scMRMA

scMRMA annotates cell identities from single-cell RNA sequencing (scRNA-seq) data using a multi-resolution, marker-based bidirectional algorithm to improve annotation resolution and accuracy.

Key Features:

• Bidirectional Methodology: Implements a bidirectional approach that contrasts with conventional unidirectional projection methods to assign cell identities.
• Marker-based Annotation: Uses marker genes in a multi-resolution framework to inform cell-type assignments.
• Hierarchical Reference Utilization: Leverages a hierarchical reference to guide annotation and refine classification across resolution levels.
• Iterative Clustering and Deep Annotation: Performs iterative clustering and annotation cycles to progressively refine clusters and labels.
• Improved Resolution and Accuracy: Enhances the resolution and accuracy of cell identity determination in scRNA-seq datasets.

Scientific Applications:

• Cellular Heterogeneity Analysis: Supports dissection of cellular heterogeneity by providing more precise cell identity predictions in complex samples.
• Cancer Research and Immunotherapy Studies: Has been applied to reveal expansion of CD8 T cell populations in squamous cell carcinoma following anti-PD-1 treatment.

Methodology:

Employs a bidirectional approach contrasting with unidirectional projection methods, uses a hierarchical reference, and iteratively refines clustering and annotation.