CIARA

CIARA identifies candidate marker genes for rare cell types in single-cell RNA sequencing and other single-cell omics data and integrates these candidates with clustering to improve detection and characterization of rare cell populations.

Key Features:

• Cluster-independent marker selection: Performs marker gene selection independently of pre-defined clusters to prioritize genes likely to mark rare cell types.
• Likelihood-based candidate selection: Selects and ranks candidate genes based on their likelihood of being markers for rare cell populations.
• Integration with clustering: Integrates selected candidate genes with conventional clustering algorithms to facilitate grouping and identification of rare cell types.
• Multi-omics applicability: Operates on single-cell RNA sequencing and other single-cell omics datasets.
• Enhanced rare-population detection: Improves detection and characterization of rare populations that standard clustering approaches may overlook.

Scientific Applications:

• Human gastrula analysis: Identified previously uncharacterized rare populations in human gastrula samples.
• Mouse embryonic stem cells: Detected rare populations in mouse embryonic stem cells treated with retinoic acid.
• General single-cell studies: Uncovers novel rare cell types overlooked by standard clustering in single-cell datasets.

Methodology:

CIARA selects candidate marker genes independently of clustering based on their likelihood of marking rare cell types and integrates these candidates with conventional clustering algorithms.