update sessions

vignettes/Introduction.Rmd

@@ -31,6 +31,12 @@ knitr::opts_chunk$set(echo = TRUE, cache = FALSE)
 
 - TWITTER/X: https://x.com/LGMartelotto
 
+## Workshop partner: PHYSALIA
+
+```{r, echo=FALSE, out.width="700px"}
+knitr::include_graphics(here("inst/images/physalia-min.png"))
+```
+
 ## Workshop goals and objectives
 
 ### What you will learn

vignettes/Session_1_sequencing_assays.Rmd

@@ -837,8 +837,6 @@ brain_reference_list <- lapply(unique(brain_reference$dataset_id), function(x) b
 dec = scran::modelGeneVar(brain_reference, subset.row = genes, block = brain_reference$sample_id)
 hvg_CAQ = scran::getTopHVGs(dec, n = 1000)
             
-
-
 hvg_CAQ = unique( unlist(hvg_CAQ))
 
 head(hvg_CAQ)
@@ -854,6 +852,12 @@ spatial_data_gene_name = logNormCounts(spatial_data_gene_name)
 
 We then identify and score relevant marker genes based on their expression and significance across different cell types. The result is a curated list of high-potential marker genes, organised and ready for deeper analysis and interpretation in the context of spatial gene expression patterns.
 
+::: {.note}
+This function provides a convenience wrapper for marker gene identification between groups of cells, based on running `pairwiseTTests.` 
+
+This function represents a simpler and more intuitive summary of the differences between the groups. We do this by realizing that the p-values for these types of comparisons are largely meaningless; individual cells are not meaningful units of experimental replication, while the groups themselves are defined from the data. Thus, by discarding the p-values, we can simplify our marker selection by focusing only on the effect sizes between groups.
+:::
+
 ```{r}
 mgs <- scran::scoreMarkers(
   brain_reference, 

vignettes/Session_2_Tidy_spatial_analyses.Rmd

@@ -113,6 +113,9 @@ rownames(spatialCoords(spatial_data)) = colnames(spatial_data) # Bug?
 # Display the object
 spatial_data
 ```
+::: {.note}
+If `ExperimentHub` should not work. The `spatial_data` object from the previous code block can be downloaded from [Zenodo - 10.5281/zenodo.11233385](https://zenodo.org/records/11233385/files/tidySpatialWorkshop2024_spatial_data.rds?download=1)
+:::
 
 ### 1. tidySpatialExperiment package
 
@@ -332,7 +335,6 @@ This capability is powered by `tidygate`. We show how you can visualise your dat
 Let's draw an arbitrary gate interactively
 
 ```{r, eval=FALSE}
-
 spatial_data_gated = 
   spatial_data |> 
   
@@ -724,6 +726,7 @@ spatial_data |>
   ggplot(aes(array_row, array_col)) +
   geom_point(aes(color = spatialLIBD)) +
   facet_wrap(~sample_id) +
+  coord_fixed() +
   theme(legend.position = "none") +
   labs(title = "spatialLIBD regions")
 ```
@@ -754,7 +757,8 @@ spatial_data_filtered |>
   ggplot(aes(subsets_mito_percent, sum_gene)) + 
   geom_point(aes(color = spatialLIBD), size=0.2) +  
   stat_ellipse(aes(group = spatialLIBD), alpha = 0.3) +
-  scale_color_manual(values = libd_layer_colors |> str_remove("ayer")) +
+  scale_color_manual(values = libd_layer_colors |>
+  str_remove("ayer")) +
   scale_y_log10() +
   theme_bw()
 
@@ -768,7 +772,7 @@ Interestingly, if we plot the correlation between these two quantities we observ
 spatial_data_filtered |> 
   ggplot(aes(subsets_mito_percent, sum_gene)) + 
   geom_point(aes(color = spatialLIBD), size=0.2) +  
-  scale_color_manual(values = libd_layer_colors |> str_remove("ayer")) +
+  scale_color_manual(values = libd_layer_colors |>    str_remove("ayer")) +
   geom_smooth(method="lm") + 
   facet_wrap(~spatialLIBD) + 
   scale_y_log10() +
@@ -794,13 +798,25 @@ 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 2.3**
+**Excercise 2.3 (assisted)**
 
 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.
 
-We assume that the cells we filtered as non-alive or damaged, characterised by being reached uniquely for mitochondrial, genes, and genes, linked to up ptosis. it is good practice to check these assumption. This exercise aims to estimate what genes are differentially expressed between filtered and unfiltered cells. Then visualise the results
+We assume that the cells we filtered as non-alive or damaged, characterised by being enriched uniquely for mitochondrial, genes, and genes, linked to up apoptosis. It is good practice to check these assumption. This exercise aims to estimate what genes are differentially expressed between filtered and unfiltered cells. Then visualise the results.
+
+Use `tidyomic`/`tidyverse` tools to label dead cells and perform differential expression within each region. Some of the comments you can use are: `mutate`, `nest`, `map`, `aggregate_cells`, `tidybulk:::test_differential_abundance`, 
+
+A hist:
+
+- spatial_data |> 
+  mutate(
+    dead = ...
+
+- Aggregate by sample, dead status, ad annotated region
+
+- `nest` by annotated region
 
-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`.
+- use `map` to test DE
 :::
 
 ::: {.note}
@@ -811,7 +827,6 @@ Inspired by our audience, let's try to use `tidyomics` to identify potential Amy
 Amyloid plaques are extracellular deposits primarily composed of aggregated amyloid-beta (Aβ) peptides. They are a hallmark of Alzheimer's disease (AD) and are also found in certain other neurodegenerative conditions.
 
 Amyloid plaques can be found in the brains of mice, particularly in transgenic mouse models that are engineered to develop Alzheimer's disease-like pathology. 
-
 Although amyloid plaques themselves are extracellular, the presence and formation of these plaques are associated with specific gene expression changes in the surrounding and involved cells. These gene markers are indicative of the processes that contribute to amyloid plaque formation, as well as the cellular response to these plaques ([Ranman et al., 2021](https://molecularneurodegeneration.biomedcentral.com/articles/10.1186/s13024-021-00465-0).)
 
 ```{r}
@@ -822,12 +837,14 @@ rownames(spatial_data) = rowData(spatial_data)$gene_name
 ```
 
 The excercise includes
+
 - Join the features
+
 - Rescaling
+
 - Summarising signature (sum), `mutate()`
-- Plotting colousing by the signature
 
-# Plotting 
+- Plotting colousing by the signature
 :::