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

TL;DR

DeeDeeExperiment is introduced, a new S4 class for managing and storing omics data analysis results, implemented within the Bioconductor ecosystem, which promotes interoperability, reproducibility and good documentation.

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).

Figures (1)

• Figure 1: Anatomy of DeeDeeExperiment class. It extends the SingleCellExperiment class (right panel), inheriting its core slots (assays, rowData, colData, reducedDims, metadata) and adds two new slots: dea, for storing Differential Expression Analysis (DEA) results, and fea, for storing Functional Enrichment Analysis (FEA) results. Both slots store results as a list of named contrasts/comparisons (hereby used interchangeably). Their names are provided by the user (defaulting to the object name if not specified), and are enforced to be unique. For each contrast, the dea slot stores (when available) metadata describing the DEA, such as the alpha level, LFC threshold, package used to generate the results and its version. The fea slot follows the same design. For each contrast, it stores metadata such as the associated DEA contrast from which the FEA was derived, the FEA name, the "shaken" GeneTonic output, the tool used to perform the enrichment analysis and its version. Original result objects and feature-level summaries are linked through the metadata and rowData.