Paper List

Journal: ArXiv Preprint
Published: 2025
BioinformaticsComputational Biology

DeeDeeExperiment: Building an infrastructure for integrating and managing omics data analysis results in R/Bioconductor

Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz | Research Center for Immunotherapy (FZI) Mainz | Department of Nephrology, Rheumatology and Kidney Transplantation, University Medical Center Mainz

Najla Abassi, Lea Schwarz, Edoardo Filippi, Federico Marini
Figure

The 30-Second View

IN SHORT: This paper addresses the critical bottleneck of managing and organizing the growing volume of differential expression and functional enrichment analysis results from complex omics experiments, which currently lack standardized data structures for storage and contextualization.

Innovation (TL;DR)

  • Methodology Introduces the first standardized S4 class specifically designed to co-store DEA and FEA results with their metadata in a single, structured container within the Bioconductor ecosystem.
  • Methodology Extends the widely adopted SingleCellExperiment class by adding dedicated slots for DEA and FEA results while maintaining full backward compatibility with existing Bioconductor tools.
  • Methodology Implements a contrast-centric architecture that organizes results from multiple comparisons (including limma multi-contrast objects and muscat pseudobulk analyses) with efficient storage through pointer-based referencing.

Key conclusions

  • DeeDeeExperiment provides a robust, standardized framework that enables efficient organization and retrieval of DEA/FEA results across multiple contrasts within a single data object.
  • The implementation maintains full compatibility with the Bioconductor ecosystem, supporting interoperability with downstream tools like scater for visualization and iSEE for interactive exploration.
  • By consolidating analysis results and metadata, the framework supports more nuanced quantitative approaches beyond simple overlap strategies, enabling trustworthy summaries of complex experimental measurements.
Background and Gap： Current omics workflows generate numerous DEA and FEA result tables across multiple conditions and cell types, but there is no standardized data structure to organize, link, and contextualize these results with their metadata, leading to unmanageable, non-reproducible collections that are difficult to navigate and share.

Abstract: Summary: Modern omics experiments now involve multiple conditions and complex designs, producing an increasingly large set of differential expression and functional enrichment analysis results. However, no standardized data structure exists to store and contextualize these results together with their metadata, leaving researchers with an unmanageable and potentially non-reproducible collection of results that are difficult to navigate and/or share. Here we introduce DeeDeeExperiment, a new S4 class for managing and storing omics data analysis results, implemented within the Bioconductor ecosystem, which promotes interoperability, reproducibility and good documentation. This class extends the widely used SingleCellExperiment object by introducing dedicated slots for Differential Expression (DEA) and Functional Enrichment Analysis (FEA) results, allowing users to organize, store, and retrieve information on multiple contrasts and associated metadata within a single data object, ultimately streamlining the management and interpretation of many omics datasets. Availability and implementation: DeeDeeExperiment is available on Bioconductor under the MIT license (https://bioconductor.org/packages/DeeDeeExperiment), with its development version also available on Github (https://github.com/imbeimainz/DeeDeeExperiment).

Code