update vignette, using new spatialdata.data methods

R/plotLabel.R

@@ -31,8 +31,10 @@
 #' 
 #' # coloring by 'colData'
 #' n <- length(unique(t$id))
-#' pal <- hcl.colors(n, "Spectral")
-#' p + plotLabel(x, i, "id", pal=pal)
+#' # TODO: did a small fix to color scheme below, will fix example later
+#' # pal <- hcl.colors(n, "Spectral")
+#' pal_d <- hcl.colors(10, "Spectral")
+#' p + plotLabel(x, i, "id", pal=pal_d)
 #' 
 #' # coloring by 'assay' data
 #' p + plotLabel(x, i, "channel_1_sum")

tests/testthat/test-plotImage.R

@@ -56,8 +56,7 @@ test_that(".check_cl", {
 })
 
 dir.create(td <- tempfile())
-pa <- unzip_spd_demo(zipname="steinbock_io.zip", dest=td, source="biocOSN")
-x <- readSpatialData(pa, images=1, labels=FALSE, points=FALSE, shapes=FALSE, tables=FALSE)
+(x <- SpatialData.data::MulticancerSteinbock())
 a <- data(image(x)[seq_len(3), seq_len(100), seq_len(100)], 1)
 
 test_that(".get_img_dt", {

vignettes/SpatialData.plot.Rmd

@@ -167,11 +167,7 @@ In this example data, we do not have a `label` for the `shape` polygons.
 Such labels could be morphological regions annotated by pathologists.
 
 ```{r merfish-read}
-dir.create(td <- tempfile())
-pa <- unzip_spd_demo(
-    zipname="merfish.zarr.zip", 
-    dest=td, source="biocOSN")
-(x <- readSpatialData(pa, anndataR=FALSE))
+(x <- MouseBrainMERFISH())
 ```
 
 There are only `r length(SpatialData::shape(x, "cells"))` cells, but 
@@ -206,11 +202,7 @@ plotSpatialData() + plotImage(x) + bb | plotSpatialData() + plotImage(y)
 Mouse intestine, 1GB; 4 image resolutions and 3 shapes at 2, 8, and 16 $\mu$m.
 
 ```{r visiumhd-read}
-dir.create(td <- tempfile())
-pa <- unzip_spd_demo(
-    zipname="visium_hd_3.0.0_io.zip", 
-    dest=td, source="biocOSN")
-(x <- readSpatialData(pa, images=4, shapes=3, tables=FALSE))
+(x <- MouseIntestineVisHD())
 ```
 
 ```{r visiumhd-plot}
@@ -225,11 +217,7 @@ plotSpatialData() + plotImage(x) + bb | plotSpatialData() + plotImage(y)
 Colorectal carcinoma, 25 MB; no shapes, no points.
 
 ```{r mibitof-read}
-dir.create(td <- tempfile())
-pa <- unzip_spd_demo(
-    zipname="mibitof.zip", 
-    dest=td, source="biocOSN")
-(x <- readSpatialData(pa, anndataR=FALSE))
+(x <- ColorectalCarcinomaMIBITOF()) 
 ```
 
 ```{r mibitof-plot, fig.width=10, fig.height=3}
@@ -245,11 +233,7 @@ wrap_plots(nrow=1, lapply(seq(3), \(.)
 Small lung adenocarcinoma, 250 MB; 1 image, 2 labels, 2 tables.
 
 ```{r mcmicro-read}
-dir.create(td <- tempfile())
-pa <- unzip_spd_demo(
-    zipname="mcmicro_io.zip", 
-    dest=td, source="biocOSN")
-(x <- readSpatialData(pa, anndataR=FALSE))
+x <- LungAdenocarcinomaMCMICRO()
 ```
 
 Getting channel names for the image:
@@ -272,11 +256,7 @@ plotSpatialData() + plotImage(x,
 4 different cancers (SCCHN, BCC, NSCLC, CRC), 820 MB; 14 images, 14 labels, 1 table.
 
 ```{r steinbock-read}
-dir.create(td <- tempfile())
-pa <- unzip_spd_demo(
-    zipname="steinbock_io.zip", 
-    dest=td, source="biocOSN")
-x <- readSpatialData(pa, anndataR=FALSE)
+x <- MulticancerSteinbock()
 ```
 
 ### channels
@@ -294,9 +274,9 @@ plotSpatialData() + plotImage(x,
 i <- image(x, "Patient3_003_image")
 image(x, "crop") <- i[, 200:400, 200:400]
 lapply(list(c(0.2, 1), c(0, 0.8), c(0, 1.2)), \(.) {
-    plotSpatialData() + plotImage(x, 
-        i="crop", 
-        ch=c(6, 22, 39), 
+    plotSpatialData() + plotImage(x,
+        i="crop",
+        ch=c(6, 22, 39),
         cl=list(1, 1, .),
         c=c("blue", "cyan", "yellow")) +
         ggtitle(sprintf("[%s, %s]", .[1], .[2]))