Merge pull request #81 from jiajic/main

add missing code from morning tutorials

01_session2.Rmd

@@ -36,6 +36,15 @@ Each package also has its own website:
 
 More information is available at https://drieslab.github.io/Giotto_website/articles/ecosystem.html 
 
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 ## Installation + python environment
 
 ### Giotto installation
@@ -56,26 +65,28 @@ To install the currently released version of Giotto in a single step:
 pak::pak("drieslab/Giotto")
 ```
 
-This should automatically install all the Giotto dependencies and other Giotto module packages.
+This should automatically install all the Giotto dependencies and other Giotto module packages (main branch).
 
 
 
 #### Installation of dev branch Giotto packages
 
-You can install dev branch versions by using `devtools::install_github()`
+_pak_ tends to forcibly install all dependencies, which can have issues when working with multiple dev branch packages.
+
+You can install dev branch versions by using `devtools::install_github()` instead
 
 Core module dev branchs:
 
-- "drieslab/Giotto@suite_dev"
-- "drieslab/GiottoVisuals@dev"
-- "drieslab/GiottoClass@dev"
-- "drieslab/GiottoUtils@dev"
+- `"drieslab/Giotto@suite_dev"`
+- `"drieslab/GiottoVisuals@dev"`
+- `"drieslab/GiottoClass@dev"`
+- `"drieslab/GiottoUtils@dev"`
 
 ```{r, eval=FALSE}
 devtools::install_github("drieslab/GiottoClass@dev")
 ```
 
-_pak_ tends to forcibly install all dependencies, which can have issues when working with multiple dev branch packages.
+
 
 
 #### Common install issues
@@ -222,6 +233,7 @@ test <- giotto(instructions = instrs) # passing NULL instead will also generate
 g <- GiottoData::loadGiottoMini("visium")
 instructions(g) <- instrs
 instructions(g, "show_plot") # instructions say not to plot to viewer
+
 spatPlot2D(g, show_image = TRUE, image_name = "image")
 # instead it will directly write to the results folder
 ```

01_session3.Rmd

@@ -176,7 +176,7 @@ knitr::include_graphics("img/01_session3/gpoly_s.png")
 
 ### images
 
-Most images are openable using `createGiottoLargeImage()` which wraps `terra::rast()`. This allows compatibility with most common image types. Recent and non-geospatially related image formats are not well supported however. One example is `ome.tif` which 10X uses for large image exports from Xenium. For these, we use `ometif_to_tif()` to convert them into normal `.tif` files using the python _tifffile_ package. `ometif_metadata` can be used to extract and access the associated `ome xml` image metadata.
+Most images are openable using `createGiottoLargeImage()` which wraps `terra::rast()`. This allows compatibility with most common image types. Recent and non-geospatially related image formats are not well supported however. One example is `ome.tif` which 10X uses for large image exports from Xenium. For these, we use `ometif_to_tif()` to convert them into normal `.tif` files using the python _tifffile_ package. `ometif_metadata()` can be used to extract and access the associated `ome xml` image metadata.
 
 
 ### jsonlike formats
@@ -194,7 +194,7 @@ There are many types of data in spatial-omics analysis. Hierarchical formats aff
 
 ## Pre-processing
 
-The most common types of raw data needed for a Giotto object are expression matrices, centroids information, spatial feature points, polygons, and expression information. Evaluation of input data and conversion to compatible formats happens inside the `create*` functions that Giotto exports. There is one of these for each of the subobject classes.
+The most common types of raw data needed for a Giotto object are expression matrices, centroids information, spatial feature points, polygons. Evaluation of input data and conversion to compatible formats happens inside the `create*` functions that Giotto exports. There is one of these for each of the subobject classes.
 
 ### Expression matrix
 
@@ -350,7 +350,7 @@ Most of the subobjects are tagged with metadata information that allow them find
 
 ### IDs
 
-`spatIDs` and `featIDs` are used to find the spatial or feature IDs of an object.
+`spatIDs()` and `featIDs()` are used to find the spatial or feature IDs of an object.
 
 ```{r, eval=FALSE}
 spatIDs(sl_xy)