Nabo

Nabo maps single-cell RNA-sequencing (scRNA-Seq) data across cell populations to identify transcriptional similarities and differences for analyses such as identifying tumor-initiating cells and stages of differentiation arrest in cancer.

Key Features:

• Cell Mapping Across Populations: Maps cells from a target population onto a reference population by aligning transcriptomic profiles from scRNA-Seq.
• Handling Perturbation Effects: Focuses on similarities that transcend large-scale gene expression changes caused by perturbations such as oncogene activation, enabling mapping despite transcriptional shifts.
• Validation and Accuracy: Validated across multiple datasets and platforms and demonstrated higher accuracy than conventional classification methods in cell mapping tasks.
• Integrated Toolkit: Provides preprocessing of scRNA-Seq data, differential gene expression identification, and visualization.
• Reliability Evaluation Methods: Includes methods to assess the reliability of cell mapping results for exploratory studies.

Scientific Applications:

• Identification of tumor-initiating cells: Compare tumor-initiating cells with their normal counterparts to locate cell states associated with tumorigenesis despite perturbations.
• Detection of differentiation arrest: Pinpoint specific stages of differentiation arrest in oncogene-induced hematopoietic progenitor cells (GMLPs) amid cellular heterogeneity and transcriptional changes.
• Comparative analysis of perturbations: Compare healthy and oncogene-induced hematopoietic progenitor cells (GMLPs) to identify transcriptional effects of oncogenes.

Methodology:

One population is set as a reference and target cells are projected onto it based on transcriptomic similarity; includes preprocessing of scRNA-Seq data, differential gene expression identification, validation across multiple datasets and platforms, and methods to assess mapping reliability.