testing

grabadam-cal · Jan 26, 2021 · 607304f · 607304f
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Showing 2 changed files with 8 additions and 9 deletions.
diff --git a/azure-pipelines.yml b/azure-pipelines.yml
@@ -27,9 +27,9 @@ jobs:
             mkdir output/images
             mkdir output/timings
             Rscript -e "pkgdown::init_site()"
-            Rscript -e "pkgdown::build_article('pbmc3k_tutorial')"
-            ls vignettes | grep -v 'pbmc3k_tutorial.Rmd' | grep -v 'assets' | grep -v 'vignettes.yaml' | cut -f 1 -d '.' | parallel -j4 "Rscript -e 'pkgdown::build_article(\"{}\")'"
-            Rscript -e "pkgdown::build_site(lazy = TRUE)"
+            Rscript -e "pkgdown::build_article('integration_introduction')"
+            # ls vignettes | grep -v 'pbmc3k_tutorial.Rmd' | grep -v 'assets' | grep -v 'vignettes.yaml' | cut -f 1 -d '.' | parallel -j4 "Rscript -e 'pkgdown::build_article(\"{}\")'"
+            # Rscript -e "pkgdown::build_site(lazy = TRUE)"
             cp vignettes/assets/* docs/articles/assets/
         displayName: 'Build pkgdown site'
       - task: CopyFiles@2

diff --git a/vignettes/integration_introduction.Rmd b/vignettes/integration_introduction.Rmd
@@ -146,12 +146,11 @@ DimPlot(immune.combined, label = TRUE)
 The `DotPlot()` function with the `split.by` parameter can be useful for viewing conserved cell type markers across conditions, showing both the expression level and the percentage of cells in a cluster expressing any given gene. Here we plot 2-3 strong marker genes for each of our 13 clusters.
 
 ```{r splitdotplot, fig.height = 10}
-Idents(immune.combined) <- factor(
-  Idents(immune.combined),
-  levels = c("Mono/Mk Doublets", "pDC", "Eryth","Mk", "DC", "CD14 Mono", "CD16 Mono", "B Activated", "B", "CD8 T", "NK", "T activated", "CD4 Naive T", "CD4 Memory T"))
-markers.to.plot <- c("CD3D","CREM","HSPH1","SELL","GIMAP5","CACYBP","GNLY","NKG7","CCL5","CD8A","MS4A1","CD79A","MIR155HG","NME1","FCGR3A","VMO1",
-                     "CCL2","S100A9","HLA-DQA1","GPR183","PPBP","GNG11","HBA2","HBB","TSPAN13","IL3RA","IGJ")
-DotPlot(immune.combined, features = markers.to.plot, cols = c('blue', 'red'), dot.scale = 8, split.by = "stim") + RotatedAxis()
+#Idents(immune.combined) <- factor(
+#  Idents(immune.combined),
+#  levels = c("Mono/Mk Doublets", "pDC", "Eryth","Mk", "DC", "CD14 Mono", "CD16 Mono", "B Activated", "B", "CD8 T", "NK", "T activated", "CD4 Naive T", "CD4 Memory T"))
+#markers.to.plot <- c("CD3D","CREM","HSPH1","SELL","GIMAP5","CACYBP","GNLY","NKG7","CCL5","CD8A","MS4A1","CD79A","MIR155HG","NME1","FCGR3A","VMO1","CCL2","S100A9","HLA-DQA1","GPR183","PPBP","GNG11","HBA2","HBB","TSPAN13","IL3RA","IGJ")
+#DotPlot(immune.combined, features = markers.to.plot, cols = c('blue', 'red'), dot.scale = 8, split.by = "stim") + RotatedAxis()
 ```
 
 ```{r save.img, include = FALSE}