scDotPlot v0.99.4

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: scDotPlot
 Title: Cluster a Single-cell RNA-seq Dot Plot
-Version: 0.99.3
+Version: 0.99.4
 Authors@R: c(
     person(given = c("Benjamin", "I"),
            family = "Laufer",

NEWS.md

@@ -1,3 +1,7 @@
+# scDotPlot 0.99.4
+
+* Addition of seperate clusterRows and clusterColumns arguments 
+
 # scDotPlot 0.99.3
 
 * Bug fix for flipPlot argument as well as addition of font customization arguments and graphical unit tests

R/scDotPlot.R

@@ -20,7 +20,11 @@
 #' @param NumDetectedThreshold Numeric specifying threshold for fraction of cells,
 #'  where values below AverageThreshold and NumDetectedThreshold are transparent
 #' @param cluster Logical specifying whether to perform hierarchical clustering
-#'  analysis
+#'  analysis on both the rows and columns
+#' @param clusterRows Logical specifying whether to perform hierarchical clustering
+#'  analysis on the rows
+#' @param clusterColumns Logical specifying whether to perform hierarchical clustering
+#'  analysis on the columns
 #' @param groupAnno Cell annotations that are stored as names of columns
 #'  in colData of sce with annotations
 #' @param featureAnno Feature annotations that are stored as names of rows
@@ -93,6 +97,8 @@ scDotPlot.SingleCellExperiment <- function(object,
                                            swap_rownames = NULL,
                                            scale = FALSE,
                                            cluster = TRUE,
+                                           clusterRows = cluster,
+                                           clusterColumns = cluster,
                                            AverageThreshold = ifelse(scale == FALSE, 0, -Inf),
                                            NumDetectedThreshold = 0.01,
                                            groupAnno = FALSE,
@@ -168,6 +174,8 @@ scDotPlot.SingleCellExperiment <- function(object,
                           features = features,
                           scale = scale,
                           cluster = cluster,
+                          clusterRows = clusterRows,
+                          clusterColumns = clusterColumns,
                           AverageThreshold = AverageThreshold,
                           NumDetectedThreshold = NumDetectedThreshold,
                           groupAnno = groupAnno,
@@ -204,6 +212,8 @@ scDotPlot.Seurat <- function(object,
                              swap_rownames = NULL,
                              scale = FALSE,
                              cluster = TRUE,
+                             clusterRows = cluster,
+                             clusterColumns = cluster,
                              AverageThreshold = ifelse(scale == FALSE, 0, -Inf),
                              NumDetectedThreshold = 0.01,
                              groupAnno = FALSE,
@@ -271,6 +281,8 @@ scDotPlot.Seurat <- function(object,
                           features = features,
                           scale = scale,
                           cluster = cluster,
+                          clusterRows = clusterRows,
+                          clusterColumns = clusterColumns,
                           AverageThreshold = AverageThreshold,
                           NumDetectedThreshold = NumDetectedThreshold,
                           groupAnno = groupAnno,
@@ -301,6 +313,8 @@ scDotPlot.default <- function(object,
                               swap_rownames = NULL,
                               scale = FALSE,
                               cluster = TRUE,
+                              clusterRows = cluster,
+                              clusterColumns = cluster,
                               AverageThreshold = ifelse(scale == FALSE, 0, -Inf),
                               NumDetectedThreshold = 0.01,
                               groupAnno = FALSE,
@@ -334,7 +348,8 @@ scDotPlot.default <- function(object,
 
     if(any(cluster == TRUE, !is.null(groupAnno), !is.null(featureAnno))){
         dotPlot %>%
-            .annotateDotPlot(cluster = cluster,
+            .annotateDotPlot(clusterRows = clusterRows,
+                             clusterColumns = clusterColumns,
                              groupAnno = groupAnno,
                              featureAnno = featureAnno,
                              treeWidth = treeWidth,

R/utils.R

@@ -14,7 +14,8 @@
 #' @keywords internal
 #'
 .annotateDotPlot <- function(dotPlot,
-                             cluster = TRUE,
+                             clusterRows = TRUE,
+                             clusterColumns = TRUE,
                              groupAnno = FALSE,
                              featureAnno = FALSE,
                              treeWidth = 0.1,
@@ -95,9 +96,13 @@
         purrr::reduce(if(flipPlot == FALSE){rowLabels}else{colLabels},
                       ~ aplot::insert_left(.x, .y, width = annoWidth),
                       .init = .) %>%
-        {if(cluster == TRUE){
-            aplot::insert_left(., treeRow, width = treeWidth) %>%
-                aplot::insert_top(treeCol, height = treeHeight)
+        {if(clusterRows == TRUE){
+            aplot::insert_left(., treeRow, width = treeWidth)
+        }else{
+            .
+        }} %>%
+        {if(clusterColumns == TRUE){
+            aplot::insert_top(., treeCol, height = treeHeight)
         }else{
             .
         }}
@@ -140,7 +145,7 @@
 
     (plotData %>%
             dplyr::mutate(alpha = dplyr::case_when(Average > !!AverageThreshold &
-                                                       NumDetected > NumDetectedThreshold ~ 1,
+                                                       NumDetected > !!NumDetectedThreshold ~ 1,
                                                    .default = 0.1)) %>%
             {if(flipPlot == FALSE){
                 ggplot2::ggplot(., ggplot2::aes(x = !!rlang::sym(group),

vignettes/scDotPlot.Rmd

@@ -88,7 +88,7 @@ rowData(sce)$Marker <- features[match(rownames(sce), features)] |>
 
 Finally, we create the plot. The `group` arguments utilize the colData, while the `features` arguments use the rowData. The `paletteList` argument can be used to manually specify the colors for the annotations specified through `groupAnno` and `featureAnno`. The clustering of the columns shows that cell the cell sub-types cluster by cell-type, while the clustering of the rows shows that most of the markers clusters by their cell type.
 
-```{r Plot SingleCellExperiment logcounts, fig.width=12, fig.height=12, dpi=50}
+```{r scePlot1, fig.cap = "scDotPlot of SingleCellExperiment logcounts", fig.width=12, fig.height=12, dpi=50}
 library(scDotPlot)
 library(ggsci)
 
@@ -107,7 +107,7 @@ sce |>
 
 Plotting by Z-score through `scale = TRUE` improves the clustering result for the rows.
 
-```{r Plot SingleCellExperiment Z, fig.width=12, fig.height=12, dpi=50}
+```{r scePlot2, fig.cap = "scDotPlot of SingleCellExperiment Z-scores", fig.width=12, fig.height=12, dpi=50}
 sce |>
     scDotPlot(scale = TRUE,
               features = features,
@@ -150,9 +150,9 @@ features <- features |>
 
 ## Plot logcounts
 
-Plotting a Seurat object is similair to plotting a SingleCellExperiment object. 
+Plotting a Seurat object is similar to plotting a SingleCellExperiment object. 
 
-```{r Plot Seurat logcounts, fig.width=4, fig.height=5, out.width="50%", out.height="50%", dpi=50}
+```{r SeuratPlot1, fig.cap = "scDotPlot of Seurat logcounts", fig.width=4, fig.height=5, out.width="75%", out.height="75%", dpi=50}
 pbmc_small |>
     scDotPlot(features = features,
               group = "RNA_snn_res.1",
@@ -168,7 +168,7 @@ pbmc_small |>
 
 Again, we see that plotting by Z-score improves the clustering result for the rows.
 
-```{r Plot Seurat Z, fig.width=4, fig.height=5, out.width="50%", out.height="50%", dpi=50}
+```{r SeuratPlot2, fig.cap = "scDotPlot of Seurat Z-scores", fig.width=4, fig.height=5, out.width="75%", out.height="75%", dpi=50}
 pbmc_small |>
     scDotPlot(scale = TRUE,
               features = features,