_ins, all_del, total_indels) %>%
   head()
 ```
 
+<details style="margin-bottom:10px;">
+<summary>
+    Session info
+</summary>
+```{r}
+sessionInfo()
+```
+</details>
diff --git a/vignettes/plotting.Rmd b/vignettes/plotting.Rmd
index 94f37446..f21fcaf9 100644
--- a/vignettes/plotting.Rmd
+++ b/vignettes/plotting.Rmd
@@ -148,10 +148,11 @@ so %>%
   )
 ```
 
-
-
-
-
-
-
-
+<details style="margin-bottom:10px;">
+<summary>
+    Session info
+</summary>
+```{r}
+sessionInfo()
+```
+</details>