vignette revision; updated authors

Author: HelenaLC

.gitignore

@@ -3,4 +3,5 @@
 .RData
 .Ruserdata
 *.Rproj
+*html
 docs

DESCRIPTION

@@ -12,26 +12,18 @@ Authors@R: c(
 	    role=c("aut", "cre"),
 	    email="helena@crowell.eu", 
 	    comment=c(ORCID="0000-0002-4801-1767")),
-    person("Vince", "Carey",
-        role=c("aut"),
-        email="stvjc@channing.harvard.edu",
-        comment=c(ORCID="0000-0003-4046-0063")),
-    person("Yixing E.", "Dong",
-        role=c("aut"),
-        email="estelladong729@gmail.com",
-        comment=c(ORCID="0009-0003-5115-5686")),
     person("Artür", "Manukyan",
         role=c("aut"),
         email="artur-man@hotmail.com",
         comment=c(ORCID="0000-0002-0441-9517")),
-    person("Dario", "Righelli", 
-        role=c("aut"), 
-        email="dario.righelli@gmail.com",
-        comment=c(ORCID="0000-0003-1504-3583")),
-    person("Louise", "Deconinck",
+    person("Hugo", "Gruson",
         role=c("aut"),
-        email="louise.deconinck@gmail.com",
-        comment=c(ORCID="0000-0001-8100-6823")))
+        email="charlotte.soneson@fmi.ch",
+        comment=c(ORCID="0000-0002-4094-1476")),
+    person("Vince", "Carey",
+        role=c("aut"),
+        email="stvjc@channing.harvard.edu",
+        comment=c(ORCID="0000-0003-4046-0063")))
 Imports:
     DelayedArray,
     dplyr,

vignettes/SpatialData.plot.Rmd

@@ -3,11 +3,9 @@ title: "`SpatialData.plot`"
 date: "`r format(Sys.Date(), '%B %d, %Y')`"
 package: "`r BiocStyle::pkg_ver('SpatialData.plot')`"
 author: 
-  - name: Helena Lucia Crowell
-  - name: Louise Deconinck
+  - name: Helena L. Crowell
   - name: Artür Manukyan
-  - name: Dario Righelli
-  - name: Estella Dong
+  - name: Hugo Gruson
   - name: Vince Carey
 output:
   BiocStyle::html_document
@@ -69,7 +67,7 @@ Internally, multiscale `ImageArray`s are stored as a list of `ZarrArray`, e.g.:
 
 ```{r ms-dims}
 i <- image(x, "blobs_multiscale_image")
-vapply(i@data, dim, numeric(3))
+vapply(data(i, k=NULL), dim, numeric(3))
 ```
 
 To retrieve a specific scale's `ZarrArray`, we can use `data(., k)`,
@@ -82,7 +80,7 @@ wrap_plots(nrow=1, lapply(seq(3), \(.)
 
 ### Labels
 
-```{r plotLabel, fig.width=8, fig.height=3.5}
+```{r plotLabel, fig.width=9, fig.height=3.5}
 i <- "blobs_labels"
 t <- getTable(x, i)
 t$id <- sample(letters, ncol(t))
@@ -92,10 +90,9 @@ p <- plotSpatialData()
 pal_d <- hcl.colors(10, "Spectral")
 pal_c <- hcl.colors(9, "Inferno")[-9]
 
-a <- p + plotLabel(x, i) # simple binary image
-b <- p + plotLabel(x, i, c = "id", pal=pal_d) # 'colData'
-c <- p + plotLabel(x, i, c = "channel_1_sum", pal=pal_c) + 
-    theme(legend.key.width=unit(1, "lines")) # 'assay'
+a <- p + plotLabel(x, i, pal="grey")                   # binary
+b <- p + plotLabel(x, i, c="id", pal=pal_d)            # metadata
+c <- p + plotLabel(x, i, c="channel_1_sum", pal=pal_c) # assay
 
 (a | b | c) + 
     plot_layout(guides="collect") & 
@@ -105,24 +102,17 @@ c <- p + plotLabel(x, i, c = "channel_1_sum", pal=pal_c) +
 
 ### Points
 
-```{r plotPoint, eval=FALSE, fig.width=8, fig.height=2.5}
+```{r plotPoint, fig.width=10, fig.height=3}
 i <- "blobs_points"
-p <- plotSpatialData()
-# mock up a 'table'
-f <- list(
-  numbers=\(n) runif(n),
-  letters=\(n) sample(letters, n, TRUE))
-y <- setTable(x, i, f)
-# demo. viz. capabilities
-a <- p + plotPoint(y, i)
-b <- p + plotPoint(y, i, "letters") # discrete coloring
-c <- p + plotPoint(y, i, "numbers") # continuous coloring
-a | b | c
+a <- p + plotPoint(x, i)
+b <- p + plotPoint(x, i, col="genes")       # discrete
+c <- p + plotPoint(x, i, col="instance_id") # continuous
+(a | b | c) 
 ```
 
 ### Shapes
 
-```{r plotShape, fig.width=6, fig.height=2.5}
+```{r plotShape, fig.width=8, fig.height=3}
 p <- plotSpatialData()
 a <- p +
   ggtitle("polygons") +
@@ -133,7 +123,7 @@ b <- p +
 c <- p +
   ggtitle("circles") +
   plotShape(x, "blobs_circles")
-wrap_plots(a, b, c)
+(a | b | c)
 ```
 
 ### Layering
@@ -147,15 +137,15 @@ all <- p +
     plotShape(x, 1) +
     plotShape(x, 3) +
     new_scale_color() +
-    plotPoint(x, c="genes") +
+    plotPoint(x, col="genes") +
     ggtitle("layered")
 # split
 one <- list(
     p + plotImage(x) + ggtitle("image"),
     p + plotLabel(x) + ggtitle("labels"),
     p + plotShape(x, 1) + ggtitle("circles"),
     p + plotShape(x, 3) + ggtitle("polygons"),
-    p + plotPoint(x, c="genes") + ggtitle("points"))
+    p + plotPoint(x, col="genes") + ggtitle("points"))
 wrap_plots(c(list(all), one), nrow=2)
 ```
 
@@ -176,176 +166,52 @@ There are only `r length(SpatialData::shape(x, "cells"))` cells, but
 `r format(length(SpatialData::point(x, "single_molecule")), big.mark=",")` 
 molecules, so that we downsample a random subset of 1,000 for visualization:
 
-```{r merfish-plot}
-# downsample 1,000 points
-n <- length(p <- point(x))
-q <- p[sample(n, 1e3)]
-(point(x, "1k") <- q)
+```{r merfish-plot, fig.width=6, fig.height=4}
 # layered visualization
 plotSpatialData() +
     plotImage(x, c="white") +
-    new_scale_color() +
-    plotPoint(x, i="1k", c="cell_type", size=0.2) +
-    new_scale_color() +
-    plotShape(x, i="anatomical") +
-    scale_color_manual(values=hcl.colors(6, "Spectral")) 
-```
-
-```{r include=FALSE}
-knitr::opts_chunk$set(eval=FALSE)
-```
-
-```{r merfish-box}
-# bounding-box query
-qu <- list(xmin=1800, xmax=2400, ymin=5000, ymax=5400)
-bb <- geom_rect(do.call(aes, qu), data.frame(qu), col="yellow", fill=NA)
-y <- SpatialData(images=list("."=do.call(query, c(list(x=image(x)), qu))))
-plotSpatialData() + plotImage(x) + bb | plotSpatialData() + plotImage(y)
-```
-
-## VisiumHD
-
-Mouse intestine, 1GB; 4 image resolutions and 3 shapes at 2, 8, and 16 $\mu$m.
-
-```{r visiumhd-read, eval=FALSE}
-dir.create(td <- tempfile())
-pa <- MouseIntestineVisHD(target=td)
-(x <- readSpatialData(pa, images=4, shapes=3))
-```
-
-```{r visiumhd-plot, eval=FALSE}
-qu <- list(xmin=100, xmax=300, ymin=200, ymax=400)
-bb <- geom_rect(do.call(aes, qu), data.frame(qu), col="black", fill=NA)
-y <- SpatialData(images=list("."=do.call(query, c(list(x=image(x)), qu))))
-plotSpatialData() + plotImage(x) + bb | plotSpatialData() + plotImage(y)
+    plotPoint(x, n=1e3, col="cell_type", size=0.5) +
+    scale_color_manual(values=rainbow(8)) +
+    guides(col=guide_legend(override.aes=list(size=2))) +
+    plotShape(x, i="anatomical", fill=NA, col="white", linewidth=1) 
+```
+
+```{r merfish-box, fig.width=7, fig.height=6}
+# subset & downsample for speed
+y <- x[c("images", "points"), ]
+n <- length(point(y))
+i <- sample(n, 1e5)
+point(y) <- point(y)[i]
+# polygon queries
+lapply(seq_along(shape(x)), \(s) {
+    df <- data(shape(x)[s, ])
+    z <- crop(y, sf::st_as_sf(df))
+    plotSpatialData() + 
+        plotImage(z) +
+        plotPoint(z, n=1e3, size=1/3, col="cyan")
+}) |> wrap_plots(nrow=2) & theme(axis.text.x=element_text(angle=45, hjust=1))
 ```
 
 ## MibiTOF
 
 Colorectal carcinoma, 25 MB; no shapes, no points.
 
 ```{r mibitof-read}
-dir.create(td <- tempfile())
-pa <- SpatialData.data:::.unzip_spd_demo(
-    zipname="mibitof.zip", 
-    dest=td, source="biocOSN")
-(x <- readSpatialData(pa))
-```
-
-```{r mibitof-plot, fig.width=10, fig.height=3}
-pal <- hcl.colors(8, "Spectral")
-wrap_plots(nrow=1, lapply(seq(3), \(.)
-    plotSpatialData() + plotImage(x, .) +
-    plotLabel(x, ., c = "Cluster", pal=pal))) +
-    plot_layout(guides="collect")
-```
-
-## CyCIF (MCMICRO)
-
-Small lung adenocarcinoma, 250 MB; 1 image, 2 labels, 2 tables.
-
-```{r mcmicro-read}
-dir.create(td <- tempfile())
-pa <- SpatialData.data:::.unzip_spd_demo(
-    zipname="mcmicro_io.zip", 
-    dest=td, source="biocOSN")
-(x <- readSpatialData(pa))
+(x <- ColorectalCarcinomaMIBITOF())
 ```
 
-Getting channel names for the image:
-
-```{r mcmicro-channels}
-chs <- channels(image(x))
-```
-
-Plotting with multiple image channels:
-
-```{r mcmicro-plot}
-plotSpatialData() + plotImage(x,
-    ch=chs, 
-    c=grDevices::palette.colors(length(chs), palette = "Polychrome 36")
-)
-```
-
-We can specify contrast limits for each channel via the `cl` argument, but if not provided, they will be automatically computed as the 5th and 95th percentiles of the pixel intensities for each channel.
-
-## IMC (Steinbock)
-
-4 different cancers (SCCHN, BCC, NSCLC, CRC), 820 MB; 14 images, 14 labels, 1 table.
-
-```{r steinbock-read}
-dir.create(td <- tempfile())
-pa <- SpatialData.data:::.unzip_spd_demo(
-    zipname="steinbock_io.zip", 
-    dest=td, source="biocOSN")
-x <- readSpatialData(pa)
-```
-
-### channels
-
-```{r steinbock-ch}
-plotSpatialData() + plotImage(x,
-    i="Patient3_003_image",
-    ch=c(6, 22, 39),
-    c=c("blue", "cyan", "yellow"))
+```{r mibitof-plot, fig.width=9, fig.height=3.5}
+ps <- lapply(imageNames(x), \(i) plotSpatialData() + plotImage(x, i) + ggtitle(i))
+wrap_plots(ps, nrow=1)
 ```
 
-### contrasts
-
-```{r steinbock-cl, fig.width=9, fig.height=3}
-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),
-        cl=list(1, 1, .),
-        c=c("blue", "cyan", "yellow")) +
-        ggtitle(sprintf("[%s, %s]", .[1], .[2]))
-}) |> wrap_plots(nrow=1) + plot_layout(guides="collect")
-```
-
-# Masking
-
-Back to blobs...
-
-```{r read-mask}
-x <- file.path("extdata", "blobs.zarr")
-x <- system.file(x, package="SpatialData")
-x <- readSpatialData(x, tables=FALSE)
-```
-
-```{r plot-mask-one, fig.width=8, fig.height=3.5}
-i <- "blobs_circles"
-x <- mask(x, "blobs_points", i)
-(t <- getTable(x, i))
-p <- plotSpatialData() + 
-    plotPoint(x, c="genes") +
-    scale_color_manual(values=c("tomato", "cornflowerblue")) +
-    new_scale_color()
-lapply(names(c <- c(a="red", b="blue")), \(.)
-    p + plotShape(x, i, c=paste0("gene_", .)) + 
-        scale_color_gradient2(
-            low="grey", high=c[.],
-            limits=c(0, 8), n.breaks=5)) |>
-    wrap_plots() + plot_layout(guides="collect")
-```
-
-```{r plot-mask-two, fig.width=8, fig.height=3.5}
-# compute channel-wise means
-i <- "blobs_labels"
-table(x) <- NULL
-x <- mask(x, "blobs_image", i, fun=mean)
-(t <- getTable(x, i))
-# visualize side-by-side
-ps <- lapply(paste(seq_len(3)), \(.) 
-    plotSpatialData() + plotLabel(x, i, c = .) + 
-    ggtitle(paste("channel", ., "sum"))) 
-wrap_plots(ps, nrow=1) & theme(
-    legend.position="bottom", 
-    legend.title=element_blank(),
-    legend.key.width=unit(1, "lines"),
-    legend.key.height=unit(0.5, "lines"))
+```{r fig.width=8, fig.height=4}
+# bounding-box query
+bb <- list(
+    xmin=0, xmax=800, 
+    ymin=500, ymax=900)
+y <- crop(x["images", 1], bb)
+plotSpatialData() + plotImage(y)
 ```
 
 # Session info