SingleR

SingleR performs unbiased cell type recognition from single-cell RNA sequencing data by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.

Key Features:

• Reference-based annotation: Leverages reference transcriptomic datasets of pure cell types for cell type identification.
• Unbiased cell type recognition: Infers the cell of origin of each single cell independently without prior clustering.
• Bulk transcriptome integration: Annotates single-cell RNA sequencing data by reference to bulk transcriptomes.

Scientific Applications:

• Macrophage subclustering: Enables subclustering of macrophage populations to identify disease-associated subgroups in fibrosis.
• Disease-associated cell identification: Reveals transitional cell states intermediate between known cell types in pathological contexts.
• Cross-species analysis: Supports identification of human orthologs of genes expressed by disease-associated cell subgroups identified in model organisms.

Methodology:

SingleR uses a novel computational framework that annotates single-cell RNA sequencing data by reference to bulk transcriptomes.