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scrnaseq-cell-type-annotation

Marker-based and reference-based cell type annotation of single-cell RNA-seq clusters using SingleR, Azimuth, or manual curation.

person作者: TashanworldhubOpenAPI
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Skill: scRNA-seq Cell Type Annotation

Use When

  • User has a clustered single-cell dataset and wants to assign biological cell type labels to clusters.
  • User wants to use automated reference-based annotation (SingleR, Azimuth) alongside manual marker validation.
  • User wants to generate per-cluster marker gene tables and visualizations.
  • User needs cell type composition summaries across samples.

Inputs

  • Required:
    • Clustered AnnData (.h5ad) or Seurat object (.rds) with UMAP coordinates
  • Optional:
    • Framework: scanpy or seurat (default: scanpy)
    • Annotation method: singler, azimuth, manual, or combined (default: combined)
    • Reference dataset for SingleR: e.g., HumanPrimaryCellAtlasData, BlueprintEncodeData, MouseRNAseqData, or path to a user-provided reference (default: HumanPrimaryCellAtlasData)
    • Azimuth reference tissue (e.g., pbmcref, bonemarrowref, lungref, kidneyref, brainref)
    • Custom marker gene list (YAML or CSV with cell type → gene list mapping)
    • Minimum log2FC for marker genes (default: 0.25)
    • Minimum fraction of cells expressing the gene in the cluster, pct.1 (default: 0.1)

Workflow

  1. Find cluster marker genes using Wilcoxon rank-sum test (sc.tl.rank_genes_groups in Scanpy or FindAllMarkers in Seurat). Retain genes with logFC > 0.25, pct.1 > 0.1, and adjusted p-value < 0.05.
  2. Generate dot plots and feature plots for the top 5 marker genes per cluster to visualize expression specificity.
  3. If singler or combined: run SingleR using the specified reference dataset. Map cluster-level pseudobulk profiles to reference cell types. Report per-cluster label and confidence score (delta.next statistic).
  4. If azimuth or combined: run Azimuth reference mapping. Map query cells to the reference UMAP; assign predicted cell type label and confidence score per cell.
  5. Cross-validate automated labels against canonical lineage markers from the literature (e.g., CD3D/CD3E for T cells, CD19/MS4A1 for B cells, LYZ/CD14 for monocytes, EPCAM for epithelial, PECAM1 for endothelial).
  6. Assign final cell type labels per cluster. Merge clusters with identical cell types if biologically justified; flag ambiguous clusters for manual review.
  7. Plot an annotated UMAP with cell type labels.
  8. Compute cell type composition per sample as a fraction of total cells; generate a stacked bar plot.
  9. Save annotated AnnData (.h5ad) or Seurat object (.rds) with cell_type annotation in obs/metadata.

Output Contract

  • Annotated AnnData (.h5ad) or Seurat object (.rds) with cell_type column in obs
  • Marker gene table per cluster (TSV): gene, cluster, log2FC, pct.1, pct.2, adj_p_value
  • Dot plot of top markers per cluster (PDF)
  • Feature plots for canonical lineage markers (PDF)
  • SingleR/Azimuth prediction table (TSV): cluster or cell barcode, predicted label, confidence score
  • Annotated UMAP plot (PDF)
  • Cell type composition table and stacked bar plot per sample (TSV + PDF)

Limits

  • Automated annotation accuracy depends on reference dataset relevance to the tissue and species; cross-species or rare cell types may be mislabeled.
  • Manual validation against literature markers is always required; treat automated labels as hypotheses.
  • Azimuth reference panels are tissue-specific: check availability before use (PBMC, bone marrow, lung, kidney, brain, fetal development).
  • Batch effects can cause the same cell type to split across clusters; address batch effects with scrnaseq-integration before annotation.
  • Marker gene overlap between closely related subtypes (e.g., CD4+ T cell subsets) requires sub-clustering or additional markers for resolution.
  • SingleR performs poorly when the query tissue contains cell types absent from the reference; spurious assignments appear with low confidence scores (delta.next < 0.05).
  • Common failure cases:
    • Reference dataset species mismatch (e.g., using a human reference for mouse data) causing all cells to map to the wrong types.
    • Azimuth reference not installed or outdated; update with SeuratData::InstallData().
    • Marker-based annotation failing because the dataset is poorly clustered (resolution too low); re-cluster at higher resolution before annotating.