Merge pull request #1 from tidyomics/add_files

Add files

DESCRIPTION

@@ -1,18 +1,13 @@
 Package: tidySpatialWorkshop2024
 Title: Tidy genomic and transcriptomic single-cell analyses
-Version: 0.16.13
+Version: 0.17.0
 Authors@R: 
     c(
     person(given = "Stefano",
            family = "Mangiola",
-           role = c("aut"),
-           email = "[email protected]",
-           comment = c(ORCID = "0000-0001-7474-836X")),
-    person(given = "Michael",
-           family = "Love",
            role = c("aut", "cre"),
-           email = "[email protected]",
-           comment = c(ORCID = "0000-0001-8401-0545"))
+           email = "[email protected]",
+           comment = c(ORCID = "0000-0001-7474-836X"))
 	   )
 Description: Workflow for BioC conference showing tidy genomic
     and transcriptomic analyses, for a single-cell RNA-seq
@@ -22,7 +17,7 @@ Encoding: UTF-8
 LazyData: true
 LazyDataCompression: xz
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.1
 Depends:
     Biobase,
     R (>= 4.2.0)

vignettes/Introduction.Rmd

@@ -41,7 +41,7 @@ knitr::opts_chunk$set(echo = TRUE, cache = FALSE)
 
 You can view the material at the workshop webpage
 
-[here](https://tidyomics.github.io/spatialOmicsWorkshop2024/articles/main.html).
+[here](https://tidyomics.github.io/tidySpatialWorkshop2024/articles/main.html).
 
 ### Installation
 

vignettes/Session_1_sequencing_assays.Rmd

@@ -3,7 +3,7 @@ title: "Sequencing assays"
 author:
   - Stefano Mangiola, South Australian immunoGENomics Cancer Institute^[<[email protected]>], Walter and Eliza Hall Institute^[<mangiola.s at wehi.edu.au>]
 output: rmarkdown::html_vignette
-# bibliography: "`r file.path(system.file(package='spatialOmicsWorkshop2024', 'vignettes'), 'tidyomics.bib')`"
+# bibliography: "`r file.path(system.file(package='tidySpatialWorkshop2024', 'vignettes'), 'tidyomics.bib')`"
 vignette: >
   %\VignetteIndexEntry{Sequencing assays}
   %\VignetteEncoding{UTF-8}

vignettes/Session_2_Tidy_spatial_analyses.Rmd

@@ -3,7 +3,7 @@ title: "Tidy spatial analyses"
 author:
   - Stefano Mangiola, South Australian immunoGENomics Cancer Institute^[<[email protected]>], Walter and Eliza Hall Institute^[<mangiola.s at wehi.edu.au>]
 output: rmarkdown::html_vignette
-# bibliography: "`r file.path(system.file(package='spatialOmicsWorkshop2024', 'vignettes'), 'tidyomics.bib')`"
+# bibliography: "`r file.path(system.file(package='tidySpatialWorkshop2024', 'vignettes'), 'tidyomics.bib')`"
 vignette: >
   %\VignetteIndexEntry{Tidy spatial analyses}
   %\VignetteEncoding{UTF-8}
@@ -24,7 +24,7 @@ library(here)
 
 A good introduction of `tidyomics` can be found here
 
-[tidyomicsWorkshopBioc2023](https://github.com/tidyomics/tidyomicsWorkshopBioc2023) 
+[tidySpatialWorkshop2024](https://github.com/tidyomics/tidySpatialWorkshop2024) 
 [tidy transcriptomic manifesto](https://tidyomics.github.io/tidyomicsBlog/post/2021-07-07-tidy-transcriptomics-manifesto/)
 
 `tidyomics` is an interoperable software ecosystem that bridges Bioconductor and the tidyverse. `tidyomics` is installable with a single homonymous meta-package. This ecosystem includes three new packages: tidySummarizedExperiment, tidySingleCellExperiment, and tidySpatialExperiment, and five publicly available R packages: `plyranges`, `nullranges`, `tidyseurat`, `tidybulk`, `tidytof`. Importantly, `tidyomics` leaves the original data containers and methods unaltered, ensuring compatibility with existing software, maintainability and long-term Bioconductor support. 

vignettes/Session_3_imaging_assays.Rmd

@@ -60,29 +60,20 @@ tx |> filter(sample_id=="Xenium_V1_FF_Mouse_Brain_MultiSection_1_outs") |> nrow(
 # 62,744,602
 ```
 
-From this large dataset, we select a small reagion for illustrative purposes
-
-```{r, eval=FALSE}
-tx_small_region =
-  tx |>
-    filter(x |> between(3700, 4200), y |> between(5000, 5500))
-```
+#### An overview of the data
 
-Load the pre-saved data
 
-```{r}
-tx_small_region = spatialOmicsWorkshop2024::tx_small_region
+```{r, fig.width=7, fig.height=8, eval=FALSE}
+tx_small =  tx[sample(seq_len(nrow(tx)), size = nrow(tx)/500),]
 ```
 
-#### An overview of the data
-
+```{r, echo=FALSE}
+tx_small_file = tempfile() 
+utils:: download.file("https://zenodo.org/records/11213118/files/tx_small.rda?download=1", destfile = tx_small_file)
+load(tx_small_file)
+```
 
 ```{r, fig.width=7, fig.height=8}
-# tx_small =  tx[sample(seq_len(nrow(tx)), size = nrow(tx)/500),]
-# tx_small |> saveRDS("xenium_SubcellularSpatialData.rds", compress = "xz")
-
-tx_small = spatialOmicsWorkshop2024::tx_small
-
 
 tx_small |>
     ggplot(aes(x, y, colour = region)) +
@@ -102,6 +93,21 @@ Although in this session we will not use `MoleculeExperiment` class, because of
 
 We show how we would import our table of probe location into a  `MoleculeExperiment`. At the end of the Session, for knowledge, we will navigate the example code given in the [vignette material](https://www.bioconductor.org/packages/release/bioc/vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.html).
 
+From this large dataset, we select a small reagion for illustrative purposes
+
+```{r, eval=FALSE}
+tx_small_region =
+  tx |>
+    filter(x |> between(3700, 4200), y |> between(5000, 5500))
+```
+
+Load the pre-saved data
+
+```{r, echo=FALSE}
+tx_small_region_file = tempfile() 
+utils::download.file("https://zenodo.org/records/11213155/files/tx_small_region.rda?download=1", destfile = tx_small_region_file)
+load(tx_small_region_file)
+```
 
 ```{r}
 library(MoleculeExperiment)
@@ -167,11 +173,16 @@ We can appreciate how, even subsampling the data 1 in 500, we still have a vast
 
 We will convert our cell by gene count to a `SpatialExperiment`. This object stores a cell by gene matrix with relative XY coordinates.
 
+```{r}
+tx_spe_file = tempfile() 
+utils::download.file("https://zenodo.org/records/11213166/files/tx_spe.rda?download=1", destfile = tx_spe_file)
+load(tx_spe_file)
+```
 
 ```{r}
 
 tx_spe = 
-  spatialOmicsWorkshop2024::tx_spe |> 
+  tx_spe |> 
   
   # Scaling and tranformation
   logNormCounts()