Abstract Rapid advancements in single-cell RNA-sequencing (scRNA-seq) technologies revealed the richness of myriad attributes encompassing cell identity, such as diversity of cell types, organ-of-origin, or developmental stage. However, due to the large scale of the data, obtaining an interpretable compressed representation of cellular states remains a computational challenge. For this task we introduce bioIB, a method based on the Information Bottleneck algorithm, designed to extract an optimal compressed representation of scRNA-seq data with respect to a desired biological signal, such as cell type or disease state. BioIB generates a hierarchy of weighted gene clusters, termed metagenes, that maximize the information regarding the signal of interest. Applying bioIB to a scRNA-seq atlas of differentiating macrophages and setting either the organ-of-origin or the developmental stage as the signal of interest provided two distinct signal-specific sets of metagenes that captured the attributes of the respective signal. BioIB’s representation can also be used to expose specific cellular subpopulations, for example, when applied to a single-nucleus RNA-sequencing dataset of an Alzheimer’s Disease mouse model, it identified a subpopulation of disease-associated astrocytes. Lastly, the hierarchical structure of metagenes revealed interconnections between the corresponding biological processes and cellular populations. We demonstrate this over hematopoiesis scRNA-seq data, where the metagene hierarchy reflects the developmental hierarchy of hematopoietic cell types. Significance Single-cell gene expression represents an invaluable resource, encoding multiple aspects of cellular identity. However, its high complexity poses a challenge for downstream analyses. We introduce bioIB, a methodology based on the Information Bottleneck, that compresses data while maximizing the information about a biological signal-of-interest, such as disease state. bioIB generates a hierarchy of metagenes, probabilistic gene clusters, which compress the data at gradually changing resolutions, exposing signal-related processes and informative connections between gene programs and their corresponding cellular populations. Across diverse single-cell datasets, bioIB generates distinct metagene representations of the same dataset, each maximally informative relative to a different signal; uncovers signal-associated cellular populations; and produces a metagene hierarchy that reflects the developmental hierarchy of the underlying cell types.
