add vignettes

tidyomics · Sep 16, 2024 · e8e848d · e8e848d
1 parent 70f35bc
commit e8e848d
Show file tree

Hide file tree

Showing 17 changed files with 771 additions and 25 deletions.
diff --git a/.gitignore b/.gitignore
@@ -7,3 +7,4 @@ TidyGenomicsTranscriptomicsWorkshop_bioc2023.Rproj
 .DS_Store
 /doc/
 /Meta/
+tidyomicsWorkshop.Rproj
diff --git a/DESCRIPTION b/DESCRIPTION
@@ -33,6 +33,7 @@ Imports:
     tidyseurat,
     SpatialExperiment,
     tidySpatialExperiment,
+    plyranges,
     stats,
     utils,
     tibble,
@@ -63,7 +64,8 @@ Suggests:
     pkgdown
 Remotes:
     satijalab/seurat-wrappers,
-    william-hutchison/tidySpatialExperiment@ECCB2024
+    william-hutchison/tidySpatialExperiment@ECCB2024,
+    stemangiola/tidygate@ECCB2024
 Biarch: true
 URL: https://tidy-biology.github.io/TidyGenomicsTranscriptomicsWorkshop
 BugReports: https://github.com/tidy-biology/TidyGenomicsTranscriptomicsWorkshop/issues/new/choose

diff --git a/data/gate_sce_obj.rda b/data/gate_sce_obj.rda
diff --git a/data/gate_seurat_obj.rda b/data/gate_seurat_obj.rda
diff --git a/data/pbmc_h3k4me3_hg38.rda b/data/pbmc_h3k4me3_hg38.rda
diff --git a/data/tidygate_env_gates.rda b/data/tidygate_env_gates.rda
diff --git a/inst/images/tidySPE_gate.png b/inst/images/tidySPE_gate.png
diff --git a/inst/images/tidyomics.png b/inst/images/tidyomics.png
diff --git a/man/gate_seurat_obj.Rd b/man/gate_seurat_obj.Rd
diff --git a/man/seurat_obj.Rd b/man/seurat_obj.Rd
diff --git a/man/seurat_obj_UMAP3.Rd b/man/seurat_obj_UMAP3.Rd
diff --git a/vignettes/pseudobulk_transcriptomics.Rmd b/vignettes/pseudobulk_transcriptomics.Rmd
@@ -134,7 +134,7 @@ To explore the grouping, we can use tidyverse `slice` to choose a row (cell_type
 
 ```{r pseudobulk3}
 pseudo_bulk_nested |>
-	slice(1) |>
+	dplyr::slice(1) |>
 	pull(grouped_summarized_experiment)
 ```
 
@@ -172,7 +172,7 @@ If we pull out the SummarizedExperiment object for the first cell type, as befor
 
 ```{r pseudobulk6}
 pseudo_bulk_nested |>
-	slice(1) |>
+	dplyr::slice(1) |>
 	pull(grouped_summarized_experiment)
 ```
 

diff --git a/vignettes/single_cell_bioconductor_transcriptomics.Rmd b/vignettes/single_cell_bioconductor_transcriptomics.Rmd
@@ -60,7 +60,6 @@ frameborder="0">
 # Load packages
 library(SingleCellExperiment)
 library(ggplot2)
-library(plotly)
 library(dplyr)
 library(colorspace)
 library(dittoSeq)
@@ -333,6 +332,7 @@ single_cell_object |>
 We'll demonstrate creating a 3D plot using some data that has 3 UMAP dimensions. This is a fantastic way to visualise both reduced dimensions and metadata in the same representation. 
 
 ```{r umap plot 2, message = FALSE, warning = FALSE}
+library(tidySingleCellExperiment)
 pbmc <- tidyomicsWorkshop::sce_obj_UMAP3
 
 pbmc |>
@@ -343,7 +343,7 @@ pbmc |>
     color = ~cell_type,
     colors = dittoSeq::dittoColors()
   ) %>%
-  add_markers(size = I(1))
+  plotly::add_markers(size = I(1))
 ```
 
 ## Exercises
@@ -372,7 +372,7 @@ First let's have a look to the cell types that constitute this dataset
 
 ```{r nest SingleCellExperiment count}
 sce_obj |> 
-  count(cell_type)
+  dplyr::count(cell_type)
 ```
 
 Let's group the cells based on cell identity using `nest`
@@ -390,7 +390,7 @@ Let's see what the first element of the Surat column looks like
 
 ```{r nest sce 2}
 sce_obj_nested |> 
-  slice(1) |> 
+  dplyr::slice(1) |> 
   pull(sce)
 ```
 
@@ -440,7 +440,7 @@ Let's have a look to the first heatmap
 
 ```{r nest sce heatmap 2, fig.width=8, fig.height=8}
 sce_obj_nested |> 
-  slice(1) |> 
+  dplyr::slice(1) |> 
   pull(umap)
 ```
 
@@ -453,7 +453,7 @@ sce_obj |>
   nest(sce = -cell_type) |> 
   
   # Filter for sample with more than 30 cells
-  mutate(sce = map(sce, ~ .x |> add_count(sample) |> filter(n > 50))) |>
+  mutate(sce = map(sce, ~ .x |> add_count(sample) |> dplyr::filter(n > 50))) |>
   filter(map_int(sce, ncol) > 0) |>
 
   # Select significant genes
@@ -487,7 +487,6 @@ sce_obj |>
 - Answer this question avoiding to save temporary variables, and using the function add_count to count the cells (before nesting), and then filter
 - Answer this question avoiding to save temporary variables, and using the function map_int to count the cells (after nesting), and the filter
 
-
 **Session Information**
 
 ```{r}

diff --git a/vignettes/single_cell_seurat_transcriptomics.Rmd b/vignettes/single_cell_seurat_transcriptomics.Rmd
@@ -453,7 +453,7 @@ First let's have a look to the cell types that constitute this dataset
 
 ```{r nest seurat count}
 seurat_obj |> 
-  count(cell_type)
+  dplyr::count(cell_type)
 ```
 
 Let's group the cells based on cell identity using `nest`
@@ -474,7 +474,7 @@ Let's see what the first element of the Surat column looks like
 
 ```{r nest seurat 2}
 seurat_obj_nested |> 
-  slice(1) |> 
+  dplyr::slice(1) |> 
   pull(seurat)
 ```
 Now, let's perform a differential gene-transcript abundance analysis between the two conditions for each cell type.
@@ -521,7 +521,7 @@ Let's have a look to the first heatmap
 
 ```{r nest seurat heatmap 2, fig.width=8, fig.height=8}
 seurat_obj_nested |> 
-  slice(1) |> 
+  dplyr::slice(1) |> 
   pull(heatmap)
 ```
 

diff --git a/vignettes/solutions_transcriptomics.Rmd b/vignettes/solutions_transcriptomics.Rmd
@@ -38,7 +38,7 @@ seurat_obj |>
 
   mutate(gamma_delta = signature_score > 0.7) |>
   
-  count(gamma_delta) |> 
+  dplyr::count(gamma_delta) |> 
   summarise(proportion = n/sum(n))
 ```
 
@@ -50,5 +50,5 @@ There is a cluster of cells characterised by a low RNA output (nCount_RNA < 100)
 ```{r}
 seurat_obj |>
     filter(nCount_RNA < 100) %>% 
-    count(cell_type)
+    dplyr::count(cell_type)
 ```