diff --git a/DESCRIPTION b/DESCRIPTION
index 2568a14c7..58d304e4f 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -114,12 +114,14 @@ Suggests:
     SummarizedExperiment,
     tidygraph,
     trendsceek,
-    testthat (>= 3.0.0)
+    testthat (>= 3.0.0),
+    rmarkdown
 Remotes:
     drieslab/GiottoUtils,
     drieslab/GiottoClass,
     drieslab/GiottoVisuals
 Collate:
+    'ONTraC_wrapper.R'
     'auxiliary_giotto.R'
     'cell_segmentation.R'
     'clustering.R'
diff --git a/NAMESPACE b/NAMESPACE
index b9e278ca0..55de5eabd 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -238,6 +238,7 @@ export(getGEFtxCoords)
 export(getGiottoImage)
 export(getMultiomics)
 export(getNearestNetwork)
+export(getONTraCv1Input)
 export(getPolygonInfo)
 export(getRainbowColors)
 export(getSpatialEnrichment)
@@ -274,6 +275,7 @@ export(jackstrawPlot)
 export(joinGiottoObjects)
 export(loadGiotto)
 export(loadHMRF)
+export(loadOntraCResults)
 export(makePseudoVisium)
 export(makeSignMatrixDWLS)
 export(makeSignMatrixDWLSfromMatrix)
@@ -293,8 +295,10 @@ export(pieCellTypesFromEnrichment)
 export(plotCCcomDotplot)
 export(plotCCcomHeatmap)
 export(plotCPF)
+export(plotCTCompositionInNicheCluster)
 export(plotCellProximityFeatSpot)
 export(plotCellProximityFeats)
+export(plotCellTypeNTScore)
 export(plotCellTypesFromEnrichment)
 export(plotCombineCCcom)
 export(plotCombineCellCellCommunication)
@@ -309,6 +313,7 @@ export(plotInteractive3D)
 export(plotInteractivePolygons)
 export(plotMetaDataCellsHeatmap)
 export(plotMetaDataHeatmap)
+export(plotNicheClusterConnectivity)
 export(plotPCA)
 export(plotPCA_2D)
 export(plotPCA_3D)
diff --git a/R/ONTraC_wrapper.R b/R/ONTraC_wrapper.R
new file mode 100644
index 000000000..0cdc02ff4
--- /dev/null
+++ b/R/ONTraC_wrapper.R
@@ -0,0 +1,524 @@
+#' @title getONTraCv1Input
+#' @name getONTraCv1Input
+#' @description generate the input data for ONTraC v1
+#' @inheritParams data_access_params
+#' @inheritParams read_data_params
+#' @param output_path the path to save the output file
+#' @param cell_type the cell type column name in the metadata
+#' @returns data.table with columns: Cell_ID, Sample, x, y, Cell_Type
+#' @details This function generate the input data for ONTraC v1
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#'
+#' getONTraCv1Input(
+#'   gobject = g,
+#'   cell_type = "custom_leiden"
+#' )
+#' @export
+getONTraCv1Input <- function(gobject, # nolint: object_name_linter.
+                             cell_type,
+                             output_path = getwd(),
+                             spat_unit = NULL,
+                             feat_type = NULL,
+                             verbose = TRUE) {
+  # Set feat_type and spat_unit
+  spat_unit <- set_default_spat_unit(
+    gobject = gobject,
+    spat_unit = spat_unit
+  )
+  feat_type <- set_default_feat_type(
+    gobject = gobject,
+    spat_unit = spat_unit,
+    feat_type = feat_type
+  )
+
+  pos_df <- getSpatialLocations(
+    gobject = gobject,
+    spat_unit = spat_unit,
+    output = "data.table"
+  )
+  meta_df <- pDataDT(
+    gobject = gobject,
+    spat_unit = spat_unit,
+    feat_type = feat_type
+  )
+  output_df <- merge(x = pos_df, y = meta_df, by = "cell_ID")
+
+  # check if the cell_type column exits
+  if (!cell_type %in% colnames(output_df)) {
+    vmsg(.v = verbose, paste(
+      "Given",
+      cell_type,
+      "do not exist in giotto object's metadata!"
+    ))
+    return(NULL)
+  }
+
+  # add default sample name for one sample obj
+  if (!"list_ID" %in% colnames(output_df)) {
+    output_df$list_ID <- "ONTraC"
+  }
+
+  output_df <- output_df[, .SD, .SDcols = c(
+    "cell_ID",
+    "list_ID",
+    "sdimx",
+    "sdimy",
+    cell_type
+  )]
+  colnames(output_df) <- c("Cell_ID", "Sample", "x", "y", "Cell_Type")
+  file_path <- file.path(output_path, "ONTraC_dataset_input.csv")
+  write.csv(output_df, file = file_path, quote = FALSE, row.names = FALSE)
+  vmsg(.v = verbose, paste("ONTraC input file was saved as", file_path))
+
+  return(output_df)
+}
+
+
+#' @title load_cell_bin_niche_cluster
+#' @name load_cell_bin_niche_cluster
+#' @description load cell-level binarized niche cluster
+#' @inheritParams data_access_params
+#' @inheritParams read_data_params
+#' @param ontrac_results_dir the directory where the ONTraC results are saved
+#' @returns gobject with cell-level binarized niche cluster
+#' @details This function loads the ONTraC outputed cell-level binarized niche
+#' cluster into the giotto object.
+load_cell_bin_niche_cluster <- function(gobject,
+                                        ontrac_results_dir = getwd()) {
+  bin_niche_cluster_df <- read.csv(file = file.path(
+    ontrac_results_dir,
+    "GNN_dir", "cell_level_max_niche_cluster.csv.gz"
+  ))
+  colnames(bin_niche_cluster_df) <- c("cell_ID", "NicheCluster")
+  gobject <- GiottoClass::addCellMetadata(gobject,
+    new_metadata = bin_niche_cluster_df,
+    by_column = TRUE,
+    column_cell_ID = "cell_ID"
+  )
+  return(gobject)
+}
+
+
+#' @title load_cell_NT_score
+#' @name load_cell_NT_score
+#' @description load cell-level NT score
+#' @inheritParams data_access_params
+#' @inheritParams read_data_params
+#' @param ontrac_results_dir the directory where the ONTraC results are saved
+#' @returns gobject with cell-level NT score
+#' @details This function loads the ONTraC outputed cell-level NT score
+load_cell_NT_score <- function(gobject, # nolint: object_name_linter.
+                               ontrac_results_dir = getwd()) {
+  NT_score_df <- read.csv(file = file.path( # nolint: object_name_linter.
+    ontrac_results_dir,
+    "NTScore_dir", "NTScore.csv.gz"
+  ))[c("Cell_ID", "Cell_NTScore")]
+  colnames(NT_score_df) <- c("cell_ID", "NTScore") # nolint: object_name_linter.
+  gobject <- addCellMetadata(gobject,
+    new_metadata = NT_score_df, # nolint: object_name_linter.
+    by_column = TRUE,
+    column_cell_ID = "cell_ID"
+  )
+
+  return(gobject)
+}
+
+
+#' @title load_cell_niche_cluster_prob
+#' @name load_cell_niche_cluster_prob
+#' @description load cell-niche cluster probability
+#' @inheritParams data_access_params
+#' @inheritParams read_data_params
+#' @param ontrac_results_dir the directory where the ONTraC results are saved
+#' @param name name for the probability matrix
+#' @returns gobject with cell-niche cluster probability matrix
+#' @details This function loads the ONTraC outputed cell-niche cluster
+#' probability as an exprObj into the giotto object.
+load_cell_niche_cluster_prob <- function(gobject,
+                                         ontrac_results_dir = getwd(),
+                                         spat_unit = "cell",
+                                         feat_type = "niche cluster",
+                                         name = "prob") {
+  niche_cluster_prob_df <- read.csv(file = file.path(
+    ontrac_results_dir,
+    "GNN_dir", "cell_level_niche_cluster.csv.gz"
+  ))
+  rownames(niche_cluster_prob_df) <- niche_cluster_prob_df$Cell_ID
+  niche_cluster_prob_df$Cell_ID <- NULL
+  expobj <- createExprObj(t(niche_cluster_prob_df),
+    spat_unit = spat_unit,
+    feat_type = feat_type
+  )
+  gobject <- GiottoClass::setExpression(
+    gobject = gobject,
+    x = expobj,
+    spat_unit = spat_unit,
+    feat_type = feat_type,
+    name = name
+  )
+
+  return(gobject)
+}
+
+
+#' @title load_nc_connectivity
+#' @name load_nc_connectivity
+#' @description load niche cluster connectivity
+#' @inheritParams data_access_params
+#' @inheritParams read_data_params
+#' @param ontrac_results_dir the directory where the ONTraC results are saved
+#' @param name name for the connectivity matrix
+#' @returns gobject with niche cluster connectivity matrix
+#' @details This function loads the ONTraC outputed niche cluster connectivity
+#' matrix as an exprObj into the giotto object.
+load_nc_connectivity <- function(gobject,
+                                 ontrac_results_dir = getwd(),
+                                 spat_unit = "niche cluster",
+                                 feat_type = "connectivity",
+                                 name = "normalized") {
+  connectivity_df <- read.csv(file = file.path(
+    ontrac_results_dir,
+    "GNN_dir", "consolidate_out_adj.csv.gz"
+  ), header = FALSE)
+  rownames(connectivity_df) <- paste0(
+    "NicheCluster_",
+    seq_len(dim(connectivity_df)[1]) - 1
+  )
+  colnames(connectivity_df) <- paste0(
+    "NicheCluster_",
+    seq_len(dim(connectivity_df)[2]) - 1
+  )
+  expobj <- createExprObj(t(connectivity_df),
+    spat_unit = spat_unit,
+    feat_type = feat_type
+  )
+  gobject <- GiottoClass::setExpression(
+    gobject = gobject,
+    x = expobj,
+    spat_unit = spat_unit,
+    feat_type = feat_type,
+    name = name
+  )
+
+  return(gobject)
+}
+
+
+#' @title loadOntraCResults
+#' @name loadOntraCResults
+#' @description load ONTraC results
+#' @inheritParams data_access_params
+#' @inheritParams read_data_params
+#' @param ontrac_results_dir the directory where the ONTraC results are saved
+#' @returns gobject with ONTraC results
+#' @details This function loads the ONTraC results into the giotto object.
+#' @export
+loadOntraCResults <- function(gobject, # nolint: object_name_linter.
+                              ontrac_results_dir = getwd()) {
+  gobject <- load_cell_bin_niche_cluster(gobject, ontrac_results_dir)
+  gobject <- load_cell_NT_score(gobject, ontrac_results_dir)
+  gobject <- load_cell_niche_cluster_prob(gobject, ontrac_results_dir)
+  gobject <- GiottoClass::addCellMetadata(
+    gobject = gobject,
+    spat_unit = "cell",
+    feat_type = "niche cluster",
+    new_metadata = pDataDT(gobject),
+    by_column = TRUE,
+    column_cell_ID = "cell_ID"
+  )
+
+  gobject <- load_nc_connectivity(gobject, ontrac_results_dir)
+
+  return(gobject)
+}
+
+#' @title plotNicheClusterConnectivity
+#' @name plotNicheClusterConnectivity
+#' @description plot niche cluster connectivity
+#' @inheritParams data_access_params
+#' @inheritParams plot_output_params
+#' @param spat_unit name of spatial unit niche stored cluster features
+#' @param feat_type name of the feature type stored niche cluster connectivities
+#' @param values name of the expression matrix stored connectivity values
+#' @details This function plots the niche cluster connectivity matrix
+#' @export
+plotNicheClusterConnectivity <- function( # nolint: object_name_linter.
+    gobject,
+    spat_unit = "niche cluster",
+    feat_type = "connectivity",
+    values = "normalized",
+    show_plot = NULL,
+    return_plot = NULL,
+    save_plot = NULL,
+    save_param = list(),
+    theme_param = list(),
+    default_save_name = "NicheClusterConnectivity") {
+  # load `guide_edge_colourbar` function in ggraph,
+  # otherwise it will raise an error when using `scale_edge_colour_gradientn`
+  library(ggraph)
+
+  # get the niche cluster connectivity matrix
+  niche_cluster_connectivites <- getExpression(
+    gobject = gobject,
+    values = "normalized",
+    spat_unit = "niche cluster",
+    feat_type = "connectivity",
+    output = "matrix"
+  )
+
+  # transform the matrix to data.frame for constructing igraph object
+  niche_cluster_connectivites <- cbind(
+    expand.grid(dimnames(niche_cluster_connectivites)),
+    value = as.vector(as.matrix(
+      niche_cluster_connectivites
+    ))
+  )
+  colnames(niche_cluster_connectivites) <- c("from", "to", "connectivites")
+
+  # construct igraph object
+  igd <- igraph::graph_from_data_frame(
+    d = niche_cluster_connectivites[, c("from", "to", "connectivites")],
+    directed = FALSE
+  )
+  igd <- igraph::simplify(
+    graph = igd,
+    remove.loops = TRUE,
+    remove.multiple = FALSE
+  )
+  edges_sizes <- igraph::edge_attr(igd, "connectivites")
+  edges_colors <- edges_sizes
+  igd <- igraph::set.edge.attribute(
+    graph = igd,
+    index = igraph::E(igd),
+    name = "color",
+    value = edges_colors
+  )
+  igd <- igraph::set.edge.attribute(
+    graph = igd,
+    index = igraph::E(igd),
+    name = "size",
+    value = edges_sizes
+  )
+
+  # plot
+  ## layout
+  coords <- igraph::layout_with_drl(
+    graph = igd,
+    weights = edges_sizes,
+    use.seed = TRUE
+  )
+  gpl <- ggraph::ggraph(graph = igd, layout = coords)
+
+  ## edges
+  gpl <- gpl + ggraph::geom_edge_link(
+    ggplot2::aes(
+      colour = edges_sizes,
+      edge_width = 5,
+      edge_alpha = size # nolint: object_usage_linter.
+    ),
+    show.legend = FALSE
+  )
+  gpl <- gpl + ggraph::scale_edge_alpha(range = c(0.1, 1))
+  gpl <- gpl + ggraph::scale_edge_colour_gradientn(
+    colours = RColorBrewer::brewer.pal(9, "Reds"),
+    name = "Value"
+  )
+
+  ## node
+  gpl <- gpl + ggraph::geom_node_point(
+    ggplot2::aes(colour = name), # nolint: object_usage_linter.
+    size = 10
+  )
+  gpl <- gpl + ggplot2::scale_fill_gradientn(colours = viridis::turbo(100))
+  gpl <- gpl + ggraph::geom_node_text(
+    ggplot2::aes(label = name), # nolint: object_usage_linter.
+    repel = TRUE
+  )
+
+  ## theme
+  gpl <- gpl + ggplot2::theme_bw() + ggplot2::theme(
+    panel.grid = ggplot2::element_blank(),
+    panel.border = ggplot2::element_blank(),
+    axis.title = ggplot2::element_blank(),
+    axis.text = ggplot2::element_blank(),
+    axis.ticks = ggplot2::element_blank()
+  )
+  gpl
+
+  # return or save
+  return(GiottoVisuals::plot_output_handler(
+    gobject = gobject,
+    plot_object = gpl,
+    save_plot = save_plot,
+    return_plot = return_plot,
+    show_plot = show_plot,
+    default_save_name = default_save_name,
+    save_param = save_param,
+    else_return = NULL
+  ))
+}
+
+#' @title plotCTCompositionInNicheCluster
+#' @name plotCTCompositionInNicheCluster
+#' @description plot cell type composition within each niche cluster
+#' @param cell_type the cell type column name in the metadata
+#' @inheritParams data_access_params
+#' @inheritParams plot_output_params
+#' @param spat_unit name of spatial unit niche stored cluster features
+#' @param feat_type name of the feature type stored niche cluster connectivities
+#' @param values name of the expression matrix stored connectivity values
+#' @details This function plots the niche cluster connectivity matrix
+#' @export
+plotCTCompositionInNicheCluster <- function( # nolint: object_name_linter.
+    gobject,
+    cell_type,
+    values = "prob",
+    spat_unit = "cell",
+    feat_type = "niche cluster",
+    show_plot = NULL,
+    return_plot = NULL,
+    save_plot = NULL,
+    save_param = list(),
+    theme_param = list(),
+    default_save_name = "CellTypeCompositionInNicheCluster") {
+  # Get the cell type composition within each niche cluster
+  ## extract the cell-level niche cluster probability matrix
+  exp <- getExpression(
+    gobject = gobject,
+    values = values,
+    spat_unit = spat_unit,
+    feat_type = feat_type,
+    output = "exprObj"
+  )
+  prob_df <- as.data.frame(t(as.matrix(exp@exprMat)))
+  prob_df$cell_ID <- rownames(prob_df)
+  ## combine the cell type and niche cluster probability matrix
+  combined_df <- merge(
+    as.data.frame(pDataDT(gobject, feat_type = feat_type))[, c(
+      "cell_ID",
+      cell_type
+    )],
+    prob_df,
+    by = "cell_ID"
+  )
+
+  # Calculate the normalized cell type composition within each niche cluster
+  cell_type_counts_df <- combined_df %>%
+    tidyr::pivot_longer(
+      cols = dplyr::starts_with("NicheCluster_"),
+      names_to = "Cluster",
+      values_to = "Probability"
+    ) %>%
+    dplyr::group_by(
+      !!rlang::sym(cell_type),
+      Cluster # nolint: object_usage_linter.
+    ) %>%
+    dplyr::summarise(Sum = sum(Probability, # nolint: object_usage_linter.
+      na.rm = TRUE
+    )) %>%
+    tidyr::spread(key = "Cluster", value = "Sum", fill = 0)
+  cell_type_counts_df <- as.data.frame(cell_type_counts_df)
+  rownames(cell_type_counts_df) <- cell_type_counts_df[[cell_type]]
+  cell_type_counts_df[[cell_type]] <- NULL
+  normalized_df <- as.data.frame(t(
+    t(cell_type_counts_df) / colSums(cell_type_counts_df)
+  ))
+
+
+  # Reshape the data frame into long format
+  normalized_df[[cell_type]] <- rownames(normalized_df)
+  df_long <- normalized_df %>%
+    tidyr::pivot_longer(
+      cols = -!!rlang::sym(cell_type), # nolint: object_usage_linter.
+      names_to = "Cluster",
+      values_to = "Composition"
+    )
+
+  # Create the heatmap using ggplot2
+  pl <- ggplot(df_long, aes(
+    x = !!rlang::sym(cell_type), # nolint: object_usage_linter.
+    y = Cluster, # nolint: object_usage_linter.
+    fill = Composition # nolint: object_usage_linter.
+  )) +
+    geom_tile() +
+    viridis::scale_fill_viridis(option = "inferno", limits = c(0, 1)) +
+    theme_minimal() +
+    labs(
+      title = "Normalized cell type compositions within each niche cluster",
+      x = "Cell_Type",
+      y = "Cluster"
+    ) +
+    theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+  # return or save
+  return(GiottoVisuals::plot_output_handler(
+    gobject = gobject,
+    plot_object = pl,
+    save_plot = save_plot,
+    return_plot = return_plot,
+    show_plot = show_plot,
+    default_save_name = default_save_name,
+    save_param = save_param,
+    else_return = NULL
+  ))
+}
+
+
+#' @title plotCellTypeNTScore
+#' @name plotCellTypeNTScore
+#' @description plot NTScore by cell type
+#' @param cell_type the cell type column name in the metadata
+#' @inheritParams data_access_params
+#' @inheritParams plot_output_params
+#' @export
+plotCellTypeNTScore <- function(gobject, # nolint: object_name_linter.
+                                cell_type,
+                                values = "NTScore",
+                                spat_unit = "cell",
+                                feat_type = "niche cluster",
+                                show_plot = NULL,
+                                return_plot = NULL,
+                                save_plot = NULL,
+                                save_param = list(),
+                                theme_param = list(),
+                                default_save_name = "CellTypeNTScore") {
+  # Get the cell type composition within each niche cluster
+  data_df <- pDataDT(
+    gobject = gobject,
+    spat_unit = spat_unit,
+    feat_type = feat_type
+  )
+  avg_scores <- data_df %>%
+    dplyr::group_by(!!rlang::sym(cell_type)) %>% # nolint: object_usage_linter.
+    dplyr::summarise(Avg_NTScore = mean(NTScore)) # nolint: object_usage_linter.
+  data_df[[cell_type]] <- factor(data_df[[cell_type]],
+    levels = avg_scores[[cell_type]][order(avg_scores$Avg_NTScore)]
+  )
+
+  pl <- ggplot(data_df, aes(
+    x = NTScore, # nolint: object_usage_linter.
+    y = !!rlang::sym(cell_type),
+    fill = !!rlang::sym(cell_type)
+  )) +
+    geom_violin() +
+    theme_minimal() +
+    labs(
+      title = "Violin Plot of NTScore by Cell Type",
+      x = "NTScore",
+      y = "Cell Type"
+    ) +
+    ggplot2::theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+  # return or save
+  return(GiottoVisuals::plot_output_handler(
+    gobject = gobject,
+    plot_object = pl,
+    save_plot = save_plot,
+    return_plot = return_plot,
+    show_plot = show_plot,
+    default_save_name = default_save_name,
+    save_param = save_param,
+    else_return = NULL
+  ))
+}
diff --git a/R/auxiliary_giotto.R b/R/auxiliary_giotto.R
index 82a65fd15..de16b2c08 100644
--- a/R/auxiliary_giotto.R
+++ b/R/auxiliary_giotto.R
@@ -47,10 +47,13 @@
         #   mymatrix = log(mymatrix + offset)/log(base)
     } else if (methods::is(mymatrix, "dgCMatrix")) {
         mymatrix@x <- log(mymatrix@x + offset) / log(base)
-        # replace with sparseMatrixStats
+       # replace with sparseMatrixStats
     } else if (methods::is(mymatrix, "Matrix")) {
         mymatrix@x <- log(mymatrix@x + offset) / log(base)
-    } else {
+    } else if(methods::is(mymatrix, 'dbMatrix')) {
+        mymatrix[] <- dplyr::mutate(mymatrix[], x = x + offset) # workaround for lack of @x slot
+        mymatrix <- log(mymatrix)/log(base)
+  } else {
         mymatrix <- log(as.matrix(mymatrix) + offset) / log(base)
     }
 
@@ -732,7 +735,9 @@ filterGiotto <- function(
         description = "_filter"
     )
 
-    return(newGiottoObject)
+  return(newGiottoObject)
+
+
 }
 
 
@@ -740,6 +745,46 @@ filterGiotto <- function(
 
 ### normalization ####
 
+#' @title compute_dbMatrix
+#' @description saves dbMatrix to db if global option is set
+#' @details
+#' Set \code{options(giotto.dbmatrix_compute = FALSE)} if saving dbMatrix
+#' after each step of normalization workflow is not desired.
+#' @keywords internal
+.compute_dbMatrix <- function(dbMatrix, name, verbose = TRUE) {
+  # input validation
+  if(!inherits(dbMatrix, 'dbMatrix')) {
+    stop('dbMatrix must be of class dbMatrix')
+  }
+  
+  if(!is.character(name)) {
+    stop('name must be a character')
+  }
+  
+  # TODO: update with dbData generic
+  con = dbMatrix:::get_con(dbMatrix)
+  
+  # overwrite table by default
+  if(name %in% DBI::dbListTables(con)) {
+    DBI::dbRemoveTable(con, name)
+  }
+  
+  if(verbose){
+    msg <- glue::glue('Computing {name} expression matrix on disk...')
+    cat(msg)   
+  }
+  
+  dbMatrix[] |>
+    dplyr::compute(temporary=F, name = name)
+    
+  # TODO: update below with proper setters from dbMatrix
+  dbMatrix[] <- dplyr::tbl(con, name) # reassign to computed mat
+  dbMatrix@name <- name
+  
+  if(verbose) cat('done \n')
+  
+  return(dbMatrix)
+}
 
 #' @title RNA standard normalization
 #' @name .rna_standard_normalization
@@ -776,9 +821,6 @@ filterGiotto <- function(
     feat_names <- rownames(raw_expr[])
     col_names <- colnames(raw_expr[])
 
-
-
-
     ## 1. library size normalize
     if (library_size_norm == TRUE) {
         norm_expr <- .lib_norm_giotto(
@@ -853,6 +895,16 @@ filterGiotto <- function(
     }
 
     ## 5. create and set exprObj
+    # Save dbMatrix to db
+    compute_mat <- getOption("giotto.dbmatrix_compute", default = FALSE)
+    if(compute_mat && !is.null(norm_expr)){
+        norm_expr <- .compute_dbMatrix(
+            dbMatrix = norm_expr,
+            name = 'normalized',
+            verbose = verbose
+        ) 
+    }
+
     norm_expr <- create_expr_obj(
         name = "normalized",
         exprMat = norm_expr,
@@ -861,6 +913,15 @@ filterGiotto <- function(
         provenance = provenance,
         misc = NULL
     )
+  
+    # Save dbMatrix to db
+    if(compute_mat && !is.null(norm_scaled_expr)){
+        norm_scaled_expr = .compute_dbMatrix(
+            dbMatrix = norm_scaled_expr,
+            name = 'scaled',
+            verbose = verbose
+        ) 
+    }
 
     norm_scaled_expr <- create_expr_obj(
         name = "scaled",
diff --git a/R/python_hmrf.R b/R/python_hmrf.R
index 76bb4a503..6b0b597d0 100644
--- a/R/python_hmrf.R
+++ b/R/python_hmrf.R
@@ -135,8 +135,8 @@ doHMRF <- function(gobject,
     }
 
     if (!"matrix" %in% class(expr_values)) {
-        warning("this matrix will be converted to a dense and memory intensive
-                base matrix ...")
+        warning("\n this matrix will be converted to a dense and memory intensive
+                base matrix ...\n")
         expr_values <- as.matrix(expr_values)
     }
 
@@ -145,7 +145,7 @@ doHMRF <- function(gobject,
 
     # overwrite if exists
     if (file.exists(expression_file) & overwrite_output == TRUE) {
-        message("expression_matrix.txt already exists at this location, will be
+        message("\n expression_matrix.txt already exists at this location, will be
                 overwritten")
         data.table::fwrite(
             data.table::as.data.table(expr_values, keep.rownames = "gene"),
@@ -153,7 +153,7 @@ doHMRF <- function(gobject,
             row.names = FALSE, sep = " "
         )
     } else if (file.exists(expression_file) & overwrite_output == FALSE) {
-        message("expression_matrix.txt already exists at this location, will be
+        message("\n expression_matrix.txt already exists at this location, will be
                 used again")
     } else {
         data.table::fwrite(
@@ -177,7 +177,7 @@ doHMRF <- function(gobject,
         ]
     } else {
         if (is.null(spatial_genes)) {
-            stop("you need to provide a vector of spatial genes (~500)")
+            stop("\n you need to provide a vector of spatial genes (~500)")
         }
         spatial_genes_detected <- spatial_genes[
             spatial_genes %in% rownames(expr_values)
@@ -187,14 +187,14 @@ doHMRF <- function(gobject,
 
     # overwrite if exists
     if (file.exists(spatial_genes_file) & overwrite_output == TRUE) {
-        message("spatial_genes.txt already exists at this location, will be
+        message("\n spatial_genes.txt already exists at this location, will be
                 overwritten")
         write.table(spatial_genes_detected,
             file = spatial_genes_file,
             quote = FALSE, col.names = FALSE, row.names = FALSE
         )
     } else if (file.exists(spatial_genes_file) & overwrite_output == FALSE) {
-        message("spatial_genes.txt already exists at this location, will be
+        message("\n spatial_genes.txt already exists at this location, will be
                 used again")
     } else {
         write.table(spatial_genes_detected,
@@ -217,14 +217,14 @@ doHMRF <- function(gobject,
     spatial_network_file <- paste0(output_folder, "/", "spatial_network.txt")
 
     if (file.exists(spatial_network_file) & overwrite_output == TRUE) {
-        message("spatial_network.txt already exists at this location, will be
+        message("\n spatial_network.txt already exists at this location, will be
                 overwritten")
         write.table(spatial_network,
             file = spatial_network_file,
             row.names = FALSE, col.names = FALSE, quote = FALSE, sep = "\t"
         )
     } else if (file.exists(spatial_network_file) & overwrite_output == FALSE) {
-        message("spatial_network.txt already exists at this location, will be
+        message("\n spatial_network.txt already exists at this location, will be
                 used again")
     } else {
         write.table(spatial_network,
@@ -258,14 +258,14 @@ doHMRF <- function(gobject,
     )
 
     if (file.exists(spatial_location_file) & overwrite_output == TRUE) {
-        message("spatial_cell_locations.txt already exists at this location,
+        message("\n spatial_cell_locations.txt already exists at this location,
                 will be overwritten")
         write.table(spatial_location,
             file = spatial_location_file,
             row.names = FALSE, col.names = FALSE, quote = FALSE, sep = "\t"
         )
     } else if (file.exists(spatial_location_file)) {
-        message("spatial_cell_locations.txt already exists at this location,
+        message("\n spatial_cell_locations.txt already exists at this location,
                 will be used again")
     } else {
         write.table(spatial_location,
@@ -370,7 +370,7 @@ loadHMRF <- function(
         python_path_used) {
     output_data <- paste0(output_folder_used, "/", "result.spatial.zscore")
     if (!file.exists(output_data)) {
-        stop("doHMRF was not run in this output directory")
+        stop("\n doHMRF was not run in this output directory")
     }
 
     # check if it indeed exists
@@ -411,7 +411,7 @@ viewHMRFresults <- function(
         third_dim = FALSE,
         ...) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
-        stop("HMRFoutput needs to be output from doHMRFextend")
+        stop("\n HMRFoutput needs to be output from doHMRFextend")
     }
 
     ## reader.py and get_result.py paths
@@ -497,7 +497,7 @@ writeHMRFresults <- function(
         betas_to_view = NULL,
         print_command = FALSE) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
-        stop("HMRFoutput needs to be output from doHMRFextend")
+        stop("\n HMRFoutput needs to be output from doHMRFextend")
     }
 
     ## reader.py and get_result.py paths
@@ -514,7 +514,7 @@ writeHMRFresults <- function(
 
     # k-values
     if (is.null(k)) {
-        stop("you need to select a k that was used with doHMRFextend")
+        stop("\n you need to select a k that was used with doHMRFextend")
     }
     k <- HMRFoutput$k
 
@@ -609,7 +609,7 @@ addHMRF <- function(
         betas_to_add = NULL,
         hmrf_name = NULL) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
-        stop("HMRFoutput needs to be output from doHMRFextend")
+        stop("\n HMRFoutput needs to be output from doHMRFextend")
     }
 
     # Set feat_type and spat_unit
@@ -640,7 +640,7 @@ addHMRF <- function(
 
     # k-values
     if (is.null(k)) {
-        stop("you need to select a k that was used with doHMRFextend")
+        stop("\n you need to select a k that was used with doHMRFextend")
     }
     k <- HMRFoutput$k
 
@@ -743,7 +743,7 @@ viewHMRFresults2D <- function(
     )
 
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
-        stop("HMRFoutput needs to be output from doHMRFextend")
+        stop("\n HMRFoutput needs to be output from doHMRFextend")
     }
 
     ## reader.py and get_result.py paths
@@ -760,7 +760,7 @@ viewHMRFresults2D <- function(
 
     # k-values
     if (is.null(k)) {
-        stop("you need to select a k that was used with doHMRFextend")
+        stop("\n please select a valid k used with doHMRFextend")
     }
     k <- HMRFoutput$k
 
@@ -853,7 +853,7 @@ viewHMRFresults3D <- function(
         betas_to_view = NULL,
         ...) {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
-        stop("HMRFoutput needs to be output from doHMRFextend")
+        stop("\n HMRFoutput needs to be output from doHMRFextend")
     }
 
     spat_unit <- set_default_spat_unit(
@@ -877,7 +877,7 @@ viewHMRFresults3D <- function(
 
     # k-values
     if (is.null(k)) {
-        stop("you need to select a k that was used with doHMRFextend")
+        stop("\n please select a valid k used with doHMRFextend")
     }
     k <- HMRFoutput$k
 
@@ -1118,9 +1118,9 @@ filterSpatialGenes <- function(gobject, spat_unit = NULL, feat_type = NULL, spat
         xPt <- length(y0s) - xPt
         y_cutoff <- y0[xPt] # The y-value at this x point. This is our y_cutoff.
         gx_sorted <- head(gx_sorted, n = xPt)
-        message("Elbow method chosen to determine number of spatial genes.")
+        message("\n Elbow method chosen to determine number of spatial genes.")
         cat(paste0(
-            "Elbow point determined to be at x=", xPt, " genes",
+            "\n Elbow point determined to be at x=", xPt, " genes",
             " y=", y_cutoff
         ))
     }
@@ -1164,7 +1164,7 @@ chooseAvailableSpatialGenes <- function(gobject, spat_unit = NULL, feat_type = N
     } else if (eval3 == TRUE) {
         return("silhouetteRank")
     } else {
-        stop(paste0("No available spatial genes. Please run binSpect or
+        stop(paste0("\n No available spatial genes. Please run binSpect or
                     silhouetteRank\n"), call. = FALSE)
     }
 }
@@ -1223,14 +1223,14 @@ checkAndFixSpatialGenes <- function(
     } else if (use_spatial_genes == "binSpect") {
         eval1 <- "binSpect.pval" %in% names(gx)
         if (eval1 == FALSE) {
-            stop(paste0("use_spatial_genes is set to binSpect, but it has
+            stop(paste0("\n use_spatial_genes is set to binSpect, but it has
                         not been run yet. Run binSpect first."),
                 call. = FALSE
             )
         }
         return(use_spatial_genes)
     } else {
-        stop(paste0("use_spatial_genes is set to one that is not supported."),
+        stop(paste0("\n use_spatial_genes is set to one that is not supported."),
             call. = FALSE
         )
     }
@@ -1358,7 +1358,7 @@ initHMRF_V2 <-
         factor_step = 1.05,
         python_path = NULL) {
         wrap_msg(
-            "If used in published research, please cite:
+            "\n If used in published research, please cite:
         Q Zhu, S Shah, R Dries, L Cai, GC Yuan.
         'Identification of spatially associated subpopulations by combining
         scRNAseq and sequential fluorescence in situ hybridization data'
@@ -1406,24 +1406,24 @@ initHMRF_V2 <-
 
         if (use_neighborhood_composition) {
             if (is.null(spatial_network_name_for_neighborhood)) {
-                stop("spatial network is required to define neighborhood,
+                stop("\n spatial network is required to define neighborhood,
                     set with \'spatial_network_name_for_neighborhood\' \n",
                     call. = FALSE
                 )
             } else if (is.null(metadata_to_use)) {
-                stop("please specify the cluster in meta data, set with
+                stop("\n please specify the cluster in meta data, set with
                     \'metadata_to_use\' \n",
                     call. = FALSE
                 )
             } else if (is.null(cx[[metadata_to_use]])) {
-                stop("please provide a valid index in meta data, set with
+                stop("\n please provide a valid index in meta data, set with
                     \'metadata_to_use\'",
                     call. = FALSE
                 )
             }
 
             cat(paste0(
-                "use spatial network composition of \'",
+                "\n use spatial network composition of \'",
                 metadata_to_use, "\' for domain clustering"
             ))
 
@@ -1471,12 +1471,12 @@ initHMRF_V2 <-
             rownames(y0) <- cell_ID_enrich
 
             cat(paste0(
-                "Spatial enrichment result: \'",
+                "\n Spatial enrichment result: \'",
                 existing_spatial_enrichm_to_use, "\' is used."
             ))
 
             if (sum(!rownames(y0) %in% cx$cell_ID) > 0) {
-                stop("Rownames of selected spatial enrichment result do not
+                stop("\n Rownames of selected spatial enrichment result do not
                     match to (a subset of) Cell IDs, please fix them.",
                     call. = FALSE
                 )
@@ -1526,7 +1526,7 @@ initHMRF_V2 <-
             if (!"binSpect.pval" %in% names(gx) &&
                 !"silhouetteRank.score" %in% names(gx) &&
                 !"silhouetteRankTest.pval" %in% names(gx)) {
-                stop(paste0("Giotto spatial gene detection has not been run.
+                stop(paste0("\n Giotto spatial gene detection has not been run.
                     Please run spatial gene detection first: binSpect,
                     silhouetteRank."),
                     call. = FALSE
@@ -1534,8 +1534,8 @@ initHMRF_V2 <-
             }
 
             if (!is.null(user_gene_list)) {
-                message("User supplied gene list detected.")
-                message("Checking user gene list is spatial...")
+                message("\n User supplied gene list detected.")
+                message("\n Checking user gene list is spatial...")
 
                 use_spatial_genes <- chooseAvailableSpatialGenes(gobject)
                 filtered <- filterSpatialGenes(
@@ -1549,26 +1549,26 @@ initHMRF_V2 <-
                 )
                 if (filtered$num_genes_removed > 0) {
                     cat(paste0(
-                        "Removed ", filtered$num_genes_removed,
+                        "\n Removed ", filtered$num_genes_removed,
                         " from user's input gene list due to being absent or
                         non-spatial genes."
                     ))
                     cat(paste0(
-                        "Kept ", length(filtered$genes),
+                        "\n Kept ", length(filtered$genes),
                         " spatial genes for next step"
                     ))
                 }
                 spatial_genes <- filtered$genes
 
                 if (length(spatial_genes) == 0) {
-                    stop("No genes are remaining to do HMRF. Please give a
+                    stop("\n No genes are remaining to do HMRF. Please give a
                         larger gene list.",
                         call. = FALSE
                     )
                 }
             } else {
                 cat(paste0(
-                    "Choosing spatial genes from the results of ",
+                    "\n Choosing spatial genes from the results of ",
                     use_spatial_genes
                 ))
                 use_spatial_genes <- checkAndFixSpatialGenes(
@@ -1589,7 +1589,7 @@ initHMRF_V2 <-
                     method = filter_method
                 )
                 cat(paste0(
-                    "Kept ", length(filtered$genes),
+                    "\n Kept ", length(filtered$genes),
                     " top spatial genes for next step"
                 ))
                 spatial_genes <- filtered$genes
@@ -1603,7 +1603,7 @@ initHMRF_V2 <-
                 ]
                 y0 <- (pc.expr[, use_pca_dim])
             } else {
-                message("Computing spatial coexpression modules...")
+                message("\n Computing spatial coexpression modules...")
                 spat_cor_netw_DT <- detectSpatialCorFeats(
                     gobject = gobject,
                     feat_type = feat_type,
@@ -1623,7 +1623,7 @@ initHMRF_V2 <-
                             name = "spat_netw_clus",
                             k = 20
                         )
-                        message("Sampling spatial genes from coexpression
+                        message("\n Sampling spatial genes from coexpression
                                 modules...")
                         sample_genes <- sampling_sp_genes(
                             spat_cor_netw_DT$cor_clusters$spat_netw_clus,
@@ -1633,15 +1633,15 @@ initHMRF_V2 <-
                         )
                         spatial_genes_selected <- sample_genes$union_genes
                         cat(paste0(
-                            "Sampled ", length(spatial_genes_selected),
+                            "\n Sampled ", length(spatial_genes_selected),
                             " genes."
                         ))
                     } else {
                         spatial_genes_selected <- spatial_genes
                     }
                     cat(paste0(
-                        "Will use ", length(spatial_genes_selected),
-                        "spatial genes for initialization of HMRF."
+                        "\n Will use ", length(spatial_genes_selected),
+                        " spatial genes for initialization of HMRF."
                     ))
                     expr_values <- expr_values[spatial_genes_selected, ]
                 } else {
@@ -1650,7 +1650,7 @@ initHMRF_V2 <-
                     )
                     if (k.sp < cluster_metagene) {
                         cat(paste0(
-                            "construct ", k.sp,
+                            "\n construct ", k.sp,
                             " coexpression modules due to limited gene size..."
                         ))
                     }
@@ -1663,7 +1663,7 @@ initHMRF_V2 <-
                         show_top_feats = 1
                     )
 
-                    cat(paste0("Collecting top spatial genes and calculating
+                    cat(paste0("\n Collecting top spatial genes and calculating
                         metagenes from ", k.sp, " coexpression modules..."))
 
                     top_per_module <- cluster_genes_DT[
@@ -1742,7 +1742,7 @@ initHMRF_V2 <-
                 edge_ind <- edge_ind + 1
             }
         }
-        message("Parsing neighborhood graph...")
+        message("\n Parsing neighborhood graph...")
         pp <- tidygraph::tbl_graph(
             edges = as.data.frame(edgelist), directed = FALSE
         )
@@ -1758,11 +1758,11 @@ initHMRF_V2 <-
         cl.method <- tolower(cl.method)
         if (!cl.method %in% c("km", "leiden", "louvain")) {
             cl.method <- "km"
-            message("clustering method not specified, use kmeans as default...")
+            message("\n clustering method not specified, use kmeans as default...")
         }
 
         if (cl.method == "km") {
-            message("Kmeans initialization...")
+            message("\n Kmeans initialization...")
             kk <- smfishHmrf::smfishHmrf.generate.centroid(
                 y = y, par_k = k, par_seed = hmrf_seed,
                 nstart = nstart
@@ -1778,7 +1778,7 @@ initHMRF_V2 <-
             gobject@dimension_reduction$cells$spatial$spatial_feat$coordinates <- y
 
             gobject <- createNearestNetwork(
-                gobject = gobject,
+                gobject = gobject, spat_unit = spat_unit, feat_type = feat_type,
                 dim_reduction_to_use = "spatial",
                 dim_reduction_name = "spatial_feat",
                 dimensions_to_use = seq_len(ncol(y)),
@@ -1786,9 +1786,9 @@ initHMRF_V2 <-
             )
 
             if (cl.method == "leiden") {
-                message("Leiden clustering initialization...")
+                message("\n Leiden clustering initialization...")
                 leiden.cl <- doLeidenCluster(
-                    gobject = gobject,
+                    gobject = gobject, spat_unit = spat_unit, feat_type = feat_type,
                     nn_network_to_use = "sNN",
                     network_name = "sNN.initHMRF",
                     set_seed = hmrf_seed,
@@ -1801,9 +1801,9 @@ initHMRF_V2 <-
                 ]
                 mu <- aggregate(y, by = list(cl.match), FUN = mean)
             } else if (cl.method == "louvain") {
-                message("Louvain clustering initialization...")
+                message("\n Louvain clustering initialization...")
                 louvain.cl <- doLouvainCluster(
-                    gobject = gobject,
+                    gobject = gobject, spat_unit = spat_unit, feat_type = feat_type,
                     nn_network_to_use = "sNN",
                     network_name = "sNN.initHMRF",
                     set_seed = hmrf_seed,
@@ -1836,7 +1836,7 @@ initHMRF_V2 <-
             )
             damp[i] <- ifelse(is.null(di), 0, di)
         }
-        message("Done")
+        message("\n Done")
         list(
             y = y, nei = nei, numnei = numnei, blocks = blocks,
             damp = damp, mu = mu, sigma = sigma, k = k, genes = colnames(y),
@@ -1885,39 +1885,39 @@ initHMRF_V2 <-
 #' @export
 doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
     message(
-        "If used in published research, please cite:
+        "\n If used in published research, please cite:
     Q Zhu, S Shah, R Dries, L Cai, GC Yuan.
     'Identification of spatially associated subpopulations by combining
     scRNAseq and sequential fluorescence in situ hybridization data'
     Nature biotechnology 36 (12), 1183-1190. 2018"
     )
 
-    message("Please find more explanation and instruction of the HMRF function
+    message("\n Please find more explanation and instruction of the HMRF function
     on \n https://bitbucket.org/qzhudfci/smfishhmrf-r/src/master/TRANSITION.md")
     if (!"y" %in% names(HMRF_init_obj)) {
-        stop("expression matrix 'y' not in the intialization object")
+        stop("\n expression matrix 'y' not in the intialization object")
     }
     if (!"nei" %in% names(HMRF_init_obj)) {
-        stop("neighbor matrix 'nei' not in the intialization object")
+        stop("\n neighbor matrix 'nei' not in the intialization object")
     }
     if (!"numnei" %in% names(HMRF_init_obj)) {
-        stop("number of neighbors 'numnei' not in the intialization object")
+        stop("\n number of neighbors 'numnei' not in the intialization object")
     }
     if (!"blocks" %in% names(HMRF_init_obj)) {
-        stop("iteration groups 'blocks' not in the intialization object")
+        stop("\n iteration groups 'blocks' not in the intialization object")
     }
     if (!"damp" %in% names(HMRF_init_obj)) {
-        stop("dampen factors 'damp' not in the intialization object")
+        stop("\n dampen factors 'damp' not in the intialization object")
     }
     if (!"mu" %in% names(HMRF_init_obj)) {
-        stop("initial mean vector 'mu' not in the intialization object")
+        stop("\n initial mean vector 'mu' not in the intialization object")
     }
     if (!"sigma" %in% names(HMRF_init_obj)) {
-        stop("initial covariance matrix 'sigma' not in the intialization
+        stop("\n initial covariance matrix 'sigma' not in the intialization
             object")
     }
     if (!"k" %in% names(HMRF_init_obj)) {
-        stop("cluster number 'k' not in the intialization object")
+        stop("\n cluster number 'k' not in the intialization object")
     }
     if (!"spat_unit" %in% names(HMRF_init_obj)) {
         HMRF_init_obj[["spat_unit"]] <- NULL
@@ -1939,10 +1939,10 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
 
     if (is.null(betas)) {
         beta_seq <- max(ceiling(ncol(y) / 10), 2)
-        cat(paste0("Default value beta = ", beta_seq, " is used..."))
+        cat(paste0("\n Default value beta = ", beta_seq, " is used..."))
     } else if (length(betas) != 3 || (sum(betas[seq_len(3)] < 0) > 0)) {
         stop(wrap_txt(
-            "please provide betas as a vector of 3 non-negative numbers
+            "\n please provide betas as a vector of 3 non-negative numbers
             (initial value, increment, total iteration number)",
             errWidth = TRUE
         ))
@@ -2008,7 +2008,7 @@ doHMRF_V2 <- function(HMRF_init_obj, betas = NULL) {
 #' @export
 addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") {
     if (!"HMRFoutput" %in% class(HMRFoutput)) {
-        stop("HMRFoutput needs to be output from doHMRF_V2()")
+        stop("\n HMRFoutput needs to be output from doHMRF_V2()")
     }
     if (!"spat_unit" %in% names(HMRFoutput)) {
         HMRFoutput[["spat_unit"]] <- NULL
@@ -2033,19 +2033,19 @@ addHMRF_V2 <- function(gobject, HMRFoutput, name = "hmrf") {
             gobject = gobject,
             spat_unit = spat_unit,
             feat_type = feat_type,
-            column_cell_ID = "cell_ID",
-            # new_metadata = HMRFoutput[[i]]$class[match(
-            #     ordered_cell_IDs, names(HMRFoutput[[i]]$class))],
-            new_metadata = HMRFoutput[[i]]$prob[ordered_cell_IDs, ],
+            new_metadata = HMRFoutput[[i]]$class[match(
+                ordered_cell_IDs, 
+                rownames(HMRFoutput[[i]]$prob))],
             vector_name = paste(name, names(HMRFoutput)[i])
+            # ,column_cell_ID = 'cell_ID',
             # by_column = TRUE
         )
     }
     return(gobject)
 }
 
-
-
+                             
+                             
 #' @title viewHMRFresults_V2
 #' @name viewHMRFresults_V2
 #' @description function to view HMRF results with multiple betas
diff --git a/man/getONTraCv1Input.Rd b/man/getONTraCv1Input.Rd
new file mode 100644
index 000000000..784a77678
--- /dev/null
+++ b/man/getONTraCv1Input.Rd
@@ -0,0 +1,45 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{getONTraCv1Input}
+\alias{getONTraCv1Input}
+\title{getONTraCv1Input}
+\usage{
+getONTraCv1Input(
+  gobject,
+  cell_type,
+  output_path = getwd(),
+  spat_unit = NULL,
+  feat_type = NULL,
+  verbose = TRUE
+)
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{cell_type}{the cell type column name in the metadata}
+
+\item{output_path}{the path to save the output file}
+
+\item{spat_unit}{spatial unit (e.g. "cell")}
+
+\item{feat_type}{feature type (e.g. "rna", "dna", "protein")}
+
+\item{verbose}{be verbose}
+}
+\value{
+data.table with columns: Cell_ID, Sample, x, y, Cell_Type
+}
+\description{
+generate the input data for ONTraC v1
+}
+\details{
+This function generate the input data for ONTraC v1
+}
+\examples{
+g <- GiottoData::loadGiottoMini("visium")
+
+getONTraCv1Input(
+  gobject = g,
+  cell_type = "custom_leiden"
+)
+}
diff --git a/man/loadOntraCResults.Rd b/man/loadOntraCResults.Rd
new file mode 100644
index 000000000..2eda6902f
--- /dev/null
+++ b/man/loadOntraCResults.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{loadOntraCResults}
+\alias{loadOntraCResults}
+\title{loadOntraCResults}
+\usage{
+loadOntraCResults(gobject, ontrac_results_dir = getwd())
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{ontrac_results_dir}{the directory where the ONTraC results are saved}
+}
+\value{
+gobject with ONTraC results
+}
+\description{
+load ONTraC results
+}
+\details{
+This function loads the ONTraC results into the giotto object.
+}
diff --git a/man/load_cell_NT_score.Rd b/man/load_cell_NT_score.Rd
new file mode 100644
index 000000000..4a8b5d87b
--- /dev/null
+++ b/man/load_cell_NT_score.Rd
@@ -0,0 +1,22 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{load_cell_NT_score}
+\alias{load_cell_NT_score}
+\title{load_cell_NT_score}
+\usage{
+load_cell_NT_score(gobject, ontrac_results_dir = getwd())
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{ontrac_results_dir}{the directory where the ONTraC results are saved}
+}
+\value{
+gobject with cell-level NT score
+}
+\description{
+load cell-level NT score
+}
+\details{
+This function loads the ONTraC outputed cell-level NT score
+}
diff --git a/man/load_cell_bin_niche_cluster.Rd b/man/load_cell_bin_niche_cluster.Rd
new file mode 100644
index 000000000..4aafb4cb5
--- /dev/null
+++ b/man/load_cell_bin_niche_cluster.Rd
@@ -0,0 +1,23 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{load_cell_bin_niche_cluster}
+\alias{load_cell_bin_niche_cluster}
+\title{load_cell_bin_niche_cluster}
+\usage{
+load_cell_bin_niche_cluster(gobject, ontrac_results_dir = getwd())
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{ontrac_results_dir}{the directory where the ONTraC results are saved}
+}
+\value{
+gobject with cell-level binarized niche cluster
+}
+\description{
+load cell-level binarized niche cluster
+}
+\details{
+This function loads the ONTraC outputed cell-level binarized niche
+cluster into the giotto object.
+}
diff --git a/man/load_cell_niche_cluster_prob.Rd b/man/load_cell_niche_cluster_prob.Rd
new file mode 100644
index 000000000..2bcb59346
--- /dev/null
+++ b/man/load_cell_niche_cluster_prob.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{load_cell_niche_cluster_prob}
+\alias{load_cell_niche_cluster_prob}
+\title{load_cell_niche_cluster_prob}
+\usage{
+load_cell_niche_cluster_prob(
+  gobject,
+  ontrac_results_dir = getwd(),
+  spat_unit = "cell",
+  feat_type = "niche cluster",
+  name = "prob"
+)
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{ontrac_results_dir}{the directory where the ONTraC results are saved}
+
+\item{spat_unit}{spatial unit (e.g. "cell")}
+
+\item{feat_type}{feature type (e.g. "rna", "dna", "protein")}
+
+\item{name}{name for the probability matrix}
+}
+\value{
+gobject with cell-niche cluster probability matrix
+}
+\description{
+load cell-niche cluster probability
+}
+\details{
+This function loads the ONTraC outputed cell-niche cluster
+probability as an exprObj into the giotto object.
+}
diff --git a/man/load_nc_connectivity.Rd b/man/load_nc_connectivity.Rd
new file mode 100644
index 000000000..ae96d3677
--- /dev/null
+++ b/man/load_nc_connectivity.Rd
@@ -0,0 +1,35 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{load_nc_connectivity}
+\alias{load_nc_connectivity}
+\title{load_nc_connectivity}
+\usage{
+load_nc_connectivity(
+  gobject,
+  ontrac_results_dir = getwd(),
+  spat_unit = "niche cluster",
+  feat_type = "connectivity",
+  name = "normalized"
+)
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{ontrac_results_dir}{the directory where the ONTraC results are saved}
+
+\item{spat_unit}{spatial unit (e.g. "cell")}
+
+\item{feat_type}{feature type (e.g. "rna", "dna", "protein")}
+
+\item{name}{name for the connectivity matrix}
+}
+\value{
+gobject with niche cluster connectivity matrix
+}
+\description{
+load niche cluster connectivity
+}
+\details{
+This function loads the ONTraC outputed niche cluster connectivity
+matrix as an exprObj into the giotto object.
+}
diff --git a/man/plotCTCompositionInNicheCluster.Rd b/man/plotCTCompositionInNicheCluster.Rd
new file mode 100644
index 000000000..24c9a7109
--- /dev/null
+++ b/man/plotCTCompositionInNicheCluster.Rd
@@ -0,0 +1,47 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{plotCTCompositionInNicheCluster}
+\alias{plotCTCompositionInNicheCluster}
+\title{plotCTCompositionInNicheCluster}
+\usage{
+plotCTCompositionInNicheCluster(
+  gobject,
+  cell_type,
+  values = "prob",
+  spat_unit = "cell",
+  feat_type = "niche cluster",
+  show_plot = NULL,
+  return_plot = NULL,
+  save_plot = NULL,
+  save_param = list(),
+  theme_param = list(),
+  default_save_name = "CellTypeCompositionInNicheCluster"
+)
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{cell_type}{the cell type column name in the metadata}
+
+\item{values}{name of the expression matrix stored connectivity values}
+
+\item{spat_unit}{name of spatial unit niche stored cluster features}
+
+\item{feat_type}{name of the feature type stored niche cluster connectivities}
+
+\item{show_plot}{logical. show plot}
+
+\item{return_plot}{logical. return ggplot object}
+
+\item{save_plot}{logical. save the plot}
+
+\item{save_param}{list of saving parameters, see \code{\link{showSaveParameters}}}
+
+\item{default_save_name}{default save name for saving, don't change, change save_name in save_param}
+}
+\description{
+plot cell type composition within each niche cluster
+}
+\details{
+This function plots the niche cluster connectivity matrix
+}
diff --git a/man/plotCellTypeNTScore.Rd b/man/plotCellTypeNTScore.Rd
new file mode 100644
index 000000000..c24a73bd9
--- /dev/null
+++ b/man/plotCellTypeNTScore.Rd
@@ -0,0 +1,42 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{plotCellTypeNTScore}
+\alias{plotCellTypeNTScore}
+\title{plotCellTypeNTScore}
+\usage{
+plotCellTypeNTScore(
+  gobject,
+  cell_type,
+  values = "NTScore",
+  spat_unit = "cell",
+  feat_type = "rna",
+  show_plot = NULL,
+  return_plot = NULL,
+  save_plot = NULL,
+  save_param = list(),
+  theme_param = list(),
+  default_save_name = "CellTypeNTScore"
+)
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{cell_type}{the cell type column name in the metadata}
+
+\item{spat_unit}{spatial unit (e.g. "cell")}
+
+\item{feat_type}{feature type (e.g. "rna", "dna", "protein")}
+
+\item{show_plot}{logical. show plot}
+
+\item{return_plot}{logical. return ggplot object}
+
+\item{save_plot}{logical. save the plot}
+
+\item{save_param}{list of saving parameters, see \code{\link{showSaveParameters}}}
+
+\item{default_save_name}{default save name for saving, don't change, change save_name in save_param}
+}
+\description{
+plot NTScore by cell type
+}
diff --git a/man/plotNicheClusterConnectivity.Rd b/man/plotNicheClusterConnectivity.Rd
new file mode 100644
index 000000000..b6f236496
--- /dev/null
+++ b/man/plotNicheClusterConnectivity.Rd
@@ -0,0 +1,44 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/ONTraC_wrapper.R
+\name{plotNicheClusterConnectivity}
+\alias{plotNicheClusterConnectivity}
+\title{plotNicheClusterConnectivity}
+\usage{
+plotNicheClusterConnectivity(
+  gobject,
+  spat_unit = "niche cluster",
+  feat_type = "connectivity",
+  values = "normalized",
+  show_plot = NULL,
+  return_plot = NULL,
+  save_plot = NULL,
+  save_param = list(),
+  theme_param = list(),
+  default_save_name = "NicheClusterConnectivity"
+)
+}
+\arguments{
+\item{gobject}{giotto object}
+
+\item{spat_unit}{name of spatial unit niche stored cluster features}
+
+\item{feat_type}{name of the feature type stored niche cluster connectivities}
+
+\item{values}{name of the expression matrix stored connectivity values}
+
+\item{show_plot}{logical. show plot}
+
+\item{return_plot}{logical. return ggplot object}
+
+\item{save_plot}{logical. save the plot}
+
+\item{save_param}{list of saving parameters, see \code{\link{showSaveParameters}}}
+
+\item{default_save_name}{default save name for saving, don't change, change save_name in save_param}
+}
+\description{
+plot niche cluster connectivity
+}
+\details{
+This function plots the niche cluster connectivity matrix
+}
diff --git a/man/reexports.Rd b/man/reexports.Rd
index f5533929f..c4a2e34b5 100644
--- a/man/reexports.Rd
+++ b/man/reexports.Rd
@@ -298,7 +298,7 @@ These objects are imported from other packages. Follow the links
 below to see their documentation.
 
 \describe{
-  \item{GiottoClass}{\code{\link[GiottoClass:activeFeatType-generic]{activeFeatType}}, \code{\link[GiottoClass:activeFeatType-generic]{activeFeatType<-}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit<-}}, \code{\link[GiottoClass]{addCellMetadata}}, \code{\link[GiottoClass]{addFeatMetadata}}, \code{\link[GiottoClass]{addGiottoImage}}, \code{\link[GiottoClass]{addGiottoImageMG}}, \code{\link[GiottoClass]{addGiottoLargeImage}}, \code{\link[GiottoClass]{addGiottoPoints}}, \code{\link[GiottoClass:addGiottoPoints]{addGiottoPoints3D}}, \code{\link[GiottoClass]{addGiottoPolygons}}, \code{\link[GiottoClass]{addNetworkLayout}}, \code{\link[GiottoClass]{addSpatialCentroidLocations}}, \code{\link[GiottoClass]{addSpatialCentroidLocationsLayer}}, \code{\link[GiottoClass]{aggregateStacks}}, \code{\link[GiottoClass]{aggregateStacksExpression}}, \code{\link[GiottoClass]{aggregateStacksLocations}}, \code{\link[GiottoClass]{aggregateStacksPolygonOverlaps}}, \code{\link[GiottoClass]{aggregateStacksPolygons}}, \code{\link[GiottoClass]{anndataToGiotto}}, \code{\link[GiottoClass]{annotateGiotto}}, \code{\link[GiottoClass]{annotateSpatialGrid}}, \code{\link[GiottoClass]{annotateSpatialNetwork}}, \code{\link[GiottoClass]{as.points}}, \code{\link[GiottoClass]{as.polygons}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sf}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sp}}, \code{\link[GiottoClass:r_spatial_conversions]{as.stars}}, \code{\link[GiottoClass:r_spatial_conversions]{as.terra}}, \code{\link[GiottoClass]{calculateMetaTable}}, \code{\link[GiottoClass]{calculateMetaTableCells}}, \code{\link[GiottoClass]{calculateOverlap}}, \code{\link[GiottoClass]{calculateOverlapParallel}}, \code{\link[GiottoClass]{calculateOverlapPolygonImages}}, \code{\link[GiottoClass]{calculateOverlapRaster}}, \code{\link[GiottoClass]{calculateOverlapSerial}}, \code{\link[GiottoClass]{calculateSpatCellMetadataProportions}}, \code{\link[GiottoClass:centroids-generic]{centroids}}, \code{\link[GiottoClass]{changeGiottoInstructions}}, \code{\link[GiottoClass]{changeImageBg}}, \code{\link[GiottoClass]{checkGiottoEnvironment}}, \code{\link[GiottoClass]{circleVertices}}, \code{\link[GiottoClass]{combineCellData}}, \code{\link[GiottoClass]{combineFeatureData}}, \code{\link[GiottoClass]{combineFeatureOverlapData}}, \code{\link[GiottoClass]{combineMetadata}}, \code{\link[GiottoClass]{combineSpatialCellFeatureInfo}}, \code{\link[GiottoClass]{combineSpatialCellMetadataInfo}}, \code{\link[GiottoClass]{combineToMultiPolygon}}, \code{\link[GiottoClass]{convertGiottoLargeImageToMG}}, \code{\link[GiottoClass]{copy}}, \code{\link[GiottoClass]{createBentoAdata}}, \code{\link[GiottoClass]{createCellMetaObj}}, \code{\link[GiottoClass]{createDimObj}}, \code{\link[GiottoClass]{createExprObj}}, \code{\link[GiottoClass]{createFeatMetaObj}}, \code{\link[GiottoClass]{createGiottoImage}}, \code{\link[GiottoClass]{createGiottoInstructions}}, \code{\link[GiottoClass]{createGiottoLargeImage}}, \code{\link[GiottoClass]{createGiottoLargeImageList}}, \code{\link[GiottoClass]{createGiottoObject}}, \code{\link[GiottoClass]{createGiottoObjectSubcellular}}, \code{\link[GiottoClass]{createGiottoPoints}}, \code{\link[GiottoClass]{createGiottoPolygon}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromDfr}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromGeoJSON}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromMask}}, \code{\link[GiottoClass]{createMetafeats}}, \code{\link[GiottoClass]{createNearestNetObj}}, \code{\link[GiottoClass]{createNearestNetwork}}, \code{\link[GiottoClass]{createSpatEnrObj}}, \code{\link[GiottoClass]{createSpatialDefaultGrid}}, \code{\link[GiottoClass]{createSpatialDelaunayNetwork}}, \code{\link[GiottoClass]{createSpatialFeaturesKNNnetwork}}, \code{\link[GiottoClass]{createSpatialGrid}}, \code{\link[GiottoClass]{createSpatialKNNnetwork}}, \code{\link[GiottoClass]{createSpatialNetwork}}, \code{\link[GiottoClass]{createSpatialWeightMatrix}}, \code{\link[GiottoClass]{createSpatLocsObj}}, \code{\link[GiottoClass]{createSpatNetObj}}, \code{\link[GiottoClass]{crop}}, \code{\link[GiottoClass]{cropGiottoLargeImage}}, \code{\link[GiottoClass]{density}}, \code{\link[GiottoClass]{distGiottoImage}}, \code{\link[GiottoClass]{estimateImageBg}}, \code{\link[GiottoClass]{ext}}, \code{\link[GiottoClass:ext]{ext<-}}, \code{\link[GiottoClass]{fDataDT}}, \code{\link[GiottoClass:spatIDs-generic]{featIDs}}, \code{\link[GiottoClass:featType-generic]{featType}}, \code{\link[GiottoClass:featType-generic]{featType<-}}, \code{\link[GiottoClass:featureNetwork-class]{featureNetwork}}, \code{\link[GiottoClass]{flip}}, \code{\link[GiottoClass]{gefToGiotto}}, \code{\link[GiottoClass]{getCellMetadata}}, \code{\link[GiottoClass]{getDimReduction}}, \code{\link[GiottoClass]{getExpression}}, \code{\link[GiottoClass]{getFeatureInfo}}, \code{\link[GiottoClass]{getFeatureMetadata}}, \code{\link[GiottoClass]{getGiottoImage}}, \code{\link[GiottoClass]{getMultiomics}}, \code{\link[GiottoClass]{getNearestNetwork}}, \code{\link[GiottoClass]{getPolygonInfo}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass:giotto-class]{giotto}}, \code{\link[GiottoClass:giottoImage-class]{giottoImage}}, \code{\link[GiottoClass:giottoLargeImage-class]{giottoLargeImage}}, \code{\link[GiottoClass]{giottoMasterToSuite}}, \code{\link[GiottoClass:giottoPoints-class]{giottoPoints}}, \code{\link[GiottoClass:giottoPolygon-class]{giottoPolygon}}, \code{\link[GiottoClass]{giottoToAnnData}}, \code{\link[GiottoClass]{giottoToSeurat}}, \code{\link[GiottoClass]{giottoToSeuratV4}}, \code{\link[GiottoClass]{giottoToSeuratV5}}, \code{\link[GiottoClass]{giottoToSpatialExperiment}}, \code{\link[GiottoClass]{hexVertices}}, \code{\link[GiottoClass]{hist}}, \code{\link[GiottoClass]{installGiottoEnvironment}}, \code{\link[GiottoClass:instructions-generic]{instructions}}, \code{\link[GiottoClass:instructions-generic]{instructions<-}}, \code{\link[GiottoClass]{joinGiottoObjects}}, \code{\link[GiottoClass]{loadGiotto}}, \code{\link[GiottoClass]{makePseudoVisium}}, \code{\link[GiottoClass]{objHistory}}, \code{\link[GiottoClass:objName-generic]{objName}}, \code{\link[GiottoClass:objName-generic]{objName<-}}, \code{\link[GiottoClass:generate_grid]{orthoGrid}}, \code{\link[GiottoClass]{overlapImagesToMatrix}}, \code{\link[GiottoClass:overlaps-generic]{overlaps}}, \code{\link[GiottoClass]{overlapToMatrix}}, \code{\link[GiottoClass]{overlapToMatrixMultiPoly}}, \code{\link[GiottoClass]{pDataDT}}, \code{\link[GiottoClass]{plotGiottoImage}}, \code{\link[GiottoClass]{polyStamp}}, \code{\link[GiottoClass:prov-generic]{prov}}, \code{\link[GiottoClass:prov-generic]{prov<-}}, \code{\link[GiottoClass]{readCellMetadata}}, \code{\link[GiottoClass]{readDimReducData}}, \code{\link[GiottoClass]{readExprData}}, \code{\link[GiottoClass]{readExprMatrix}}, \code{\link[GiottoClass]{readFeatData}}, \code{\link[GiottoClass]{readFeatMetadata}}, \code{\link[GiottoClass]{readGiottoInstructions}}, \code{\link[GiottoClass]{readNearestNetData}}, \code{\link[GiottoClass]{readPolygonData}}, \code{\link[GiottoClass]{readSpatEnrichData}}, \code{\link[GiottoClass]{readSpatLocsData}}, \code{\link[GiottoClass]{readSpatNetData}}, \code{\link[GiottoClass]{reconnectGiottoImage}}, \code{\link[GiottoClass]{rectVertices}}, \code{\link[GiottoClass]{removeCellAnnotation}}, \code{\link[GiottoClass]{removeFeatAnnotation}}, \code{\link[GiottoClass]{removeGiottoEnvironment}}, \code{\link[GiottoClass]{replaceGiottoInstructions}}, \code{\link[GiottoClass]{rescale}}, \code{\link[GiottoClass]{rescalePolygons}}, \code{\link[GiottoClass]{saveGiotto}}, \code{\link[GiottoClass]{setCellMetadata}}, \code{\link[GiottoClass]{setDimReduction}}, \code{\link[GiottoClass]{setExpression}}, \code{\link[GiottoClass]{setFeatureInfo}}, \code{\link[GiottoClass]{setFeatureMetadata}}, \code{\link[GiottoClass]{setGiotto}}, \code{\link[GiottoClass]{setGiottoImage}}, \code{\link[GiottoClass]{setMultiomics}}, \code{\link[GiottoClass]{setNearestNetwork}}, \code{\link[GiottoClass]{setPolygonInfo}}, \code{\link[GiottoClass]{setSpatialEnrichment}}, \code{\link[GiottoClass]{setSpatialGrid}}, \code{\link[GiottoClass]{setSpatialLocations}}, \code{\link[GiottoClass]{setSpatialNetwork}}, \code{\link[GiottoClass]{seuratToGiotto}}, \code{\link[GiottoClass]{seuratToGiottoV4}}, \code{\link[GiottoClass]{seuratToGiottoV5}}, \code{\link[GiottoClass]{showGiottoCellMetadata}}, \code{\link[GiottoClass]{showGiottoDimRed}}, \code{\link[GiottoClass]{showGiottoExpression}}, \code{\link[GiottoClass]{showGiottoFeatInfo}}, \code{\link[GiottoClass]{showGiottoFeatMetadata}}, \code{\link[GiottoClass]{showGiottoImageNames}}, \code{\link[GiottoClass]{showGiottoInstructions}}, \code{\link[GiottoClass]{showGiottoNearestNetworks}}, \code{\link[GiottoClass]{showGiottoSpatEnrichments}}, \code{\link[GiottoClass]{showGiottoSpatGrids}}, \code{\link[GiottoClass]{showGiottoSpatialInfo}}, \code{\link[GiottoClass]{showGiottoSpatLocs}}, \code{\link[GiottoClass]{showGiottoSpatNetworks}}, \code{\link[GiottoClass]{showProcessingSteps}}, \code{\link[GiottoClass]{smoothGiottoPolygons}}, \code{\link[GiottoClass]{spatialExperimentToGiotto}}, \code{\link[GiottoClass:spatIDs-generic]{spatIDs}}, \code{\link[GiottoClass]{spatQueryGiottoPolygons}}, \code{\link[GiottoClass]{spatShift}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit<-}}, \code{\link[GiottoClass]{spin}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchGiottoLargeImage}}, \code{\link[GiottoClass]{subsetGiotto}}, \code{\link[GiottoClass]{subsetGiottoLocs}}, \code{\link[GiottoClass]{subsetGiottoLocsMulti}}, \code{\link[GiottoClass]{subsetGiottoLocsSubcellular}}, \code{\link[GiottoClass]{tessellate}}, \code{\link[GiottoClass:generate_grid]{triGrid}}, \code{\link[GiottoClass]{updateGiottoImage}}, \code{\link[GiottoClass]{updateGiottoImageMG}}, \code{\link[GiottoClass]{updateGiottoLargeImage}}, \code{\link[GiottoClass]{updateGiottoObject}}, \code{\link[GiottoClass]{updateGiottoPointsObject}}, \code{\link[GiottoClass]{updateGiottoPolygonObject}}, \code{\link[GiottoClass:wrap]{vect}}, \code{\link[GiottoClass]{wrap}}, \code{\link[GiottoClass]{writeGiottoLargeImage}}}
+  \item{GiottoClass}{\code{\link[GiottoClass:activeFeatType-generic]{activeFeatType}}, \code{\link[GiottoClass:activeFeatType-generic]{activeFeatType<-}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit}}, \code{\link[GiottoClass:activeSpatUnit-generic]{activeSpatUnit<-}}, \code{\link[GiottoClass]{addCellMetadata}}, \code{\link[GiottoClass]{addFeatMetadata}}, \code{\link[GiottoClass]{addGiottoImage}}, \code{\link[GiottoClass]{addGiottoImageMG}}, \code{\link[GiottoClass]{addGiottoLargeImage}}, \code{\link[GiottoClass]{addGiottoPoints}}, \code{\link[GiottoClass:addGiottoPoints]{addGiottoPoints3D}}, \code{\link[GiottoClass]{addGiottoPolygons}}, \code{\link[GiottoClass]{addNetworkLayout}}, \code{\link[GiottoClass]{addSpatialCentroidLocations}}, \code{\link[GiottoClass]{addSpatialCentroidLocationsLayer}}, \code{\link[GiottoClass]{aggregateStacks}}, \code{\link[GiottoClass]{aggregateStacksExpression}}, \code{\link[GiottoClass]{aggregateStacksLocations}}, \code{\link[GiottoClass]{aggregateStacksPolygonOverlaps}}, \code{\link[GiottoClass]{aggregateStacksPolygons}}, \code{\link[GiottoClass]{anndataToGiotto}}, \code{\link[GiottoClass]{annotateGiotto}}, \code{\link[GiottoClass]{annotateSpatialGrid}}, \code{\link[GiottoClass]{annotateSpatialNetwork}}, \code{\link[GiottoClass]{as.points}}, \code{\link[GiottoClass]{as.polygons}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sf}}, \code{\link[GiottoClass:r_spatial_conversions]{as.sp}}, \code{\link[GiottoClass:r_spatial_conversions]{as.stars}}, \code{\link[GiottoClass:r_spatial_conversions]{as.terra}}, \code{\link[GiottoClass]{calculateMetaTable}}, \code{\link[GiottoClass]{calculateMetaTableCells}}, \code{\link[GiottoClass]{calculateOverlap}}, \code{\link[GiottoClass]{calculateOverlapParallel}}, \code{\link[GiottoClass]{calculateOverlapPolygonImages}}, \code{\link[GiottoClass]{calculateOverlapRaster}}, \code{\link[GiottoClass]{calculateOverlapSerial}}, \code{\link[GiottoClass]{calculateSpatCellMetadataProportions}}, \code{\link[GiottoClass:centroids-generic]{centroids}}, \code{\link[GiottoClass]{changeGiottoInstructions}}, \code{\link[GiottoClass]{changeImageBg}}, \code{\link[GiottoClass]{checkGiottoEnvironment}}, \code{\link[GiottoClass]{circleVertices}}, \code{\link[GiottoClass]{combineCellData}}, \code{\link[GiottoClass]{combineFeatureData}}, \code{\link[GiottoClass]{combineFeatureOverlapData}}, \code{\link[GiottoClass]{combineMetadata}}, \code{\link[GiottoClass]{combineSpatialCellFeatureInfo}}, \code{\link[GiottoClass]{combineSpatialCellMetadataInfo}}, \code{\link[GiottoClass]{combineToMultiPolygon}}, \code{\link[GiottoClass]{convertGiottoLargeImageToMG}}, \code{\link[GiottoClass]{copy}}, \code{\link[GiottoClass]{createBentoAdata}}, \code{\link[GiottoClass]{createCellMetaObj}}, \code{\link[GiottoClass]{createDimObj}}, \code{\link[GiottoClass]{createExprObj}}, \code{\link[GiottoClass]{createFeatMetaObj}}, \code{\link[GiottoClass]{createGiottoImage}}, \code{\link[GiottoClass]{createGiottoInstructions}}, \code{\link[GiottoClass]{createGiottoLargeImage}}, \code{\link[GiottoClass]{createGiottoLargeImageList}}, \code{\link[GiottoClass]{createGiottoObject}}, \code{\link[GiottoClass]{createGiottoObjectSubcellular}}, \code{\link[GiottoClass]{createGiottoPoints}}, \code{\link[GiottoClass]{createGiottoPolygon}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromDfr}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromGeoJSON}}, \code{\link[GiottoClass:createGiottoPolygon]{createGiottoPolygonsFromMask}}, \code{\link[GiottoClass]{createMetafeats}}, \code{\link[GiottoClass]{createNearestNetObj}}, \code{\link[GiottoClass]{createNearestNetwork}}, \code{\link[GiottoClass]{createSpatEnrObj}}, \code{\link[GiottoClass]{createSpatLocsObj}}, \code{\link[GiottoClass]{createSpatNetObj}}, \code{\link[GiottoClass]{createSpatialDefaultGrid}}, \code{\link[GiottoClass]{createSpatialDelaunayNetwork}}, \code{\link[GiottoClass]{createSpatialFeaturesKNNnetwork}}, \code{\link[GiottoClass]{createSpatialGrid}}, \code{\link[GiottoClass]{createSpatialKNNnetwork}}, \code{\link[GiottoClass]{createSpatialNetwork}}, \code{\link[GiottoClass]{createSpatialWeightMatrix}}, \code{\link[GiottoClass]{crop}}, \code{\link[GiottoClass]{cropGiottoLargeImage}}, \code{\link[GiottoClass]{density}}, \code{\link[GiottoClass]{distGiottoImage}}, \code{\link[GiottoClass]{estimateImageBg}}, \code{\link[GiottoClass]{ext}}, \code{\link[GiottoClass:ext]{ext<-}}, \code{\link[GiottoClass]{fDataDT}}, \code{\link[GiottoClass:spatIDs-generic]{featIDs}}, \code{\link[GiottoClass:featType-generic]{featType}}, \code{\link[GiottoClass:featType-generic]{featType<-}}, \code{\link[GiottoClass:featureNetwork-class]{featureNetwork}}, \code{\link[GiottoClass]{flip}}, \code{\link[GiottoClass]{gefToGiotto}}, \code{\link[GiottoClass]{getCellMetadata}}, \code{\link[GiottoClass]{getDimReduction}}, \code{\link[GiottoClass]{getExpression}}, \code{\link[GiottoClass]{getFeatureInfo}}, \code{\link[GiottoClass]{getFeatureMetadata}}, \code{\link[GiottoClass]{getGiottoImage}}, \code{\link[GiottoClass]{getMultiomics}}, \code{\link[GiottoClass]{getNearestNetwork}}, \code{\link[GiottoClass]{getPolygonInfo}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialEnrichment}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialGrid}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialLocations}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass]{getSpatialNetwork}}, \code{\link[GiottoClass:giotto-class]{giotto}}, \code{\link[GiottoClass:giottoImage-class]{giottoImage}}, \code{\link[GiottoClass:giottoLargeImage-class]{giottoLargeImage}}, \code{\link[GiottoClass]{giottoMasterToSuite}}, \code{\link[GiottoClass:giottoPoints-class]{giottoPoints}}, \code{\link[GiottoClass:giottoPolygon-class]{giottoPolygon}}, \code{\link[GiottoClass]{giottoToAnnData}}, \code{\link[GiottoClass]{giottoToSeurat}}, \code{\link[GiottoClass]{giottoToSeuratV4}}, \code{\link[GiottoClass]{giottoToSeuratV5}}, \code{\link[GiottoClass]{giottoToSpatialExperiment}}, \code{\link[GiottoClass]{hexVertices}}, \code{\link[GiottoClass]{hist}}, \code{\link[GiottoClass]{installGiottoEnvironment}}, \code{\link[GiottoClass:instructions-generic]{instructions}}, \code{\link[GiottoClass:instructions-generic]{instructions<-}}, \code{\link[GiottoClass]{joinGiottoObjects}}, \code{\link[GiottoClass]{loadGiotto}}, \code{\link[GiottoClass]{makePseudoVisium}}, \code{\link[GiottoClass]{objHistory}}, \code{\link[GiottoClass:objName-generic]{objName}}, \code{\link[GiottoClass:objName-generic]{objName<-}}, \code{\link[GiottoClass:generate_grid]{orthoGrid}}, \code{\link[GiottoClass]{overlapImagesToMatrix}}, \code{\link[GiottoClass]{overlapToMatrix}}, \code{\link[GiottoClass]{overlapToMatrixMultiPoly}}, \code{\link[GiottoClass:overlaps-generic]{overlaps}}, \code{\link[GiottoClass]{pDataDT}}, \code{\link[GiottoClass]{plotGiottoImage}}, \code{\link[GiottoClass]{polyStamp}}, \code{\link[GiottoClass:prov-generic]{prov}}, \code{\link[GiottoClass:prov-generic]{prov<-}}, \code{\link[GiottoClass]{readCellMetadata}}, \code{\link[GiottoClass]{readDimReducData}}, \code{\link[GiottoClass]{readExprData}}, \code{\link[GiottoClass]{readExprMatrix}}, \code{\link[GiottoClass]{readFeatData}}, \code{\link[GiottoClass]{readFeatMetadata}}, \code{\link[GiottoClass]{readGiottoInstructions}}, \code{\link[GiottoClass]{readNearestNetData}}, \code{\link[GiottoClass]{readPolygonData}}, \code{\link[GiottoClass]{readSpatEnrichData}}, \code{\link[GiottoClass]{readSpatLocsData}}, \code{\link[GiottoClass]{readSpatNetData}}, \code{\link[GiottoClass]{reconnectGiottoImage}}, \code{\link[GiottoClass]{rectVertices}}, \code{\link[GiottoClass]{removeCellAnnotation}}, \code{\link[GiottoClass]{removeFeatAnnotation}}, \code{\link[GiottoClass]{removeGiottoEnvironment}}, \code{\link[GiottoClass]{replaceGiottoInstructions}}, \code{\link[GiottoClass]{rescale}}, \code{\link[GiottoClass]{rescalePolygons}}, \code{\link[GiottoClass]{saveGiotto}}, \code{\link[GiottoClass]{setCellMetadata}}, \code{\link[GiottoClass]{setDimReduction}}, \code{\link[GiottoClass]{setExpression}}, \code{\link[GiottoClass]{setFeatureInfo}}, \code{\link[GiottoClass]{setFeatureMetadata}}, \code{\link[GiottoClass]{setGiotto}}, \code{\link[GiottoClass]{setGiottoImage}}, \code{\link[GiottoClass]{setMultiomics}}, \code{\link[GiottoClass]{setNearestNetwork}}, \code{\link[GiottoClass]{setPolygonInfo}}, \code{\link[GiottoClass]{setSpatialEnrichment}}, \code{\link[GiottoClass]{setSpatialGrid}}, \code{\link[GiottoClass]{setSpatialLocations}}, \code{\link[GiottoClass]{setSpatialNetwork}}, \code{\link[GiottoClass]{seuratToGiotto}}, \code{\link[GiottoClass]{seuratToGiottoV4}}, \code{\link[GiottoClass]{seuratToGiottoV5}}, \code{\link[GiottoClass]{showGiottoCellMetadata}}, \code{\link[GiottoClass]{showGiottoDimRed}}, \code{\link[GiottoClass]{showGiottoExpression}}, \code{\link[GiottoClass]{showGiottoFeatInfo}}, \code{\link[GiottoClass]{showGiottoFeatMetadata}}, \code{\link[GiottoClass]{showGiottoImageNames}}, \code{\link[GiottoClass]{showGiottoInstructions}}, \code{\link[GiottoClass]{showGiottoNearestNetworks}}, \code{\link[GiottoClass]{showGiottoSpatEnrichments}}, \code{\link[GiottoClass]{showGiottoSpatGrids}}, \code{\link[GiottoClass]{showGiottoSpatLocs}}, \code{\link[GiottoClass]{showGiottoSpatNetworks}}, \code{\link[GiottoClass]{showGiottoSpatialInfo}}, \code{\link[GiottoClass]{showProcessingSteps}}, \code{\link[GiottoClass]{smoothGiottoPolygons}}, \code{\link[GiottoClass:spatIDs-generic]{spatIDs}}, \code{\link[GiottoClass]{spatQueryGiottoPolygons}}, \code{\link[GiottoClass]{spatShift}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit}}, \code{\link[GiottoClass:spatUnit-generic]{spatUnit<-}}, \code{\link[GiottoClass]{spatialExperimentToGiotto}}, \code{\link[GiottoClass]{spin}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchFieldCoordinates}}, \code{\link[GiottoClass]{stitchGiottoLargeImage}}, \code{\link[GiottoClass]{subsetGiotto}}, \code{\link[GiottoClass]{subsetGiottoLocs}}, \code{\link[GiottoClass]{subsetGiottoLocsMulti}}, \code{\link[GiottoClass]{subsetGiottoLocsSubcellular}}, \code{\link[GiottoClass]{tessellate}}, \code{\link[GiottoClass:generate_grid]{triGrid}}, \code{\link[GiottoClass]{updateGiottoImage}}, \code{\link[GiottoClass]{updateGiottoImageMG}}, \code{\link[GiottoClass]{updateGiottoLargeImage}}, \code{\link[GiottoClass]{updateGiottoObject}}, \code{\link[GiottoClass]{updateGiottoPointsObject}}, \code{\link[GiottoClass]{updateGiottoPolygonObject}}, \code{\link[GiottoClass:wrap]{vect}}, \code{\link[GiottoClass]{wrap}}, \code{\link[GiottoClass]{writeGiottoLargeImage}}}
 
   \item{GiottoUtils}{\code{\link[GiottoUtils:pipe]{\%>\%}}, \code{\link[GiottoUtils]{getDistinctColors}}, \code{\link[GiottoUtils]{getRainbowColors}}}
 
diff --git a/tests/testthat/test-dbMatrix_filterGiotto.R b/tests/testthat/test-dbMatrix_filterGiotto.R
new file mode 100644
index 000000000..29979f555
--- /dev/null
+++ b/tests/testthat/test-dbMatrix_filterGiotto.R
@@ -0,0 +1,45 @@
+# silence deprecated internal functions
+rlang::local_options(lifecycle_verbosity = "quiet")
+
+# ---------------------------------------------------------------------------- #
+# Setup data
+visium = GiottoData::loadGiottoMini(dataset = "visium")
+dgc = getExpression(visium, output = "matrix")
+
+con = DBI::dbConnect(duckdb::duckdb(), ":memory:")
+
+dbsm = dbMatrix::dbMatrix(value = dgc, 
+                                con = con, 
+                                name = 'dgc', 
+                                class = "dbSparseMatrix",
+                                overwrite = TRUE)
+
+# Create exprObj with dbsm
+expObj_db = createExprObj(expression_data = dbsm, 
+                          expression_matrix_class = 'dbSparseMatrix', 
+                          name = 'raw')
+
+# Create giotto object
+gobject_db = suppressWarnings(createGiottoObject(expression = expObj_db))
+
+# ---------------------------------------------------------------------------- #
+# Perform filtering 
+visium_filtered = filterGiotto(visium, spat_unit = "cell", 
+                               feat_type = "rna",
+                               expression_values = "raw")
+
+gobject_db_filtered = filterGiotto(gobject_db, spat_unit = "cell",
+                                   feat_type = "rna",
+                                   expression_values = "raw")
+
+# Get filtered matrix
+dgc_visium = getExpression(visium_filtered, output = "matrix")
+mat_db = getExpression(gobject_db_filtered, output = "matrix")
+dgc_db = dbMatrix:::as_matrix(mat_db)
+
+# ---------------------------------------------------------------------------- #
+# Test filterGiotto() equivalence between dbMatrix and dgCMatrix
+
+test_that("dbMatrix equivalent to dgCMatrix after filterGiotto()", {
+  expect_equal(dgc_visium, dgc_db)
+})
diff --git a/tests/testthat/test-dbMatrix_libNorm.R b/tests/testthat/test-dbMatrix_libNorm.R
new file mode 100644
index 000000000..be575f17b
--- /dev/null
+++ b/tests/testthat/test-dbMatrix_libNorm.R
@@ -0,0 +1,64 @@
+# silence deprecated internal functions
+rlang::local_options(lifecycle_verbosity = "quiet")
+
+# ---------------------------------------------------------------------------- #
+# Setup data
+visium = GiottoData::loadGiottoMini(dataset = "visium")
+dgc = getExpression(visium, output = "matrix")
+
+con = DBI::dbConnect(duckdb::duckdb(), ":memory:")
+
+dbsm = dbMatrix::dbMatrix(value = dgc, 
+                                con = con, 
+                                name = 'dgc', 
+                                class = "dbSparseMatrix",
+                                overwrite = TRUE)
+
+# Create exprObj with dbsm
+expObj_db = createExprObj(expression_data = dbsm, 
+                          expression_matrix_class = 'dbSparseMatrix', 
+                          name = 'raw')
+
+# Create giotto object
+gobject_db = suppressWarnings(createGiottoObject(expression = expObj_db))
+
+# ---------------------------------------------------------------------------- #
+# Perform filtering 
+visium_filtered = filterGiotto(visium, spat_unit = "cell", 
+                               feat_type = "rna",
+                               expression_values = "raw")
+
+gobject_db_filtered = filterGiotto(gobject_db, spat_unit = "cell",
+                                   feat_type = "rna",
+                                   expression_values = "raw")
+
+# ---------------------------------------------------------------------------- #
+# Perform library normalization and scaling
+visium_filtered = normalizeGiotto(gobject = visium_filtered,
+                                  spat_unit = 'cell',
+                                  feat_type = 'rna',
+                                  expression_values = 'raw',
+                                  library_size_norm = TRUE,
+                                  log_norm = FALSE,
+                                  scale_feats = FALSE,
+                                  scale_cells = FALSE)
+
+
+gobject_db_filtered = normalizeGiotto(gobject = gobject_db_filtered,
+                                      spat_unit = 'cell',
+                                      feat_type = 'rna',
+                                      expression_values = 'raw',
+                                      library_size_norm = TRUE,
+                                      log_norm = FALSE,
+                                      scale_feats = FALSE,
+                                      scale_cells = FALSE)
+# Get normalized matrix
+dgc_visium = getExpression(visium_filtered, output = "matrix", values = "normalized")
+mat_db = getExpression(gobject_db_filtered, output = "matrix", values = "normalized")
+dgc_db = dbMatrix:::as_matrix(mat_db)
+
+# ---------------------------------------------------------------------------- #
+# Test normalizeGiotto() equivalence between dbMatrix and dgCMatrix
+test_that("dbMatrix equivalent to dgCMatrix after normalizeGiotto(library_size_norm = TRUE)", {
+  expect_equal(dgc_visium, dgc_db)
+})
\ No newline at end of file
diff --git a/tests/testthat/test-dbMatrix_logNorm.R b/tests/testthat/test-dbMatrix_logNorm.R
new file mode 100644
index 000000000..1731e634f
--- /dev/null
+++ b/tests/testthat/test-dbMatrix_logNorm.R
@@ -0,0 +1,64 @@
+# silence deprecated internal functions
+rlang::local_options(lifecycle_verbosity = "quiet")
+
+# ---------------------------------------------------------------------------- #
+# Setup data
+visium = GiottoData::loadGiottoMini(dataset = "visium")
+dgc = getExpression(visium, output = "matrix")
+
+con = DBI::dbConnect(duckdb::duckdb(), ":memory:")
+
+dbsm = dbMatrix::dbMatrix(value = dgc, 
+                                con = con, 
+                                name = 'dgc', 
+                                class = "dbSparseMatrix",
+                                overwrite = TRUE)
+
+# Create exprObj with dbsm
+expObj_db = createExprObj(expression_data = dbsm, 
+                          expression_matrix_class = 'dbSparseMatrix', 
+                          name = 'raw')
+
+# Create giotto object
+gobject_db = suppressWarnings(createGiottoObject(expression = expObj_db))
+
+# ---------------------------------------------------------------------------- #
+# Perform filtering 
+visium_filtered = filterGiotto(visium, spat_unit = "cell", 
+                               feat_type = "rna",
+                               expression_values = "raw")
+
+gobject_db_filtered = filterGiotto(gobject_db, spat_unit = "cell",
+                                   feat_type = "rna",
+                                   expression_values = "raw")
+
+# ---------------------------------------------------------------------------- #
+# Perform library normalization and scaling
+visium_filtered = normalizeGiotto(gobject = visium_filtered,
+                                  spat_unit = 'cell',
+                                  feat_type = 'rna',
+                                  expression_values = 'raw',
+                                  library_size_norm = FALSE,
+                                  log_norm = TRUE,
+                                  scale_feats = FALSE,
+                                  scale_cells = FALSE)
+
+
+gobject_db_filtered = normalizeGiotto(gobject = gobject_db_filtered,
+                                      spat_unit = 'cell',
+                                      feat_type = 'rna',
+                                      expression_values = 'raw',
+                                      library_size_norm = FALSE,
+                                      log_norm = TRUE,
+                                      scale_feats = FALSE,
+                                      scale_cells = FALSE)
+# Get normalized matrix
+dgc_visium = getExpression(visium_filtered, output = "matrix", values = "normalized")
+mat_db = getExpression(gobject_db_filtered, output = "matrix", values = "normalized")
+dgc_db = dbMatrix:::as_matrix(mat_db)
+
+# ---------------------------------------------------------------------------- #
+# Test normalizeGiotto() equivalence between dbMatrix and dgCMatrix
+test_that("dbMatrix equivalent to dgCMatrix after normalizeGiotto(log_norm=TRUE)", {
+  expect_equal(dgc_visium, dgc_db)
+})
\ No newline at end of file
diff --git a/tests/testthat/test-dbMatrix_scale.R b/tests/testthat/test-dbMatrix_scale.R
new file mode 100644
index 000000000..b28504d35
--- /dev/null
+++ b/tests/testthat/test-dbMatrix_scale.R
@@ -0,0 +1,64 @@
+# silence deprecated internal functions
+rlang::local_options(lifecycle_verbosity = "quiet")
+
+# ---------------------------------------------------------------------------- #
+# Setup data
+visium = GiottoData::loadGiottoMini(dataset = "visium")
+dgc = getExpression(visium, output = "matrix")
+
+con = DBI::dbConnect(duckdb::duckdb(), ":memory:")
+
+dbsm = dbMatrix::dbMatrix(value = dgc, 
+                                con = con, 
+                                name = 'dgc', 
+                                class = "dbSparseMatrix",
+                                overwrite = TRUE)
+
+# Create exprObj with dbsm
+expObj_db = createExprObj(expression_data = dbsm, 
+                          expression_matrix_class = 'dbSparseMatrix', 
+                          name = 'raw')
+
+# Create giotto object
+gobject_db = suppressWarnings(createGiottoObject(expression = expObj_db))
+
+# ---------------------------------------------------------------------------- #
+# Perform filtering 
+visium_filtered = filterGiotto(visium, spat_unit = "cell", 
+                               feat_type = "rna",
+                               expression_values = "raw")
+
+gobject_db_filtered = filterGiotto(gobject_db, spat_unit = "cell",
+                                   feat_type = "rna",
+                                   expression_values = "raw")
+
+# ---------------------------------------------------------------------------- #
+# Perform library normalization and scaling
+visium_filtered = normalizeGiotto(gobject = visium_filtered,
+                                  spat_unit = 'cell',
+                                  feat_type = 'rna',
+                                  expression_values = 'raw',
+                                  library_size_norm = FALSE,
+                                  log_norm = FALSE,
+                                  scale_feats = TRUE,
+                                  scale_cells = TRUE)
+
+
+gobject_db_filtered = normalizeGiotto(gobject = gobject_db_filtered,
+                                      spat_unit = 'cell',
+                                      feat_type = 'rna',
+                                      expression_values = 'raw',
+                                      library_size_norm = FALSE,
+                                      log_norm = FALSE,
+                                      scale_feats = TRUE,
+                                      scale_cells = TRUE)
+# Get normalized matrix
+dgc_visium = getExpression(visium_filtered, output = "matrix", values = "scaled") |> as.matrix()
+mat_db = getExpression(gobject_db_filtered, output = "matrix", values = "scaled")
+dgc_db = dbMatrix:::as_matrix(mat_db)
+
+# ---------------------------------------------------------------------------- #
+# Test normalizeGiotto() equivalence between dbMatrix and dgCMatrix
+test_that("dbMatrix equivalent to dgCMatrix after normalizeGiotto(scale_feats=T,scale=cells=T)", {
+  expect_equal(dgc_visium, dgc_db)
+})
\ No newline at end of file
diff --git a/vignettes/dbMatrix.Rmd b/vignettes/dbMatrix.Rmd
new file mode 100644
index 000000000..588737d97
--- /dev/null
+++ b/vignettes/dbMatrix.Rmd
@@ -0,0 +1,93 @@
+---
+title: "Using dbMatrix with Giotto"
+output: rmarkdown::html_vignette
+vignette: >
+  %\VignetteIndexEntry{Using dbMatrix with Giotto}
+  %\VignetteEngine{knitr::rmarkdown}
+  %\VignetteEncoding{UTF-8}
+---
+
+```{r, include = FALSE}
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>"
+)
+```
+
+# Introduction
+This vignette demonstrates how to use a [`dbMatrix`](https://github.com/drieslab/dbMatrix) within a Giotto Object. The `dbMatrix` is a database-backed matrix that can be used to store large matrices in a database. This allows for efficient storage and retrieval of large matrices and enables efficiently working with larger-than-memory cell count matrices.
+
+# 1. Set up Giotto
+
+```{r, eval=FALSE}
+# Ensure Giotto Suite is installed.
+if(!"Giotto" %in% installed.packages()) {
+  devtools::install_github("drieslab/Giotto@suite")
+}
+
+# Ensure GiottoData, a small, helper module for tutorials, is installed.
+if(!"GiottoData" %in% installed.packages()) {
+  devtools::install_github("drieslab/GiottoData")
+}
+
+library(Giotto)
+library(GiottoData)
+
+# Ensure the Python environment for Giotto has been installed.
+genv_exists = checkGiottoEnvironment()
+if(!genv_exists){
+  # The following command need only be run once to install the Giotto environment.
+  installGiottoEnvironment()
+}
+```
+
+# 2. Create Giotto object with `dbMatrix`                                   
+
+```{r}
+# Get test dataset from Giotto Data package
+visium = GiottoData::loadGiottoMini(dataset = "visium")
+
+# Extract the cell expression matrix as a test dataset
+dgc = getExpression(visium, output = "matrix")
+
+# Create a DBI connection object
+con = DBI::dbConnect(duckb::duckdb(), ":memory:")
+
+# Create a dbSparseMatrix using the dbMatrix constructor function
+dbsm = dbMatrix::dbMatrix(value = dgc, 
+                          con = con, 
+                          name = 'dgc', 
+                          class = "dbSparseMatrix",
+                          overwrite = TRUE)
+
+# Create Giotto exprObj with the dbMatrix
+expObj_db = createExprObj(expression_data = dbsm, 
+                          expression_matrix_class = 'dbSparseMatrix', 
+                          name = 'raw')
+
+# Create the Giotto object consisting of only the cell count matrix
+gobject_db = createGiottoObject(expression = expObj_db)
+```
+
+# 3. Preprocess Giotto object with `dbMatrix`
+```{r}
+# Perform filtering 
+gobject_db_filtered = filterGiotto(gobject_db, spat_unit = "cell",
+                                   feat_type = "rna",
+                                   expression_values = "raw")
+
+# Perform library normalization and scaling
+gobject_db_filtered = normalizeGiotto(gobject = gobject_db_filtered,
+                                      spat_unit = 'cell',
+                                      feat_type = 'rna',
+                                      expression_values = 'raw',
+                                      library_size_norm = FALSE,
+                                      log_norm = FALSE,
+                                      scale_feats = TRUE,
+                                      scale_cells = TRUE)
+```
+
+
+```{r}
+sessionInfo()
+```
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