Partition-Aware Joint Sparse Precision Matrix Estimation for Cancer Diagnosis

Precision matrices, which encode gene regulatory networks, play an important role for cancer diagnosis from transcriptomics data. Cancer subtypes often form partially related families that share regulatory structure to varying degrees, yet most multi-condition network estimation methods treat subtype relatedness as either absent or uniform. This mistreatment is particularly limiting in high-dimensional transcriptomic studies, where subtype relationships are uncertain and sample sizes are small. We propose Partition-Aware Joint Sparse Precision Matrix Estimation (PA-JSPME), a unified framework that jointly estimates subtype-specific precision matrices while learning latent clusters of related subtypes directly from data. The method introduces a partition-aware fusion penalty that selectively encourages similarity within inferred clusters while allowing networks from different clusters to diverge. To reduce shrinkage bias and preserve strong regulatory signals, PA-JSPME employs the Smoothly Clipped Absolute Deviation (SCAD) regularization. Computational scalability is achieved through a quadratic surrogate likelihood and an efficient alternating optimization algorithm combining k-means partition updates with an ADMM-MM solver. Experiments on synthetical datasets demonstrate that PA-JSPME accurately recovers both latent subtype structure and class-specific networks across a range of dimensionality settings. Applications to pediatric and adult brain tumor transcriptomics datasets yield biologically coherent subtype groupings, sparse and interpretable regulatory networks, and improved cancer diagnosis performance compared to existing joint graphical modeling approaches. Furthermore experiments on two additional real-world transcriptomic datasets illustrate the generalizability of our proposed method.