Imputing single-cell protein abundance in multiplex tissue imaging

Multiplex tissue imaging enables single-cell spatial proteomics and transcriptomics but remains limited by incomplete molecular profiling, tissue loss, and probe failure. Here, we apply machine learning to impute single-cell protein abundance using multiplex tissue imaging data from a breast cancer cohort. We evaluate regularized linear regression, gradient-boosted trees, and deep learning autoencoders, incorporating spatial context to enhance imputation accuracy. Our models achieve mean absolute errors between 0.05–0.3 on a [0,1] scale, closely approximating ground truth values. Using imputed data, we classify single cells as pre- or post-treatment, demonstrating their biological relevance. These findings establish the feasibility of imputing missing protein abundance, highlight the advantages of spatial information, and support machine learning as a powerful tool for improving single-cell tissue imaging.