gistfile1.txt

---
title: "`Some SpatialData visualizations`"
date: "`r format(Sys.Date(), '%B %d, %Y')`"
package: "`r BiocStyle::pkg_ver('SpatialData')`"
author:
  - name: Helena Lucia Crowell
  - name: Louise Deconinck
  - name: Artür Manukyan
  - name: Dario Righelli
  - name: Estella Dong
  - name: Vince Carey
output:
  BiocStyle::html_document
vignette: |
  %\VignetteIndexEntry{Some SpatialData visualizations}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---


```{r backing, echo=FALSE}
# shape rendering support
# could transition to S4 if needed

.shapenames = function(sdobj) {
 stopifnot(is(sdobj, "SpatialData"))
 names(shapes(sdobj))
}

.shapes2sf = function(sdobj, elem) {
 stopifnot(elem %in% .shapenames(sdobj))
 sf::st_as_sf(SpatialData::data(SpatialData::shapes(sdobj)[[elem]]))
}

.pointnames = function(sdobj) {
 stopifnot(is(sdobj, "SpatialData"))
 names(points(sdobj))
}

.txdf = function(sdobj) {
 stopifnot("transcripts" %in% .pointnames(sdobj))
 as.data.frame(data(points(sdobj)$transcripts))
}

.pointdf = function(sdobj, elem) {
 stopifnot(elem %in% .pointnames(sdobj))
 as.data.frame(data(points(sdobj)[[elem]]))
}

.available_transcripts = function(sdobj) {   # maybe too specific?  'points'?
 txdf = .txdf(sdobj)
 as.character(unique(txdf$feature_name)) # valid?  feature_name comes back as *factor*
}
 
viewShape = function(sdobj, elem) {
 thesf = .shapes2sf(sdobj, elem)
 ggplot2::ggplot(thesf) + geom_sf()
}

add_tx = function(sdobj, featurename, size=.1) {
 txdf = .txdf(sdobj) |> dplyr::filter(feature_name == featurename)
 ggplot2::geom_point(data=txdf, aes(x=x, y=y), size=size)
}

add_points = function(sdobj, featurename, size=.1) {
 pointdf = .pointdf(sdobj) |> dplyr::filter(feature_name == featurename)
 ggplot2::geom_point(data=pointdf, aes(x=x, y=y), size=size)
}
```

# Introduction

In this vignette we explore how shape and point data in Xenium and
MERFISH experiments can be visualized.  We use examples that
can be retrieved from a Bioconductor bucket and cached in
BiocFileCache, using utilities in SpatialData package.

The xenium examples are:
```{r lkx, message=FALSE}
library(SpatialData)
available_10x_xen_zips()
```

[1] "Xenium_V1_human_Breast_2fov_outs.zip"
[2] "Xenium_V1_human_Lung_2fov_outs.zip"  


# Breast sample, "two fields of view"

## A view of "image data"

```{r getbr, message=FALSE}
pa <- path_to_10x_xen_demo("Xenium_V1_human_Breast_2fov_outs.zip")
dir.create(td <- tempfile())
unzip(pa, exdir=td)
# read & write to .zarr w/ 'spatialdata-io'
target <- tempfile()
nn = use_sdio("xenium", srcdir=td, dest=target) # returns NULL
# read into R
(br2fov <- readSpatialData(target))
plotSpatialData() + plotImage(br2fov)
```

## A view of the segmentation

```{r lkcb, message=FALSE}
library(ggplot2)
viewShape(br2fov, "cell_boundaries")
```

## Add points for a specific 'transcript'

```{r lkcb2, message=FALSE}
viewShape(br2fov, "cell_boundaries") + add_tx(br2fov, "EPCAM")
unlink(target, recursive=TRUE)
```

# Lung sample, "two fields of view"

## Image

```{r getlu, message=FALSE}
pa <- path_to_10x_xen_demo("Xenium_V1_human_Lung_2fov_outs.zip")
dir.create(td <- tempfile())
unzip(pa, exdir=td)
target <- tempfile()
nn = use_sdio("xenium", srcdir=td, dest=target) # returns NULL
(lu2fov <- readSpatialData(target))
plotSpatialData() + plotImage(lu2fov)
```

## Transcripts on segmentation

```{r lklu2, message=FALSE}
viewShape(lu2fov, "cell_boundaries") + add_tx(lu2fov, "EPCAM")
```