scrna-meta-analysis
Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use t
npx skills add majiayu000/claude-skill-registry --skill scrna-meta-analysis-ammawla-encode-toolkit --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
Gathers and analyzes multiple scRNA-seq datasets to perform a cross-study meta-analysis, harmonizing cell type annotations across studies, and constructing a unified tissue/cell-type atlas with reproducibility-focused quality assessment. It targets users who want to know which cell types exist in a tissue and which genes define them, by combining data across donors, labs, and platforms, following Mawla et al. 2019 framework. It supports batch correction (Harmony/Seurat), accounts for dropout and ambient contamination, and addresses platform-specific biases. It also guides integration strategy, QC, and downstream cell type annotation and harmonization across datasets.
How it works
- Step 1 (Find All Available scRNA-seq Experiments): Identify ENCODE scRNA-seq datasets for a target tissue, compile a summary table of experiments, platforms, labs, donors, and organisms.
- Step 2 (Quality-Gate Each Experiment): For each dataset, evaluate QC metrics such as Genes/cell, UMI/cell, mitochondrial percentage, doublet rate, and mapping rate; exclude datasets with errors or poor metrics; track included experiments.
- Step 3 (Download Gene Quantification Files): Retrieve per-dataset gene quantifications in TSV or other specified formats (GRCh38 for human, mm10 for mouse); download and organize files by experiment.
- Step 4 (Integration Strategy): Choose an integration method based on platform similarity; apply batch correction and cross-study harmonization using Harmony, Seurat v5, scVI/scANVI, or STACAS as appropriate; harmonize cell type ontologies with CellHint if needed; validate with scIB metrics and batch vs biology balance.
- Pre-integration QC and ambient RNA handling: perform cell/ gene filtering, remove ambient RNA with tools like CellBender, SoupX, or DecontX, and remove doublets before integration. Identify HVGs and compute integrated embeddings, then cluster and annotate cell types using manual markers, CellTypist, scANVI, or reference mapping.
When to use it
- When you need meta-analysis across multiple scRNA-seq datasets for integration, cross-study comparison, atlas construction, or reproducibility assessment involving ENCODE single-cell data.
- When you want to harmonize cell type annotations across studies from different labs and platforms, and build a reference atlas with reproducibility-aware quality assessment.
- When your analysis goals include detecting conserved cell populations across datasets and evaluating cross-study reproducibility under detection-limit-aware interpretation.
What it can touch
- Tools: Harmony, Seurat v5, scVI, scANVI, STACAS, CellBender, SoupX, DecontX, CellTypist, Azimuth, Luecken/Benchmark metrics (scIB), kBET, iLISI, ARI, NMI, cLISI, UMAP/tSNE, Leiden/Louvain.
- Data types: scRNA-seq gene quantifications (TSV), annotations, QC metrics, HVG lists, embedding matrices, clustering results, and cell type labels.
- Procedures: experiment QC, file listing/downloading, integration strategy decision tree, ambient RNA removal, doublet removal, normalization, HVG identification, joint embedding, clustering, annotation, and cross-study harmonization.
Caveats
- Licensing: MIT. Any use should respect data licenses and model/tool licenses; the skill declares MIT but actual data licensing must be observed by the user.
- The skill prescribes a multi-step workflow with several external tools; results depend on dataset availability, platform compatibility, and correct parameter choices.
- It recommends specific tools and workflows (e.g., Harmony first for batch correction, scANVI/scVI for atlas-scale integration) but outcomes are not guaranteed and depend on data context.
# Cross-Study Meta-Analysis of scRNA-seq Data ## When to Use - User wants to perform meta-analysis across multiple single-cell RNA-seq datasets - User asks about "scRNA-seq meta-analysis", "dataset integration", "batch correction", or "cross-study comparison" - User needs to harmonize cell type annotations across studies from different labs - User wants to build reference atlases or identify conserved cell populations across datasets - Example queries: "integrate 5 scRNA-seq datasets from different labs", "harmonize cell type labels across studies", "meta-analyze single-cell data for pancreas" Integrate multiple ENCODE scRNA-seq datasets for a tissue/cell type into a unified cell atlas with reproducibility-aware quality assessment. ## Scientific Rationale **The question**: "What cell types and transcriptional programs are present in my tissue, and which findings are reproducible across studies?" Unlike bulk genomic assays (ChIP-seq, ATAC-seq) where signal detection is largely binary, single-cell transcriptomics operates at or below the limit of detection for most genes. This means that **heterogeneous detection is the norm, not the exception** — and distinguishing true biological h
- When to Use
- Scientific Rationale
- The Core Problem (Mawla et al. 2019)
- Literature Support
- Step 1: Find All Available scRNA-seq Experiments
- Step 2: Quality-Gate Each Experiment
- Include if:
- Exclude if:
- Key QC Metrics to Record Per Study:
- Step 3: Download Gene Quantification Files
- Step 4: Integration Strategy
- Decision Tree
- Method Selection Guide (Luecken et al. 2022 Benchmark)
- Integration Workflow (Scanpy/Seurat)
What does the scrna-meta-analysis skill do?
Conduct rigorous cross-study meta-analysis of scRNA-seq data from ENCODE, integrating multiple single-cell transcriptomic datasets for a tissue/cell type. Use when the user wants to answer "what cell types exist in my tissue and what genes define them?" by combining scRNA-seq data across donors, labs, and platforms. Follows the Mawla et al. 2019 framework for assessing cross-study reproducibility, TIN-based quality filtering, and detection-limit-aware interpretation. Handles batch correction (Harmony/Seurat), dropout awareness, cross-contamination artifacts, and platform-specific biases. Use t
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill scrna-meta-analysis-ammawla-encode-toolkit --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.