Seurat Skill

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.

Update check

Throttle to one check per 24 hours per installation; never mutate the skill directory without explicit user consent.

1. If /.last_update exists and is less than 24 hours old, skip this step entirely.

1. Otherwise, fetch the latest tag from upstream:

``bash git -C ls-remote --tags origin 'v*' 2>/dev/null \ | awk '{print $2}' | sed 's|refs/tags/||' \ | sort -V | tail -1 ``

1. Compare with this skill's metadata.version from the frontmatter. If the upstream tag is strictly newer (semver), tell the user one line and ask:

> "A newer version of this skill is available: vX.Y.Z → vA.B.C. Want me to git pull?"

If they say yes, run git -C pull --ff-only. Refresh .last_update either way so the prompt doesn't repeat for 24 hours.

1. If upstream is the same or older, refresh .last_update silently and continue.

1. On any failure (offline, not a git checkout — e.g. ClawHub-installed copy, read-only path, no permission), swallow the error silently and continue with the user's task. Do not mention the failure.

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  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)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Agents365-ai
• Source: Agents365-ai/seurat-skill
• License: MIT

Install and usage instructions live in the source repository linked above.