Abstract
In single-cell datasets, patient labels indicating disease status (e.g., "sick" or "not sick") are typically available, but individual cell labels indicating which of a patient's cells are associated with their disease state are generally unknown. To address this, we introduce mixture modeling for multiple-instance learning (MMIL), an expectation-maximization approach that trains cell-level binary classifiers using only patient-level labels. Applied to primary samples from patients with acute leukemia, MMIL accurately separates leukemia from nonleukemia baseline cells, including rare minimal residual disease (MRD) cells; generalizes across tissues and treatment time points; and identifies biologically relevant features with accuracy approaching that of a hematopathologist. MMIL can also incorporate cell labels when they are available, creating a robust framework for leveraging both labeled and unlabeled cells. MMIL provides a flexible modeling framework for cell classification, especially in scenarios with unknown gold-standard cell labels.