scrna-meta-analysislisted

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 this skill for ANY scRNA-seq integration task, cross-dataset comparison, cell atlas construction, or reproducibility assessment involving ENCODE single-cell data.
ammawla/encode-toolkit · ★ 35 · AI & Automation · score 79

Install: claude install-skill ammawla/encode-toolkit

# 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 heterogeneity from technical dropout is the central challenge of any scRNA-seq meta-analysis. ### The Core Problem (Mawla et al. 2019) Mawla, van der Meulen & Huising (2019, Diabetes) conducted a landmark meta-analysis of five independent human pancreatic islet scRNA-seq studies and revealed: