update

R/data.R

@@ -1,5 +1,6 @@
 #' Preprocessed MERFISH dataset of the mouse preoptic area for a bregma -0.29 slice 
-#' from a female naive animal (Animal ID = 1).
+#' from a female naive animal (Animal ID = 1, Animal Sex = "Female", 
+#' # Behavior = "Naive", Bregma = "-0.29").
 #'
 #' @format \code{SpatialExperiment} object where \code{assay} slot contains genes-by-cells 
 #' matrix with preprocessed gene expression (total RNA counts per cell divided by 

R/main.R

@@ -57,6 +57,7 @@
 #' @importFrom sf st_make_grid st_coordinates st_centroid st_as_sf st_intersects
 #' @importFrom BiocParallel MulticoreParam bpstart bplapply bpstop
 #' @importFrom Matrix rowMeans rowSums
+#' @importFrom methods as
 #' 
 #' @export
 #' 
@@ -104,7 +105,7 @@ rasterizeMatrix <- function(data, pos, bbox, resolution = 100, square = TRUE, fu
   ## check if cellnames in data and pos are identical if they are provided in colnames and rownames respectively
   stopifnot((is.null(colnames(data)) | is.null(rownames(pos))) | identical(colnames(data), rownames(pos)))|
 
-    ## check to see if bbox input is of class numeric or bbox, if numeric, convert to st_bbox
+  ## check to see if bbox input is of class numeric or bbox, if numeric, convert to st_bbox
   stopifnot("bbox must be of class 'numeric' or 'bbox' (as generated by sf::st_bbox)" = class(bbox) %in% c('numeric', 'bbox'))
   if (class(bbox)=='numeric') {
     stopifnot("Bounding box for rasterization must be defined by a numeric vector of length four, with xmin, ymin, xmax and ymax values or object of class bbox (as generated by sf::st_bbox)" = length(bbox)==4)
@@ -270,13 +271,15 @@ rasterizeMatrix <- function(data, pos, bbox, resolution = 100, square = TRUE, fu
 #' assayNames(merfish_mousePOA)
 #' 
 #' # rasterize a single SpatialExperiment object
-#' # make sure to specify the assay_name argument when the input SpatialExperiment object has multiple assay names (assay_name is used here as an example)
+#' # make sure to specify the assay_name argument when the input SpatialExperiment 
+#' # object has multiple assay names (assay_name is used here as an example)
 #' out <- rasterizeGeneExpression(merfish_mousePOA, assay_name = "volnorm", fun = "mean")
 #' 
 #' # rasterize a single SpatialExperiment object with user-defined resolution and hexagonal pixels
 #' out <- rasterizeGeneExpression(merfish_mousePOA, assay_name = "volnorm", resolution = 200, square = FALSE, fun = "mean")
 #' 
-#' # rasterize a list of SpatialExperiment objects (in this case, permutated datasets with 3 different rotations)
+#' # rasterize a list of SpatialExperiment objects (in this case, permutated datasets 
+#' # with 3 different rotations)
 #' spe_list <- permutateByRotation(merfish_mousePOA, n_perm = 3)
 #' out_list <- rasterizeGeneExpression(spe_list, assay_name = "volnorm", resolution = 100, square = TRUE, fun = "mean")
 #' 
@@ -437,7 +440,8 @@ rasterizeGeneExpression <- function(input, assay_name = NULL, resolution = 100,
 #' 
 #' data("merfish_mousePOA")
 #' 
-#' # check assay names for this particular SpatialExperiment object (you can see that cell-type labels are stored in the "celltype" column)
+#' # check assay names for this particular SpatialExperiment object (you can see 
+#' # that cell-type labels are stored in the "celltype" column)
 #' head(colData(merfish_mousePOA))
 #' 
 #' # rasterize a single SpatialExperiment object
@@ -447,7 +451,8 @@ rasterizeGeneExpression <- function(input, assay_name = NULL, resolution = 100,
 #' # rasterize a single SpatialExperiment object with user-defined resolution and hexagonal pixels
 #' out <- rasterizeCellType(merfish_mousePOA, col_name = "celltype", resolution = 200, square = FALSE, fun = "sum")
 #' 
-#' # rasterize a list of SpatialExperiment objects (in this case, permutated datasets with 3 different rotations)
+#' # rasterize a list of SpatialExperiment objects (in this case, permutated datasets 
+#' # with 3 different rotations)
 #' spe_list <- permutateByRotation(merfish_mousePOA, n_perm = 3)
 #' out_list <- rasterizeCellType(spe_list, col_name = "celltype", resolution = 100, square = TRUE, fun = "sum")
 #' 
@@ -594,7 +599,8 @@ rasterizeCellType <- function(input, col_name, resolution = 100, square = TRUE,
 #' data("merfish_mousePOA")
 #' 
 #' # create a list of 3 permutated datasets rotated at 0 (original), 120, and 240 degrees
-#' # this output can directly be fed into rasterizeGeneExpression or rasterizeCellType functions to rasterize all 3 permutations at once with the same pixel coordinates
+#' # this output can directly be fed into rasterizeGeneExpression or rasterizeCellType 
+#' # functions to rasterize all 3 permutations at once with the same pixel coordinates
 #' spe_list <- permutateByRotation(merfish_mousePOA, n_perm = 3)
 #' 
 #' # create a list of 5 permutated datasets rotated at 0 (original), 72, 144, 216, 288 degrees
@@ -754,7 +760,9 @@ permutateByRotation <- function(input, n_perm = 1, verbose = FALSE) {
 #' # rasterize gene expression
 #' out <- rasterizeGeneExpression(merfish_mousePOA, assay_name = "volnorm", fun = "mean")
 #' 
-#' # plot total rasterized gene expression per pixel (there is only one assay_name in out and default for feature_name argument is "sum"; therefore, these arguments are not specified)
+#' # plot total rasterized gene expression per pixel (there is only one assay_name 
+#' # in out and default for feature_name argument is "sum"; therefore, these arguments 
+#' # are not specified)
 #' plotRaster(out, name = "total rasterized gexp")
 #' 
 #' # plot rasterized expression of a specific gene/feature per pixel
@@ -763,8 +771,10 @@ permutateByRotation <- function(input, n_perm = 1, verbose = FALSE) {
 #' # rasterize cell-type labels with user-defined resolution and hexagonal pixels
 #' out <- rasterizeCellType(merfish_mousePOA, col_name = "celltype", resolution = 50, square = FALSE, fun = "sum")
 #' 
-#' # plot total cell counts per pixel (there is only one assay_name in out and default for feature_name argument is "sum"; therefore, these arguments are not specified)
-#' # here, let's use additional parameters for ggplot2::scale_fill_viridis_c so that it would have a different color scheme from gene expression plots
+#' # plot total cell counts per pixel (there is only one assay_name in out and default 
+#' # for feature_name argument is "sum"; therefore, these arguments are not specified)
+#' # here, let's use additional parameters for ggplot2::scale_fill_viridis_c so 
+#' # that it would have a different color scheme from gene expression plots
 #' plotRaster(out, name = "total cell counts", option = "inferno")
 #' 
 #' # plot specific cell type's cell counts per pixel
@@ -785,27 +795,27 @@ plotRaster <- function(input, assay_name = NULL, feature_name = "sum", factor_le
   df_sf <- sf::st_sf(geometry = colData(input)$geometry, row.names = rownames(colData(input)))
   # add pixel values
   if (feature_name == "sum") {
-    df_sf <- cbind(df_sf, fill = colSums(mat))
+    df_sf <- cbind(df_sf, fill_var = colSums(mat))
   } else if (feature_name == "mean") {
-    df_sf <- cbind(df_sf, fill = colMeans(mat))
+    df_sf <- cbind(df_sf, fill_var = colMeans(mat))
   } else {
     stopifnot(is.character(feature_name))
     stopifnot("feature_name does not exist in the input SpatialExperiment object's assay slot" = feature_name %in% rownames(mat))
-    df_sf <- cbind(df_sf, fill = mat[feature_name,])
+    df_sf <- cbind(df_sf, fill_var = mat[feature_name,])
   }
 
   if (is.null(factor_levels)) {
     plt <- ggplot2::ggplot() +
       ggplot2::coord_fixed() +
-      ggplot2::geom_sf(data = df_sf, ggplot2::aes(fill = fill)) +
+      ggplot2::geom_sf(data = df_sf, ggplot2::aes(fill = fill_var)) +
       ggplot2::scale_fill_viridis_c(...) +
       ggplot2::theme_bw() +
       ggplot2::theme(panel.grid = ggplot2::element_blank())
   } else {
-    df_sf$fill <- factor(df_sf$fill, levels = factor_levels)
+    df_sf$fill_var <- factor(df_sf$fill_var, levels = factor_levels)
     plt <- ggplot2::ggplot() +
       ggplot2::coord_fixed() +
-      ggplot2::geom_sf(data = df_sf, ggplot2::aes(fill = fill)) +
+      ggplot2::geom_sf(data = df_sf, ggplot2::aes(fill = fill_var)) +
       ggplot2::scale_fill_viridis_d(...) +
       ggplot2::theme_bw() +
       ggplot2::theme(panel.grid = ggplot2::element_blank())

vignettes/formatting-SpatialExperiment-for-SEraster.Rmd

@@ -27,6 +27,7 @@ In the original work, [Moffitt J. and Bambah-Mukku D. et al. (2018), "Molecular,
 library(SpatialExperiment)
 library(Matrix)
 library(ggplot2)
+library(ggrastr)
 ```
 
 ## Load the subsetted dataset
@@ -88,7 +89,7 @@ df_plt <- data.frame(pos, total_gexp = colSums(mat))
 
 ggplot(df_plt, aes(x = x, y = y, color = total_gexp)) +
   coord_fixed() +
-  geom_point(size = 1.5, stroke = 0) +
+  rasterise(geom_point(size = 1.5, stroke = 0)) +
   scale_color_viridis_c(name = "total gene expression") +
   theme_bw() +
   theme(panel.grid = element_blank(),
@@ -104,7 +105,7 @@ df_plt <- data.frame(pos, celltype = meta$celltype)
 
 ggplot(df_plt, aes(x = x, y = y, color = celltype)) +
   coord_fixed() +
-  geom_point(size = 1.5, stroke = 0) +
+  rasterise(geom_point(size = 1.5, stroke = 0)) +
   theme_bw() +
   theme(panel.grid = element_blank(),
         axis.title = element_blank(),

vignettes/getting-started-with-SEraster.Rmd

@@ -41,6 +41,7 @@ library(SpatialExperiment)
 library(nnSVG)
 library(CooccurrenceAffinity)
 library(ggplot2)
+library(ggrastr)
 ```
 
 ## Load example dataset
@@ -63,7 +64,7 @@ df <- data.frame(spatialCoords(merfish_mousePOA), celltype = colData(merfish_mou
 
 ggplot(df, aes(x = x, y = y, col = celltype)) +
   coord_fixed() +
-  geom_point(size = 1.5) +
+  rasterise(geom_point(size = 1.5)) +
   guides(col = guide_legend(override.aes = list(size = 3))) +
   labs(x = "x (μm)",
        y = "y (μm)",