merge

vignettes/integration_introduction.Rmd

@@ -86,12 +86,12 @@ features <- SelectIntegrationFeatures(object.list = ifnb.list)
 We then identify anchors using the `FindIntegrationAnchors()` function, which takes a list of Seurat objects as input, and use these anchors to integrate the two datasets together with `IntegrateData()`.
 
 ```{r find.anchors}
-immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features, dims = 1:20)
+immune.anchors <- FindIntegrationAnchors(object.list = ifnb.list, anchor.features = features)
 ```
 
 ```{r integrate.data}
 # this command creates an 'integrated' data assay
-immune.combined <- IntegrateData(anchorset = immune.anchors, dims = 1:20)
+immune.combined <- IntegrateData(anchorset = immune.anchors)
 ```
 
 ## Perform an integrated analysis
@@ -106,8 +106,8 @@ DefaultAssay(immune.combined) <- "integrated"
 # Run the standard workflow for visualization and clustering
 immune.combined <- ScaleData(immune.combined, verbose = FALSE)
 immune.combined <- RunPCA(immune.combined, npcs = 30, verbose = FALSE)
-immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:20)
-immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:20)
+immune.combined <- RunUMAP(immune.combined, reduction = "pca", dims = 1:30)
+immune.combined <- FindNeighbors(immune.combined, reduction = "pca", dims = 1:30)
 immune.combined <- FindClusters(immune.combined, resolution = 0.5)
 ```
 
@@ -139,18 +139,18 @@ We can explore these marker genes for each cluster and use them to annotate our
 
 ```{r annotate, results = 'hide', message=FALSE, fig.height = 8}
 FeaturePlot(immune.combined, features = c("CD3D", "SELL", "CREM", "CD8A", "GNLY", "CD79A", "FCGR3A", "CCL2", "PPBP"), min.cutoff = "q9")
-immune.combined <- RenameIdents(immune.combined, "0" = "CD14 Mono", "1" = "CD4 Naive T", "2" = "CD4 Memory T", "3" = "CD16 Mono", "4" = "B", "5" = "CD8 T", "6" = "NK" , "7" = "T activated", "8" = "DC", "9" = "B Activated", "10" = "Mk", "11" = "pDC", "12" = "Eryth", "13" = "Mono/Mk Doublets")
+immune.combined <- RenameIdents(immune.combined, "0" = "CD14 Mono", "1" = "CD4 Naive T", "2" = "CD4 Memory T", "3" = "CD16 Mono", "4" = "B", "5" = "CD8 T", "6" = "NK" , "7" = "T activated", "8" = "DC", "9" = "B Activated", "10" = "Mk", "11" = "pDC", "12" = "Eryth", "13" = "Mono/Mk Doublets", "14" = "HSPC")
 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.
+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 14 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")
+  levels = c("HSPC", "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","PRSS57")
 DotPlot(immune.combined, features = markers.to.plot, cols = c('blue', 'red'), dot.scale = 8, split.by = "stim") + RotatedAxis()
 ```