Quality-driven divisive K-Means: A new clustering strategy for MALDI imaging data for a more precise and less biased characterization of complex biological tissues

Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging (MALDI-MSI) has become the foremost technique for molecular characterization of complex biological tissues, owing to its unparalleled sensitivity, broad molecular coverage, and high spatial resolution. While targeted analysis is traditionally dominated MALDI imaging workflows, the inherent limitations of hypothesis-driven approaches have fueled interest in untargeted strategies. Clustering, particularly k-means-based methods, has emerged as a powerful tool for exploring spectral datasets without predefined assumptions. However, conventional k-means struggles with heterogeneous spectral distributions, prompting the adoption of bisecting k-means in MALDI imaging. Despite its hierarchical structure, bisecting k-means introduces biases by arbitrarily merging or fragmenting clusters, potentially distorting biological interpretations. This study introduces Quality-Driven Divisive k-means, a novel clustering approach that retains the hierarchical nature of bisecting k-means while dynamically optimizing the number of clusters at each partitioning level. Using MALDI imaging of squamous cell carcinoma tissues from the tongue, we illustrate the potential of Quality-Driven Divisive k-means to provide a more faithful representation of molecular architectures, mitigating the distortions inherent to fixed binary partitioning. Our findings suggest that adaptive clustering methodologies could enhance spectroscopic imaging, paving the way for more accurate tissue characterization in biomedical and clinical research. • A new adaptive clustering method, Quality-Driven Divisive k-means (QDD k-means), is introduced for MALDI imaging data. • QDD k-means dynamically selects the optimal number of clusters using silhouette scores at each hierarchy level. • Applied to tongue squamous cell carcinoma, QDD k-means accurately identifies cancer-specific molecular patterns. • Compared to bisecting k-means, QDD k-means provides a more faithful and less biased representation of tissue heterogeneity.