fix dependencies

tidyomics · Sep 25, 2024 · 1335441 · 1335441
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commit 1335441
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Showing 9 changed files with 61 additions and 12 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
@@ -21,7 +21,7 @@ Encoding: UTF-8
 LazyData: true
 LazyDataCompression: xz
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.3
+RoxygenNote: 7.3.2
 Depends:
     Biobase,
     R (>= 4.2.0)

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
@@ -335,7 +335,7 @@ We'll demonstrate creating a 3D plot using some data that has 3 UMAP dimensions.
 pbmc <- tidyomicsWorkshop::sce_obj_UMAP3
 
 pbmc |>
-  plot_ly(
+  ttservice::plot_ly(
     x = ~`UMAP_1`,
     y = ~`UMAP_2`,
     z = ~`UMAP_3`,
@@ -371,7 +371,7 @@ 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`
@@ -389,7 +389,7 @@ 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)
 ```
 
@@ -439,7 +439,7 @@ 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)
 ```
 

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)
 ```