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Shamim Ashrafiyan

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Open access Jul 2026

AtlasLens: Metadata-centric exploration and analysis of single-cell atlases

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 and a time-resolved whole-lung single-cell atlas of bleomycin-induced lung injury and fibrosis, highlighting its ability to support complex metadata-driven biological investigations. Availability Source code is available at https://github.com/SchulzLab/AtlasLens. Contact marcel.schulz@em.uni-frankfurt.de

Shamim Ashrafiyan, Iaroslav Kosaretskii, Marcel H. Schulz · 0 citations
Open access Jul 2026

Comparing machine learning methods predicting transcriptome from epigenome with applications to association studies

Understanding how epigenome variation contributes to gene expression in disease and development is a fundamental challenge. Regulatory regions show cell type-specific epigenome activity and differ in their location, size, and distance to their target genes, complicating discovery and analysis. Recent machine learning models have been proposed to address these problems by learning functions for the prediction of gene expression from epigenomic data. Here, we use the large IHEC EpiATLAS dataset to benchmark state-of-the-art linear and nonlinear approaches. We optimize each approach for over 28,000 human genes, providing an inferred regulatory catalog of gene models. In-depth comparison reveals that gene characteristics and the epigenomic complexity of the locus influence the difficulty of predicting the epigenome-to-transcriptome association. The model performance is further evaluated using CRISPRi and eQTL validation data. Based on these models, we conduct histone-acetylation association studies in a systematic way to investigate how epigenetic variation impacts gene expression. The model-based analysis revealed genes and regulatory regions linked to B-cell leukemia in patient data with known disease-related functions. Our work provides a foundation for applications that link epigenome variation to gene expression in human cells, by benchmarking methods on a per-gene basis, illustrating their use in a disease context and making trained models available to the community.

Fatemeh Behjati Ardakani, Shamim Ashrafiyan, Laura Rumpf et al. · 0 citations