Lana Garmire

Abstract Copy number variations (CNV) are key drivers of cancer progression, yet methods for predicting spatial CNVs directly from haematoxylin and eosin (H&E) images are currently lacking. We introduce HisToSpatialCNV, a multiscale deep-learning framework that integrates histopathological features from H&E-stained images to infer spatial CNV patterns.

Transformer-based large language models are gaining traction in biomedical research, particularly in single-cell omics. Despite their promise, the application of single-cell large language models (scLLMs) remains limited in practice. In this Comment, we examine the current landscape of scLLMs and the benchmark studies that assess their applications in various analytical tasks.

Abstract Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding.