testing

vignettes/integration_introduction.Rmd

@@ -146,11 +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"))
+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()
+DotPlot(immune.combined, features = markers.to.plot, cols = c('blue', 'red'), dot.scale = 8, split.by = "stim") + RotatedAxis()
 ```
 
 ```{r save.img, include = FALSE}