Finish spatial transcriptomics use-case

use-cases/spatial_transcriptomics.md

@@ -7,11 +7,47 @@ For this example there is also a [video](https://youtu.be/1dDaxOAZ9Sg) that high
 
 ## Data & project set-up
 
-The data from this publication comes from the publication [Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis](https://doi.org/10.1038/s41587-021-01006-2).
-
-TODO
+The data in this project comes from the publication [Integration of spatial and single-cell transcriptomic data elucidates mouse organogenesis](https://doi.org/10.1038/s41587-021-01006-2), which studies the genetic mechanisms of organ formation in mouse embryos.
+The study contains data from three embryos and contains membrane and nucleus channels, decoded gene detections and segmented cells.
+We present all of the data for one of the embryos in MoBIE, using the MoBIE python library to create the corresponding MoBIE project (see the scripts in [git repository](https://github.com/mobie/mouse-embryo-spatial-transcriptomics-project) for details.)
 
+Note that this data is also available on [IDR](https://idr.openmicroscopy.org/) (accession id: `idr0138`) and that we access the image data from a mirror of the IDR data set up on the EBI-embassy s3 cloud.
 
 ## Exploring the project
 
-TODO
+Open the project from [https://github.com/mobie/mouse-embryo-spatial-transcriptomics-project](https://github.com/mobie/mouse-embryo-spatial-transcriptomics-project). See ["Getting Started"]("../tutorials/explore_a_prject.md") for how to open a project in the MoBIE Fiji plugin.
+The project will open to the `default` view, which shows just one of the tile positions of the embryo. (This is done in order to open the project fast.)
+We switch to the view `stitched-view` to see the full embryo. Note that this will take a while since this view contains many spots (corresponding to the gene detections).
+<br> <img width="800" alt="Stitched view of all positions in the mouse embryo" src="./images/st-stitched-view.png"> <br>
+
+Here, each colored position corrpesonds to a tile (annotated through a `RegionDisplay`). The tiles are placed at their correct positions using affine transformations specified in the view. And we can see the membrane channel as the only data that is initially visible.
+Let's explore the available data and zoom into one of the tiles:
+<br> <img width="800" alt="Zoomin to one of the tiles" src="./images/st-zoomin.png"> <br>
+
+Next, we activate the `genes` layer, which will show each decoded gene detection with a spot in the image:
+<br> <img width="800" alt="Genes" src="./images/st-genes.png"> <br>
+
+As you can see we have very many spots (11 million for the whole embryo). Since they are all loaded at once analyzing them visually is challenging.
+We can for example select spots interactively from the table or image, just as for segmentations or regions:
+<br> <img width="800" alt="Gene selection" src="./images/st-genes-selected.png"> <br>
+
+We can also select all spots corresponding to certain genes using the table, via `Select->Select Equal To...` in the `genes` table. And then choosing `Column: geneID` and entering the gene name in the `value` field. If `Keep current selection` is marked the spots that are already selected will stay active, if not they will be deselected.
+Below screenshot shows all spots for the two genes `Podxl` and `Foxh1` selected, and the selection menu with the settings to add the ones corresponding to `Abcc4` open: 
+<br> <img width="800" alt="Gene table selection" src="./images/st-genes-table.png"> <br>
+
+This feature is especially useful to see the spatial distribution of one or several genes, see for example the distribution for `Foxf1` below.
+<br> <img width="800" alt="Gene table selection" src="./images/st-foxf1.png"> <br>
+
+While these features enable visual inspection and qualitative analysis of the gene expression spots, quantitative analysis is best done in a specialized external tool.
+By supporting import and export of tabular data MoBIE can then load these analysis results to help visualize them in full spatial context. See [importing and exporting tables]("../tutorials/importing_and_exporting_tables.md") for more details. 
+
+<!--- Don't have access to these clusterings right now.
+Here, we visualize one of the main analysis results from the original publication, the gene based clustering of segmented cells:
+First let's see the cell segmentation by activating the `cells` layer:
+<br> <img width="800" alt="Initial full plate view" src="./images/full_plate_initial.png"> <br>
+
+As you can see, the segmentation was done per tile, and we also have some ids that correspond to the background.
+
+Now, let's load the clustering result. It is stored in the `xxx` column of the `cells` table and we can visualize it by selecting `Color->Color by Column...` and then entering `Column: xxx`, `Color by Column: glasbey` in the menu that opens:
+<br> <img width="800" alt="Initial full plate view" src="./images/full_plate_initial.png"> <br>
+-->