HematoMap

HematoMap identifies and visualizes lineage aberrations in leukemia from single-cell and bulk RNA sequencing data by mapping leukemic cells to their normal hematopoietic counterparts.

Key Features:

• Curated Reference Hierarchy: A comprehensive reference of normal hematopoiesis constructed through multistep clustering and annotation of over 100,000 bone marrow mononuclear cells from 25 healthy donors.
• Cosine Distance Scoring: A likelihood score based on the cosine distance algorithm that determines similarities of leukemic cells to their putative normal counterparts.
• Bulk RNA-seq LASSO Score Model: A least absolute shrinkage and selection operator (LASSO) score model that reveals subtle differences in lineage aberrancy within acute myeloid leukemia (AML) or B cell precursor acute lymphoblastic leukemia (BCP-ALL) patients.
• Dual Input Compatibility: Analysis capabilities for both single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data.

Scientific Applications:

• Leukemia Cell Mapping: Mapping cells of acute myeloid leukemia (AML) and B cell precursor acute lymphoblastic leukemia (BCP-ALL) samples to their corresponding normal hematopoietic counterparts.
• Cell-of-Origin Determination: Understanding the cell-of-origin and mechanisms underlying disease initiation and development in leukemia.
• Lineage Aberrancy Analysis: Revealing subtle differences in lineage aberrancy within AML or BCP-ALL patient cohorts.
• Leukemogenesis Research: Providing curated resources and data analytics for understanding leukemogenesis, with potential to enhance leukemia risk stratification and personalized treatments.

Methodology:

HematoMap employs a curated clustering approach using single-cell RNA sequencing data to construct the reference hierarchy, followed by cosine distance algorithm-based scoring to determine similarities between leukemic and normal cells. For bulk RNA sequencing data, a least absolute shrinkage and selection operator (LASSO) score model is used.