diff --git a/R/plot_dotplot.R b/R/plot_dotplot.R
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+#' @name dotPlot
+#' @title Create a dotplot
+#' @description Visualize feature expression statistics applied across
+#'   clusters/groupings of cells. The default behavior is dot size scaled by
+#'   what percentage of cells within a particular cluster express the feature,
+#'   and dot color scaled by mean expression of that feature within the cluster.
+#' @inheritParams data_access_params
+#' @inheritParams plot_params
+#' @inheritParams plot_output_params
+#' @param
+#' @param dot_size,dot_color summary function e.g. `sum`, `mean`, `var`, or
+#'   other custom function. The default for `dot_size` finds the percentage of
+#'   cells of a particular cluster that do not have an expression level of 0.
+#' @param dot_size_threshold numeric. The minimal value at which a dot is no
+#'   longer drawn.
+#' @param feats character vector. Features to use
+#' @param cluster_column character. Clusterings column to use (usually in cell
+#'   metadata)
+#' @param cluster_custom_order character vector. Specific cluster order to use
+#' @param dot_scale numeric. Controls size of dots
+#' @param dot_color_gradient hex codes or palette name. Color gradient to use.
+#' @param gradient_limits numeric vector of length 2. Set minmax value mappings
+#'   for color gradient
+#' @param expression_values character. Expression values to use.
+#' @param title character. title for plot
+#' @param theme_param list of additional params passed to `ggplot2::theme()`
+#' @examples
+#' g <- GiottoData::loadGiottoMini("visium")
+#' feats <- head(featIDs(g), 20)
+#' dotPlot(g, cluster_column = "leiden_clus", feats = feats)
+#' dotPlot(g,
+#'     cluster_column = "leiden_clus",
+#'     feats = feats,
+#'     dot_size = mean,
+#'     dot_color = var
+#' )
+#' @export
+dotPlot <- function(
+        gobject,
+        spat_unit = NULL,
+        feat_type = NULL,
+        feats,
+        cluster_column,
+        cluster_custom_order = NULL,
+        dot_size = function(x) mean(x != 0) * 100,
+        dot_size_threshold = 0,
+        dot_scale = 6,
+        dot_color = mean,
+        dot_color_gradient = NULL,
+        gradient_midpoint = NULL,
+        gradient_style = "sequential",
+        gradient_limits = NULL,
+        expression_values = c(
+            "normalized",
+            "scaled",
+            "custom"
+        ),
+        title = NULL,
+        show_legend = TRUE,
+        legend_text = 10,
+        legend_symbol_size = 2,
+        background_color = "white",
+        axis_text = 10,
+        axis_title = 9,
+        theme_param = list(),
+        show_plot = NULL,
+        return_plot = NULL,
+        save_plot = NULL,
+        save_param = list(),
+        default_save_name = "dotPlot"
+) {
+    checkmate::assert_character(cluster_column, len = 1L)
+    checkmate::assert_character(feats)
+    checkmate::assert_class(gobject, "giotto")
+    if (!is.null(gradient_limits)) {
+        checkmate::assert_numeric(gradient_limits, len = 2L)
+    }
+
+
+    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)
+
+    expression_values <- match.arg(
+        expression_values,
+        unique(c("normalized", "scaled", "custom", expression_values))
+    )
+
+    clus <- spatValues(gobject, spat_unit = spat_unit, feat_type = feat_type,
+                       feats = cluster_column, verbose = FALSE)
+
+    expr <- spatValues(gobject, spat_unit = spat_unit, feat_type = feat_type,
+                       feats = feats, expression_values = expression_values,
+                       verbose = FALSE)
+    ann_dt <- clus[expr, on = "cell_ID"]
+
+    dsize <- ann_dt[, lapply(.SD, dot_size), .SDcols = feats, by = cluster_column]
+    dcol <- ann_dt[, lapply(.SD, dot_color), .SDcols = feats, by = cluster_column]
+
+    dsize <- data.table::melt(dsize,
+        id.vars = cluster_column,
+        measure.vars = feats,
+        value.name = "size",
+        variable.name = "feat"
+    )
+    dcol <- data.table::melt(dcol,
+        id.vars = cluster_column,
+        measure.vars = feats,
+        value.name = "color",
+        variable.name = "feat"
+    )
+    plot_dt <- dsize[dcol, on = c(cluster_column, "feat")]
+    data.table::setnames(plot_dt, old = cluster_column, new = "cluster")
+
+    ## dot size cutoff ##
+    plot_dt <- plot_dt[size > dot_size_threshold,]
+
+    ## set cluster order ##
+    if (is.null(cluster_custom_order)) {
+        plot_dt[, cluster := factor(cluster, levels = mixedsort(unique(cluster)))]
+    } else {
+        plot_dt[, cluster := factor(cluster, levels = cluster_custom_order)]
+    }
+
+    # apply limits
+    if (!is.null(gradient_limits)) {
+        plot_dt[, color := scales::oob_squish(color, gradient_limits)]
+    }
+
+    pl <- ggplot2::ggplot() +
+        ggplot2::geom_point(
+            data = plot_dt,
+            ggplot2::aes(x = cluster, y = feat, color = color, size = size)
+        )
+
+    # apply color gradient
+    if (is.null(gradient_midpoint)) {
+        gradient_midpoint <-
+            stats::median(plot_dt$color)
+    }
+    pl <- pl + set_default_color_continuous_cell(
+        colors = dot_color_gradient,
+        instrs = instructions(gobject),
+        midpoint = gradient_midpoint,
+        style = gradient_style,
+        type = "color"
+    )
+
+    # size scaling
+    pl <- pl + scale_size_continuous(range = c(1, dot_scale))
+
+    ## theme ##
+    gg_theme_args <- c(
+        theme_param,
+        legend_text = legend_text,
+        axis_title = axis_title,
+        axis_text = axis_text,
+        background_color = background_color,
+        axis.ticks = element_blank(),
+        axis_text_y_angle = 0
+    )
+    pl <- pl + do.call(.gg_theme, args = gg_theme_args)
+
+
+    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
+    )
+}
+
+
+
+