Individual skill
single-cell-rnaseq
Single-cell RNA-seq analysis — QC, normalization, clustering, annotation, and trajectory inference.
skills/single-cell-rnaseq/SKILL.md
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## Overview
single-cell-rnaseq covers the standard scRNA-seq analysis pipeline: quality control at the cell
level, normalization for sparse count data, dimensionality reduction, clustering, cell-type
annotation, differential expression, and trajectory inference. Single-cell data is sparse,
noisy, and batch-sensitive — the pipeline choices matter more here than in bulk RNA-seq.
## When to use
- QC: doublet detection, ambient RNA, mitochondrial content, empty droplets.
- Normalization: log-normalization, SCTransform, size factors for sparse data.
- Dimensionality reduction: PCA, UMAP/t-SNE (and their limits).
- Clustering: Louvain/Leiden, resolution selection, cluster validation.
- Cell-type annotation: marker genes, reference mapping (Azimuth, SingleR).