Identifying maximally informative signal-aware representations of single-cell data using the information bottleneck

Rapid advancements in single-cell RNA sequencing (scRNA-seq) technologies revealed the richness of myriad attributes encompassing cell identity. However, the complexity of the data hinders tasks focusing on a specific biological signal. To address this challenge, we introduce bioIB, a framework based on the information bottleneck method, designed to extract an interpretable compressed representation of scRNA-seq data, optimally informative with respect to a specific biological signal, such as disease state. Provided with cellular labels representing the signal of interest, bioIB generates weighted gene clusters, termed metagenes, that compress the data while maximizing signal-specific information. Further, bioIB provides the hierarchical structure of the metagenes, revealing the interconnections between the corresponding biological processes and cellular populations, such as the developmental hierarchy of hematopoietic cell types. We showcase bioIB's applicability to diverse biological contexts, including Alzheimer's disease, epithelial-to-mesenchymal transition, immune development, and hematopoiesis, demonstrating diverse applications of optimally compressed multicellular representations.