Paper List
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GOPHER: Optimization-based Phenotype Randomization for Genome-Wide Association Studies with Differential Privacy
This paper addresses the core challenge of balancing rigorous privacy protection with data utility when releasing full GWAS summary statistics, overco...
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Real-time Cricket Sorting By Sex A low-cost embedded solution using YOLOv8 and Raspberry Pi
This paper addresses the critical bottleneck in industrial insect farming: the lack of automated, real-time sex sorting systems for Acheta domesticus ...
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Training Dynamics of Learning 3D-Rotational Equivariance
This work addresses the core dilemma of whether to use computationally expensive equivariant architectures or faster symmetry-agnostic models with dat...
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Fast and Accurate Node-Age Estimation Under Fossil Calibration Uncertainty Using the Adjusted Pairwise Likelihood
This paper addresses the dual challenge of computational inefficiency and sensitivity to fossil calibration errors in Bayesian divergence time estimat...
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Few-shot Protein Fitness Prediction via In-context Learning and Test-time Training
This paper addresses the core challenge of accurately predicting protein fitness with only a handful of experimental observations, where data collecti...
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scCluBench: Comprehensive Benchmarking of Clustering Algorithms for Single-Cell RNA Sequencing
This paper addresses the critical gap of fragmented and non-standardized benchmarking in single-cell RNA-seq clustering, which hinders objective compa...
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Simulation and inference methods for non-Markovian stochastic biochemical reaction networks
This paper addresses the computational bottleneck of simulating and performing Bayesian inference for non-Markovian biochemical systems with history-d...
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Assessment of Simulation-based Inference Methods for Stochastic Compartmental Models
This paper addresses the core challenge of performing accurate Bayesian parameter inference for stochastic epidemic models when the likelihood functio...
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
30秒速读
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.
核心创新
- 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.
主要结论
- 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.
摘要: 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).