Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics
This paper addresses the core challenge of achieving accurate, interpretable, and training-free nanoparticle counting in digital diagnostic assays, wh...
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MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare
This paper addresses the critical gap in healthcare AI systems that lack contextual reasoning, long-term state management, and verifiable workflows by...
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Model Gateway: Model Management Platform for Model-Driven Drug Discovery
This paper addresses the critical bottleneck of fragmented, ad-hoc model management in pharmaceutical research by providing a centralized, scalable ML...
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Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species
This paper addresses the fragmentation of tree-based inference methods across biological scales by identifying shared algorithmic principles and stati...
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
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.
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).