Automatic computational classification of bone marrow cells for B cell pediatric leukemia using UMAP

B Acute Lymphoblastic Leukemia (B-ALL) accounts for approximately 80% of pediatric leukemia cases. Despite treatment advances, 15 20% of children experience relapse, highlighting the need of improved monitoring of patients and novel strategies leading to successful therapies. Flow Cytometry (FC) is essential for diagnosing and monitoring hematological diseases, particularly for measuring residual disease and guiding treatment. However, traditional manual gating limits its efficiency. In recent years, computational tools have been integrated to enhance these clinical processes but many mathematical techniques are underexploited. Particularly, Uniform Manifold Approximation and Projection (UMAP) provides promising approaches for analyzing large datasets. In this work, we examine 234 samples FC data from 75 B ALL patients, regenerated bone marrow and leukemic bone marrow datasets, including relapsed and non-relapsed cases. With UMAP, we are able to automatically identify key subpopulations and classify B cell maturation states. This allows an automatic track of disease progression across diagnosis, day +33, and +78 after treatment, finding differences in bone marrow regeneration patterns between both cohorts of patients. This study advances FC based analysis by integrating UMAP, enabling automatic classification. This represents a step forward in standardized and innovative mathematical approaches that pave the way for monitoring B ALL and its prognosis.