Seurat v5 Skill

Guide for single-cell and multimodal data analysis with Seurat v5 in R. This SKILL.md contains the essential quick reference. Detailed workflows are in references/ files, read the relevant one when you need step-by-step code for a specific analysis.

Auto-Update (run on skill load)

When this skill is loaded, immediately check <skill_directory>/.last_update. If the file is missing or older than 24 hours, run this before doing anything else:

git -C <skill_directory> pull --ff-only 2>/dev/null && date +%s > <skill_directory>/.last_update

If the pull fails (offline, conflict, etc.), ignore the error and continue normally. Do not mention the update to the user unless they ask.

Reference Files

Read the relevant reference file when the user's task matches a topic:

Topic	File	When to read
Installation	references/install.md	Installing Seurat, dependencies, Docker
PBMC 3K Tutorial	references/pbmc3k-tutorial.md	Standard scRNA-seq workflow end-to-end
Getting Started	references/get-started.md	Seurat v5 new features, BPCells
Essential Commands	references/essential-commands.md	Object access, metadata, identity, layers
Visualization	references/visualization.md	Plotting: DimPlot, FeaturePlot, VlnPlot, DoHeatmap
Advanced Plots	references/plotting-advanced.md	Interactive, linked, polygon, spatial image, cluster tree plots
Differential Expression	references/de-vignette.md	FindMarkers, FindAllMarkers, DE tests
Integration Intro	references/integration-introduction.md	When and why to integrate
Integration	references/integration.md	CCA, RPCA, Harmony, scVI integration
Integration RPCA	references/integration-rpca.md	Reciprocal PCA integration
Integration Mapping	references/integration-mapping.md	Label transfer, reference mapping
Integration Large	references/integration-large-datasets.md	Scalable integration, sketch-based
SCTransform	references/sctransform.md	SCTransform normalization workflow
SCTransform v2	references/sctransform-v2.md	Improved SCTransform (v2 regularization)
SCTransform Integration	references/sctransform-integration.md	Integration with SCTransform
Merge and Split	references/merge.md	Merging/splitting objects and layers
Cell Cycle	references/cell-cycle.md	Cell cycle scoring and regression
Multimodal (CITE-seq)	references/multimodal.md	Weighted nearest neighbor, CITE-seq
Multimodal Mapping	references/multimodal-reference-mapping.md	Reference mapping multimodal data
WNN	references/wnn.md	Weighted nearest neighbor analysis
Hashing	references/hashing.md	Cell hashing, HTODemux, demultiplexing
Mixscape	references/mixscape.md	Perturb-seq, CRISPR screen analysis
Spatial (Visium)	references/spatial.md	10x Visium spatial transcriptomics
Spatial (Other)	references/spatial-2.md	Slide-seq, MERFISH, STARmap
Visium HD	references/visiumhd.md	Visium HD high-resolution spatial
ATAC-seq	references/atacseq-integration.md	scATAC-seq and RNA+ATAC integration
Bridge Integration	references/integration-bridge.md	Cross-modality bridge integration
Sketch Analysis	references/sketch-analysis.md	Sketch-based analysis for large data
Advanced Clustering	references/advanced-clustering.md	Leiden, sub-clustering, spatial stats, identity management
BPCells	references/bpcells.md	On-disk matrices with BPCells
Data Loading	references/data-loading.md	Read10X, ReadMtx, Load10X_Spatial, ReadXenium, all Read*/Load*
Dim Reduction	references/dim-reduction.md	PCA, tSNE, UMAP, CCA, ICA, LDA, SPCA, projection methods
Interaction	references/interaction.md	Interactive data exploration
Conversion	references/conversion.md	Convert between Seurat/AnnData/loom/SCE
Parallelization	references/parallelization.md	future-based parallel processing
COVID Mapping	references/covid-sctmapping.md	SCTransform mapping example
ParseBio Sketch	references/parsebio-sketch.md	ParseBio data with sketch integration
Extensions	references/extensions.md	Signac, SeuratData, SeuratWrappers, Azimuth ecosystem
v4 to v5 Migration	references/v4-to-v5-migration.md	API changes, parameter renames, removed functions

Quick Reference

Standard Workflow

obj = CreateSeuratObject(counts = counts, project = "my_project", min.cells = 3, min.features = 200)
obj[["percent.mt"]] = PercentageFeatureSet(obj, pattern = "^MT-")
obj = subset(obj, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)

# Option A: Log-normalize
obj = NormalizeData(obj)
obj = FindVariableFeatures(obj)
obj = ScaleData(obj)

# Option B: SCTransform (replaces the 3 steps above)
obj = SCTransform(obj)

obj = RunPCA(obj)
obj = FindNeighbors(obj, dims = 1:30)
obj = FindClusters(obj, resolution = 0.5)
obj = RunUMAP(obj, dims = 1:30)
DimPlot(obj, reduction = "umap", label = TRUE)

Differential Expression

markers = FindAllMarkers(obj, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
markers = FindMarkers(obj, ident.1 = "cluster1", ident.2 = "cluster2")
markers = FindMarkers(obj, ident.1 = "cluster1", test.use = "DESeq2", slot = "counts")

Integration

# v5 layer-based integration
obj[["RNA"]] = split(obj[["RNA"]], f = obj$batch)
obj = NormalizeData(obj)
obj = FindVariableFeatures(obj)
obj = ScaleData(obj)
obj = RunPCA(obj)
obj = IntegrateLayers(obj, method = CCAIntegration, orig.reduction = "pca",
  new.reduction = "integrated.cca")
# Also: RPCAIntegration, HarmonyIntegration, FastMNNIntegration, scVIIntegration
obj = FindNeighbors(obj, reduction = "integrated.cca", dims = 1:30)
obj = FindClusters(obj, resolution = 0.5)
obj = RunUMAP(obj, reduction = "integrated.cca", dims = 1:30)
obj[["RNA"]] = JoinLayers(obj[["RNA"]])

Subsetting

subset(obj, idents = "B")                              # by cluster identity
subset(obj, idents = c("B", "NK"), invert = TRUE)      # exclude clusters
subset(obj, subset = MS4A1 > 2.5)                      # by expression
subset(obj, subset = condition == "treated")            # by metadata
subset(obj, downsample = 100)                           # downsample per cluster

Key Visualization

DimPlot(obj, reduction = "umap", group.by = "celltype", label = TRUE)
FeaturePlot(obj, features = c("CD3D", "MS4A1", "CD8A"))
VlnPlot(obj, features = c("CD3D", "MS4A1"), group.by = "celltype")
DotPlot(obj, features = c("CD3D", "MS4A1", "CD14"), group.by = "celltype")
DoHeatmap(obj, features = top_markers) + NoLegend()
FeatureScatter(obj, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")

Object Access

Cells(obj)                    # cell barcodes
Features(obj)                 # gene names
Idents(obj)                   # active identities
obj[[]]                       # metadata data.frame
obj$nCount_RNA                # single metadata column
Embeddings(obj, "pca")        # PCA embeddings
obj[["RNA"]]$counts           # raw counts (v5 layer)
DefaultAssay(obj)             # current default assay
Layers(obj)                   # list layers
VariableFeatures(obj)         # HVGs
FetchData(obj, vars = c("UMAP_1", "UMAP_2", "CD3D"))  # mixed data access

Pseudobulk

bulk = AggregateExpression(obj, group.by = c("celltype", "sample"), return.seurat = TRUE)

Multi-Assay (CITE-seq)

obj[["ADT"]] = CreateAssayObject(counts = adt.counts)
obj = NormalizeData(obj, assay = "ADT", normalization.method = "CLR", margin = 2)
DefaultAssay(obj) = "ADT"
FeaturePlot(obj, features = "adt_CD3")

Spatial

obj = Load10X_Spatial(data.dir = "path/to/spaceranger/outs")
SpatialDimPlot(obj)
SpatialFeaturePlot(obj, features = "MS4A1")

Format Conversion

# Seurat to AnnData
library(SeuratDisk)
SaveH5Seurat(obj, filename = "obj.h5Seurat")
Convert("obj.h5Seurat", dest = "h5ad")

# Seurat to SingleCellExperiment
sce = as.SingleCellExperiment(obj)

# SingleCellExperiment to Seurat
obj = as.Seurat(sce)