@@ -16,13 +16,12 @@

 #' * `counts`: `matrix` with generated gene counts.

 #'

 #' @examples

-#' spe <- mockSVG(10, tot_genes = 200, de_genes = 20, return_SPE = TRUE)

+#' spe <- mockSVG(size = 20, tot_genes = 3, de_genes = 1, return_SPE = TRUE)

 #' spe

 #'

 #' @export

 #' @importFrom stats rnbinom runif

-mockSVG <- function(size = 10, tot_genes = 1000, de_genes = 100,

-                    return_SPE = FALSE) {

+mockSVG <- function(size, tot_genes, de_genes, return_SPE = FALSE) {

     n_cells <- size * size

     coordinates <- data.frame(

         x = rep(seq.int(size), size),

@@ -49,8 +49,7 @@ mockSVG <- function(size = 10, tot_genes = 1000, de_genes = 100,

     if (return_SPE) {

         out <- SpatialExperiment::SpatialExperiment(

             assays = list(counts = counts),

-            spatialData = S4Vectors::DataFrame(coordinates),

-            spatialCoordsNames = c("x", "y")

+            spatialCoords = as.matrix(coordinates)

         )

     }

     out

@@ -49,7 +49,7 @@ mockSVG <- function(size = 10, tot_genes = 1000, de_genes = 100,

     if (return_SPE) {

         out <- SpatialExperiment::SpatialExperiment(

             assays = list(counts = counts),

-            spatialData = coordinates,

+            spatialData = S4Vectors::DataFrame(coordinates),

             spatialCoordsNames = c("x", "y")

         )

     }

@@ -1,9 +1,11 @@

 #' Generate count matrix for spatially variable genes.

 #'

-#' @param size `int` genes will be spatially arranged on a size x size grid

-#' @param tot_genes `int` total number of genes

-#' @param de_genes `int` number of spatially variable genes

-#' @param return_SPE `logical`, whether to return result as a

+#' @param size An `integer` scalar. Cells will be spatially arranged on a `size

+#'   x size` grid. Default: 10, corresponding to 100 cells.

+#' @param tot_genes An `integer` scalar. Total number of genes. Default: 1000.

+#' @param de_genes An `integer` scaler. The number of spatially variable genes.

+#'   Default: 100.

+#' @param return_SPE A `logical`, whether to return result as a

 #'   \linkS4class{SpatialExperiment}. Default: `FALSE`.

 #'

 #' @return

@@ -19,7 +21,8 @@

 #'

 #' @export

 #' @importFrom stats rnbinom runif

-mockSVG <- function(size, tot_genes, de_genes, return_SPE = FALSE) {

+mockSVG <- function(size = 10, tot_genes = 1000, de_genes = 100,

+                    return_SPE = FALSE) {

     n_cells <- size * size

     coordinates <- data.frame(

         x = rep(seq.int(size), size),

@@ -3,13 +3,24 @@

 #' @param size `int` genes will be spatially arranged on a size x size grid

 #' @param tot_genes `int` total number of genes

 #' @param de_genes `int` number of spatially variable genes

+#' @param return_SPE `logical`, whether to return result as a

+#'   \linkS4class{SpatialExperiment}. Default: `FALSE`.

 #'

-#' @return `list` containing:

+#' @return

+#' If `return_SPE = TRUE`, returns a \linkS4class{SpatialExperiment} object.

+#'

+#' If not, a `list` containing:

 #' * `coordinates`: `data.frame` with `x` and `y` columns;

 #' * `counts`: `matrix` with generated gene counts.

+#'

+#' @examples

+#' spe <- mockSVG(10, tot_genes = 200, de_genes = 20, return_SPE = TRUE)

+#' spe

+#'

 #' @export

 #' @importFrom stats rnbinom runif

-mockSVG <- function(size, tot_genes, de_genes) {

+mockSVG <- function(size, tot_genes, de_genes, return_SPE = FALSE) {

+    n_cells <- size * size

     coordinates <- data.frame(

         x = rep(seq.int(size), size),

         y = rep(seq.int(size), each = size)

@@ -17,12 +28,27 @@ mockSVG <- function(size, tot_genes, de_genes) {

     mu <- 2^runif(tot_genes, -1, 5)

     counts <- matrix(

-        rnbinom(tot_genes * size * size, mu = mu, size = 10),

+        rnbinom(tot_genes * n_cells, mu = mu, size = 10),

         nrow = tot_genes

     )

     m <- (size / 2) + 1

     mask <- coordinates$x < m & coordinates$y < m

     counts[seq.int(de_genes), mask] <- counts[seq.int(de_genes), mask] + 20

-    list(coordinates = coordinates, counts = counts)

+    ## Set dimnames

+    gene_names <- sprintf("Gene_%s", formatC(seq_len(tot_genes), width = 4, flag = 0))

+    cell_names <- sprintf("Cell_%s", formatC(seq_len(n_cells), width = 3, flag = 0))

+    rownames(coordinates) <- cell_names

+    dimnames(counts) <- list(gene_names, cell_names)

+

+    out <- list(coordinates = coordinates, counts = counts)

+

+    if (return_SPE) {

+        out <- SpatialExperiment::SpatialExperiment(

+            assays = list(counts = counts),

+            spatialData = coordinates,

+            spatialCoordsNames = c("x", "y")

+        )

+    }

+    out

 }

@@ -10,16 +10,19 @@

 #' @export

 #' @importFrom stats rnbinom runif

 mockSVG <- function(size, tot_genes, de_genes) {

-  coordinates <- data.frame(

-    x = rep(seq.int(size), size), 

-    y = rep(seq.int(size), each=size)

-  ) 

-  

-  mu <- 2^runif(tot_genes, -1, 5)

-  counts <- matrix(rnbinom(tot_genes*size*size, mu=mu, size=10), nrow=tot_genes)

-  m <- (size/2)+1

-  mask <- coordinates$x < m & coordinates$y < m

-  counts[seq.int(de_genes), mask] <- counts[seq.int(de_genes), mask] + 20 

-  

-  list(coordinates=coordinates, counts=counts)

+    coordinates <- data.frame(

+        x = rep(seq.int(size), size),

+        y = rep(seq.int(size), each = size)

+    )

+

+    mu <- 2^runif(tot_genes, -1, 5)

+    counts <- matrix(

+        rnbinom(tot_genes * size * size, mu = mu, size = 10),

+        nrow = tot_genes

+    )

+    m <- (size / 2) + 1

+    mask <- coordinates$x < m & coordinates$y < m

+    counts[seq.int(de_genes), mask] <- counts[seq.int(de_genes), mask] + 20

+

+    list(coordinates = coordinates, counts = counts)

 }

@@ -8,6 +8,7 @@

 #' * `coordinates`: `data.frame` with `x` and `y` columns;

 #' * `counts`: `matrix` with generated gene counts.

 #' @export

+#' @importFrom stats rnbinom runif

 mockSVG <- function(size, tot_genes, de_genes) {

   coordinates <- data.frame(

     x = rep(seq.int(size), size), 

new file mode 100644

@@ -0,0 +1,24 @@

+#' Generate count matrix for spatially variable genes.

+#'

+#' @param size `int` genes will be spatially arranged on a size x size grid

+#' @param tot_genes `int` total number of genes

+#' @param de_genes `int` number of spatially variable genes

+#'

+#' @return `list` containing:

+#' * `coordinates`: `data.frame` with `x` and `y` columns;

+#' * `counts`: `matrix` with generated gene counts.

+#' @export

+mockSVG <- function(size, tot_genes, de_genes) {

+  coordinates <- data.frame(

+    x = rep(seq.int(size), size), 

+    y = rep(seq.int(size), each=size)

+  ) 

+  

+  mu <- 2^runif(tot_genes, -1, 5)

+  counts <- matrix(rnbinom(tot_genes*size*size, mu=mu, size=10), nrow=tot_genes)

+  m <- (size/2)+1

+  mask <- coordinates$x < m & coordinates$y < m

+  counts[seq.int(de_genes), mask] <- counts[seq.int(de_genes), mask] + 20 

+  

+  list(coordinates=coordinates, counts=counts)

+}