vignettes/essential_commands.Rmd
essential_commands.RmdSeurat workflow
pbmc <- NormalizeData(object = pbmc)
pbmc <- FindVariableFeatures(object = pbmc)
pbmc <- ScaleData(object = pbmc)
pbmc <- RunPCA(object = pbmc)
pbmc <- FindNeighbors(object = pbmc, dims = 1:30)
pbmc <- FindClusters(object = pbmc)
pbmc <- RunUMAP(object = pbmc, dims = 1:30)
DimPlot(object = pbmc, reduction = "umap")
pbmc <- SCTransform(object = pbmc)
pbmc <- RunPCA(object = pbmc)
pbmc <- FindNeighbors(object = pbmc, dims = 1:30)
pbmc <- FindClusters(object = pbmc)
pbmc <- RunUMAP(object = pbmc, dims = 1:30)
DimPlot(object = pbmc, reduction = "umap")
# note that you can chain multiple commands together with %>%
pbmc <- SCTransform(pbmc) %>%
RunPCA() %>%
FindNeighbors(dims = 1:30) %>%
FindClusters() %>%
RunUMAP(dims = 1:30)Seurat Object Data Access
# Get cell and feature names, and total numbers We show multiple ways to get the same output cell names
colnames(pbmc)
Cells(pbmc)
# feature names
Features(pbmc)
rownames(pbmc)
# number of cells/features
num_cells <- ncol(pbmc)
num_features <- nrow(pbmc)
# List of object layers
Layers(pbmc)
# working with multimodal objects list assays
Assays(cbmc)
# Assay-specific features (genes/ADT)
Features(cbmc[["RNA"]])
Features(cbmc[["ADT"]])
# Variable feature names
VariableFeatures(pbmc)
# Set variable features
VariableFeatures(cbmc) <- var.gene.names
# set for a specific assay
VariableFeatures(cbmc[["ADT"]]) <- var.gene.names
# Setting and retrieving cell identities
# Set identity classes to an existing column in meta data
Idents(object = pbmc) <- "seurat_annotations"
# View cell identities, get summary table
Idents(pbmc)
table(Idents(pbmc))
# Set identity to CD4 T cells for all cells
Idents(pbmc) <- "CD4 T cells"
# Set for a selected group of cells
pbmc.cells <- Cells(pbmc)
Idents(object = pbmc, cells = pbmc.cells[1:10]) <- "CD4 T cells"
# Get cell identity classes
Idents(object = pbmc)
levels(x = pbmc)
# Stash cell identity classes in metadata
pbmc[["old.ident"]] <- Idents(object = pbmc)
pbmc <- StashIdent(object = pbmc, save.name = "old.ident")
# Rename identity classes
pbmc <- RenameIdents(object = pbmc, `CD4 T cells` = "T Helper cells")
# View metadata data frame, stored in object@meta.data
pbmc[[]]
# Retrieve specific values from the metadata
pbmc$nCount_RNA
pbmc[[c("percent.mito", "nFeature_RNA")]]
# Add metadata, see ?AddMetaData
random_group_labels <- sample(x = c("g1", "g2"), size = ncol(x = pbmc), replace = TRUE)
pbmc$groups <- random_group_labels
# Retrieve data in an expression matrix RNA counts matrix
pbmc[["RNA"]]$counts
# Alternate accessor function with the same result
LayerData(pbmc, assay = "RNA", layer = "counts")
# GetAssayData from Seurat v4 is still supported
GetAssayData(object = pbmc, assay = "RNA", slot = "counts")
# ADT counts matrix (multimodal object)
cbmc[["ADT"]]$counts
# Set expression data assume new.data is a new expression matrix
pbmc[["RNA"]]$counts <- new.data
# Alternate setter function with the same result
LayerData(pbmc, assay = "RNA", layer = "counts") <- new.data
# SetAssayData from Seurat v4 is still supported
pbmc <- SetAssayData(object = pbmc, slot = "counts", new.data = new.data)
# Get cell embeddings and feature loadings stored on pbmc[['pca']]@cell.embeddings
Embeddings(pbmc, reduction = "pca")
# stored in pbmc[['pca]]@feature.loadings
Loadings(pbmc, reduction = "pca")
# Create custom dimensional reduction loadings matrix is optional
new_reduction <- CreateDimReducObject(embeddings = new.embeddings, loadings = new.loadings, key = "custom_pca")
pbmc[["custom_pca"]] <- new_reduction
# Subset Seurat object based on identity class, also see ?SubsetData
subset(x = pbmc, idents = "B")
subset(x = pbmc, idents = c("Naive CD4 T", "CD8 T"), invert = TRUE)
# Subset on the expression level of a gene/feature
subset(x = pbmc, subset = MS4A1 > 2.5)
# Subset on a combination of criteria
subset(x = pbmc, subset = MS4A1 > 2.5 & PC_1 > 5)
subset(x = pbmc, subset = MS4A1 > 2.5, idents = "B")
# Subset on a value in the object meta data
subset(x = pbmc, subset = groups == "g1")
# Downsample the number of cells per identity class
subset(x = pbmc, downsample = 100)
# In Seurat v5, users can now split in object directly into different layers keeps expression data in one object, but
# splits multiple samples into layers can proceed directly to integration workflow after splitting layers
ifnb[["RNA"]] <- split(ifnb[["RNA"]], f = ifnb$stim)
Layers(ifnb)
# If desired, for example after intergation, the layers can be joined together again
ifnb <- JoinLayers(ifnb)
# In line with prior workflows, you can also into split your object into a list of multiple objects based on a metadata
# column creates a list of two objects
ifnb_list <- SplitObject(ifnb, split.by = "stim")
ifnb_list$CTRL
ifnb_list$STIMIn Seurat v5, merging creates a single object, but keeps the expression information split into different layers for integration. If not proceeding with integration, rejoin the layers after merging.
# Merge two Seurat objects
merged_obj <- merge(x = ifnb_list$CTRL, y = ifnb_list$STIM)
merged_obj[["RNA"]] <- JoinLayers(merged_obj)
# Example to merge more than two Seurat objects
merge(x = pbmc1, y = list(pbmc2, pbmc3))See introduction to integration for more information.
merged_obj <- merge(x = ifnb_list$CTRL, y = ifnb_list$STIM)
merged_obj <- NormalizeData(merged_obj)
merged_obj <- FindVariableFeatures(merged_obj)
merged_obj <- ScaleData(merged_obj)
merged_obj <- RunPCA(merged_obj)
merged_obj <- IntegrateLayers(object = obj, method = RPCAIntegration, orig.reduction = "pca", new.reduction = "integrated.rpca",
verbose = FALSE)
# now that integration is complete, rejoin layers
merged_obj[["RNA"]] <- JoinLayers(merged_obj)See DE vignette for information on how to add the donor_id column to meta data.
# pseudobulk cells only by cell type
bulk <- AggregateExpression(ifnb, group.by = "seurat_annotations", return.seurat = TRUE)
Cells(bulk)
# pseudobulk cells by stimulation condition AND cell type
bulk <- AggregateExpression(ifnb, group.by = c("stim", "seurat_annotations"), return.seurat = TRUE)
Cells(bulk)
# pseudobulk cells by stimulation condition AND cell type AND donor
bulk <- AggregateExpression(ifnb, group.by = c("stim", "seurat_annotations", "donor_id"), return.seurat = TRUE)
Cells(bulk)Seurat
Seurat has a vast, ggplot2-based plotting library. All plotting functions will return a ggplot2 plot by default, allowing easy customization with ggplot2.
# Dimensional reduction plot
DimPlot(object = pbmc, reduction = "pca")
# Dimensional reduction plot, with cells colored by a quantitative feature Defaults to UMAP if available
FeaturePlot(object = pbmc, features = "MS4A1")
# Scatter plot across single cells
FeatureScatter(object = pbmc, feature1 = "MS4A1", feature2 = "PC_1")
FeatureScatter(object = pbmc, feature1 = "MS4A1", feature2 = "CD3D")
# Scatter plot across individual features, repleaces CellPlot
CellScatter(object = pbmc, cell1 = "AGTCTACTAGGGTG", cell2 = "CACAGATGGTTTCT")
VariableFeaturePlot(object = pbmc)
# Violin and Ridge plots
VlnPlot(object = pbmc, features = c("LYZ", "CCL5", "IL32"))
RidgePlot(object = pbmc, feature = c("LYZ", "CCL5", "IL32"))
# Heatmaps (visualize scale.data slot)
DimHeatmap(object = pbmc, reduction = "pca", cells = 200)
# standard workflow
pbmc <- ScaleData(pbmc, features = heatmap_markers)
DoHeatmap(object = pbmc, features = heatmap_markers)
# sctransform workflow
pbmc <- GetResidual(pbmc, features = heatmap_markers)
DoHeatmap(object = pbmc, features = heatmap_markers)
# heatmap with maximum of 100 cells per group
DoHeatmap(pbmc, heatmap_markers, cells = subset(pbmc, downsample = 100))
# New things to try! Note that plotting functions now return ggplot2 objects, so you can add themes, titles, and
# options onto them
VlnPlot(object = pbmc, features = "MS4A1", split.by = "groups")
DotPlot(object = pbmc, features = c("LYZ", "CCL5", "IL32"), split.by = "groups")
FeaturePlot(object = pbmc, features = c("MS4A1", "CD79A"), blend = TRUE)
DimPlot(object = pbmc) + DarkTheme()
DimPlot(object = pbmc) + labs(title = "2,700 PBMCs clustered using Seurat and viewed\non a two-dimensional UMAP")Seurat provides many prebuilt themes that can be added to ggplot2 plots for quick customization
| Theme | Function |
|---|---|
DarkTheme |
Set a black background with white text |
FontSize |
Set font sizes for various elements of a plot |
NoAxes |
Remove axes and axis text |
NoLegend |
Remove all legend elements |
RestoreLegend |
Restores a legend after removal |
RotatedAxis |
Rotates x-axis labels |
# Plotting helper functions work with ggplot2-based scatter plots, such as DimPlot, FeaturePlot, CellScatter, and
# FeatureScatter
plot <- DimPlot(object = pbmc) + NoLegend()
# HoverLocator replaces the former `do.hover` argument It can also show extra data throught the `information` argument,
# designed to work smoothly with FetchData
HoverLocator(plot = plot, information = FetchData(object = pbmc, vars = c("ident", "PC_1", "nFeature_RNA")))
# FeatureLocator replaces the former `do.identify`
select.cells <- FeatureLocator(plot = plot)
# Label points on a ggplot object
LabelPoints(plot = plot, points = TopCells(object = pbmc[["pca"]]), repel = TRUE)With Seurat, you can easily switch between different assays at the single cell level (such as ADT counts from CITE-seq, or integrated/batch-corrected data). Most functions now take an assay parameter, but you can set a Default Assay to avoid repetitive statements.
cbmc <- CreateSeuratObject(counts = cbmc.rna)
# Add ADT data
cbmc[["ADT"]] <- CreateAssayObject(counts = cbmc.adt)
# Run analyses by specifying the assay to use
NormalizeData(object = cbmc, assay = "RNA")
NormalizeData(object = cbmc, assay = "ADT", method = "CLR")
# Retrieve and set the default assay
DefaultAssay(object = cbmc)
DefaultAssay(object = cbmc) <- "ADT"
DefaultAssay(object = cbmc)
# Pull feature expression from both assays by using keys
FetchData(object = cbmc, vars = c("rna_CD3E", "adt_CD3"))
# Plot data from multiple assays using keys
FeatureScatter(object = cbmc, feature1 = "rna_CD3E", feature2 = "adt_CD3")