update vignettes

vignettes/Session_2_Tidy_spatial_analyses.Rmd

@@ -590,7 +590,7 @@ spatial_data_filtered |>
 As you can appreciate, the relationship between the number of genes, probed Purcell and their mitochondrial prescription abundance it's quite  consistent.
 
 :::: {.note}
-**Excercise**
+**Excercise 2.1**
 
 To to practice the use of `tidyomics` on spatial data, we propose a few exercises that connect manipulation, calculations and visualisation. These exercises are just meant to be simple use cases that exploit tidy R streamlined language.
 
@@ -600,43 +600,6 @@ We assume that the cells we filtered as non-alive or damaged, characterised by b
 Use `tidyomic`s/`tidyverse` tools to label dead cells and perform differential expression within each region. Some of the comments you can use are: `mutate`, `nest`, `aggregate_cells`.
 ::::
 
-**Solution**
-
-```{r}
-library(tidySummarizedExperiment)
-library(tidybulk)
-  
-differential_analysis = 
-  spatial_data |> 
-  mutate(
-    dead = 
-      
-      # Stringent threshold
-      subsets_mito_percent > 20 |
-      sum < 700 |
-      detected < 500
-  ) |> 
-  aggregate_cells(c(sample_id, spatialLIBD, dead)) |> 
-  keep_abundant(factor_of_interest = c(dead)) |> 
-  nest(data = - spatialLIBD) |> 
-  
-  # filter regions having both alive and dead cells
-  filter(  map_int(data, ~ .x |> distinct(sample_id, dead) |> nrow() ) == 6 ) |>
-  mutate(data = map(
-    data,
-    test_differential_abundance,
-    ~ dead + sample_id,
-    method = "edgeR_quasi_likelihood", 
-    test_above_log2_fold_change = log(2)
-  )) 
-
-differential_analysis |> 
-  mutate(data = map(data, pivot_transcript)) |> 
-  unnest(data) |> 
-  filter(FDR<0.05) 
-
-#  tidybulk::test_differential_abundance(~ dead + sample_id + (1 | spatialLIBD), method = "glmmseq_lme4")
-```
 
 **Session Information**
 

vignettes/Session_3_imaging_assays.Rmd

@@ -277,13 +277,6 @@ tx_spe_sample_1 |>
       scale_color_manual(values = colorRampPalette( brewer.pal(9,"Set1") )(150) ) +
   guides(color = "none")
 
-```
-
-### 3. Cell type caracterisation
-
-```{r}
-
-
 ```
 
 ### 4. Neighborhood analyses
@@ -295,12 +288,12 @@ https://www.bioconductor.org/packages/release/bioc/vignettes/hoodscanR/inst/doc/
 https://divingintogeneticsandgenomics.com/post/neighborhood-cellular-niches-analysis-with-spatial-transcriptome-data-in-seurat-and-bioconductor/
 
 :::: {.note}
-**Excercise**
+**Exercise**
 
 **Spatial-aware clustering:** Apply the spatial aware clustering method BANKSY. Taking as example the code run for session 2.
 ::::
 
-### 5. Neighborhood analysis
+
 
 ```{r, fig.width=7, fig.height=8, message=FALSE}
 
@@ -368,7 +361,7 @@ tx_spe_sample_1 |>
 ```
 
 
-### 6. Supplementary: `MoleculeExperiment` package
+### 5. Supplementary: `MoleculeExperiment` package
 
 https://www.bioconductor.org/packages/release/bioc/vignettes/MoleculeExperiment/inst/doc/MoleculeExperiment.html
 

vignettes/Solutions.Rmd

@@ -63,3 +63,42 @@ plotSpotQC(
   facet_wrap(~sample_id)
 ```
 
+:::: {.note}
+**Excercise 2.1**
+::::
+
+```{r}
+library(tidySummarizedExperiment)
+library(tidybulk)
+  
+differential_analysis = 
+  spatial_data |> 
+  mutate(
+    dead = 
+      
+      # Stringent threshold
+      subsets_mito_percent > 20 |
+      sum < 700 |
+      detected < 500
+  ) |> 
+  aggregate_cells(c(sample_id, spatialLIBD, dead)) |> 
+  keep_abundant(factor_of_interest = c(dead)) |> 
+  nest(data = - spatialLIBD) |> 
+  
+  # filter regions having both alive and dead cells
+  filter(  map_int(data, ~ .x |> distinct(sample_id, dead) |> nrow() ) == 6 ) |>
+  mutate(data = map(
+    data,
+    test_differential_abundance,
+    ~ dead + sample_id,
+    method = "edgeR_quasi_likelihood", 
+    test_above_log2_fold_change = log(2)
+  )) 
+
+differential_analysis |> 
+  mutate(data = map(data, pivot_transcript)) |> 
+  unnest(data) |> 
+  filter(FDR<0.05) 
+
+#  tidybulk::test_differential_abundance(~ dead + sample_id + (1 | spatialLIBD), method = "glmmseq_lme4")
+```