AtlasLens: Metadata-centric exploration and analysis of single-cell atlases (processed datasets)

Motivation: The rapid expansion of single-cell RNA sequencing (scRNA-seq) atlases has generated datasets comprising millions of cells annotated with increasingly rich metadata, including tissue, cell type, disease status, sex, age, treatment, and temporal information. Biological questions frequently require simultaneous interrogation of multiple metadata dimensions, such as identifying specific cell populations within defined tissues, disease states, demographic groups, and time points. While existing interactive platforms facilitate visualization and analysis of scRNA-seq data, deep metadata-driven exploration and downstream analysis of atlas-scale datasets remain insufficiently supported. Results: We developed AtlasLens, an open-source R/Shiny application for interactive exploration of scRNA-seq datasets and integrated cellular atlases. AtlasLens enables iterative filtering across arbitrary metadata combinations, allowing users to define biologically meaningful cellular subsets and immediately perform downstream analyses. The platform integrates interactive visualization, differential expression analysis, Gene Ontology enrichment with redundancy reduction, temporal expression analysis, and context-dependent gene function profiling through GeneCOCOA. AtlasLens additionally records analysis history and automatically generates corresponding R code to enhance reproducibility. The application is distributed through Docker for simple local deployment, preserving data privacy and eliminating dependency-management challenges. We demonstrate AtlasLens using the Tabula Muris atlas and a multi-dataset acute myocardial infarction atlas, highlighting its ability to support complex metadata-driven biological investigations.