seuratpreparing
Load, prepare, and apply quality control (QC) to single-cell RNA-seq data using Seurat. Performs data loading, QC filtering, normalization, and multi-sample integration. This is a core preprocessing process that prepares Seurat objects for downstream clustering and analysis.
npx skills add majiayu000/claude-skill-registry --skill seuratpreparing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Conveys a Seurat-based preprocessing workflow that loads scRNA-seq data from various formats, applies quality control filters, performs normalization (either standard or SCTransform), identifies variable features, scales data, runs PCA, and optionally integrates multiple samples using methods like harmony or rpca. It supports optional doublet detection (DoubletFinder or scDblFinder) and can generate QC plots. It also provides per-sample or per-study configuration for QC and feature choices.
How it works
- Data loading from multiple input formats (10x, h5, loom, or pre-loaded Seurat objects) and merging samples into one Seurat object.
- Environment-driven processing path:
- Standard workflow (use_sct = false): NormalizeData, FindVariableFeatures, ScaleData, RunPCA, followed by optional integration via IntegrateLayers (methods include harmony, rpca, etc.).
- SCTransform workflow (use_sct = true): SCTransform replaces the standard normalization and feature selection steps, outputs a SCT assay, with options to regress variables and control memory usage.
- QC controls: cell_qc and gene_qc expressions determine filtering criteria, with per-sample overrides possible.
- Integration: supports various methods (harmony, rpca, etc.) and specifies base reduction (orig.reduction) and related settings.
- Doublet detection: can activate DoubletFinder or scDblFinder with configurable parameters such as PCs, doublet rate, and cores.
- Outputs are prepared for downstream steps (SeuratClustering, SeuratClusteringOfAllCells).
When to use it
- Essential for RNA-based immunopipe analyses requiring QC, normalization, and integration across multiple samples.
- Use when data are not already loaded as Seurat objects, or when multi-sample batch correction is required.
- Enable per-sample QC customization and optional doublet detection as part of preprocessing.
What it can touch
- External inputs: SampleInfo metafile paths and data files (RNAData entries).
- Seurat objects created or loaded during the process, and an optional SCT assay when use_sct is true.
- Integration layers and reduction objects (pca) used for alignment across samples.
- Doublet detection modules (DoubletFinder or scDblFinder) when enabled.
Caveats
- License and usage terms are specified in the skill metadata (MIT).
- Behavior depends on exact environment settings (ncores, min_cells, min_features, qc expressions).
- Some configurations may require additional dependencies (Seurat vX, DoubletFinder, scDblFinder) not stated here.
# SeuratPreparing Process Configuration ## Purpose Load, prepare, and apply quality control (QC) to single-cell RNA-seq data using Seurat. Performs data loading, QC filtering, normalization, and multi-sample integration. This is a core preprocessing process that prepares Seurat objects for downstream clustering and analysis. ## When to Use - **Essential process** for all RNA-based immunopipe analyses (TCR and non-TCR routes) - Required unless loading from already-prepared Seurat objects (see `LoadingRNAFromSeurat` process) - Multi-sample datasets requiring batch correction and integration - Data requiring doublet detection and removal - Raw scRNA-seq data needing QC, normalization, and integration ### Data Requirements - Input data paths specified in `SampleInfo` metafile (`RNAData` column) - Supported formats: 10x Genomics output (matrix.mtx, barcodes.tsv, features.tsv), h5 files, loom files, or pre-loaded Seurat objects (RDS/qs2) - Each sample loaded individually and merged into one Seurat object ### Dependencies - **Upstream**: `SampleInfo` (provides metadata) OR `LoadingRNAFromSeurat` (provides prepared Seurat object) - **Downstream**: `SeuratClustering`, `SeuratClusteringOfAll
- Purpose
- When to Use
- Data Requirements
- Dependencies
- Configuration Structure
- Process Enablement
- Input Specification
- Environment Variables
- External References
- Seurat Normalization Functions
- Integration Methods
- Doublet Detection
- Configuration Examples
- Minimal Configuration (Single Sample, No Integration)
What does the seuratpreparing skill do?
Load, prepare, and apply quality control (QC) to single-cell RNA-seq data using Seurat. Performs data loading, QC filtering, normalization, and multi-sample integration. This is a core preprocessing process that prepares Seurat objects for downstream clustering and analysis.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill seuratpreparing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.