Seurat v5 Skill

Description

--- name: seurat-skill description: Comprehensive Seurat v5 (R) guide for single-cell RNA-seq and multimodal analysis. Covers installation, standard workflows (Normalize/SCTransform), clustering, integration (CCA/RPCA/Harmony), differential expression (FindMarkers/FindAllMarkers), visualization (DimPlot/FeaturePlot/VlnPlot/DoHeatmap), spatial transcriptomics (Visium/Visium HD/MERFISH/Slide-seq), CITE-seq, ATAC-seq, WNN, cell cycle regression, hashing/demultiplexing, sketch analysis, BPCells on-disk, pseudobulk, and format conversion. Use this skill whenever writing, debugging, or reviewing Seurat R code, building scRNA-seq pipelines, or looking up Seurat syntax, even for simple questions. homepage: https://github.com/Agents365-ai/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 `<this-skill-dir>/.last_update` exists and is less than 24 hours old, skip this step entirely. 2. Otherwise, fetch the latest tag from upstream: ```bash git -C <this-skill-dir> ls-remote --tags origin 'v*' 2>/dev/null \ | awk '{print $2}' | sed 's|refs/tags/||' \ | sort -V | tail -1 ``` 3. 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 "A newer version of this skill is available: vX.Y.Z → vA.B.C. Want me to `git pull`?"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 <this-skill-dir> pull --ff-only`. Refresh `.last_update` either way so the prompt doesn't repeat for 24 hours. 4. If upstream is the same or older, refresh `.last_update` silently and continue. 5. 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 ```r 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 ```r 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 ```r # 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 ```r 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 ```r 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 ```r 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 ```r bulk = AggregateExpression(obj, group.by = c("celltype", "sample"), return.seurat = TRUE) ``` ### Multi-Assay (CITE-seq) ```r 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 ```r obj = Load10X_Spatial(data.dir = "path/to/spaceranger/outs") SpatialDimPlot(obj) SpatialFeaturePlot(obj, features = "MS4A1") ``` ### Format Conversion ```r # 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) ```

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