consensusDE

consensusDE integrates differential expression results from multiple RNA-seq algorithms and optionally applies RUV-seq to mitigate unwanted variation, enabling identification of consensus differentially expressed genes across conditions.

Key Features:

• Multi-Algorithm Integration: Supports Voom, EdgeR, and DESeq2 and combines their outputs to identify consensus differentially expressed genes.
• RUV Correction: Optionally integrates RUV-seq to mitigate unwanted variation and stabilize fold change estimates across algorithms, particularly in low-replication settings to assist false discovery rate (FDR) control.
• Input Requirements: Accepts a sample group description table, a directory of BAM files or preprocessed count tables, and an optional transcript database for annotation.
• Technical Replicate Merging and Paired Analysis Support: Handles technical replicate merging and paired analysis designs.
• Output Visualization: Produces a compendium of plots for interpretation and validation of differential expression results.
• Algorithm-Specific Assessment: Evaluates fold change stability across algorithms and applies algorithm-specific adjustments to inform consensus calls and FDR reduction.

Scientific Applications:

• Transcriptomics differential expression: Identification of robust differentially expressed genes in RNA-seq studies across conditions or treatments.
• Low-replication studies: Improving reliability of DE calls and FDR control in experiments with limited biological replication.
• Cross-method consensus prioritization: Prioritizing genes consistently detected by Voom, EdgeR, and DESeq2 for downstream validation.

Methodology:

Integrates outputs from Voom, EdgeR, and DESeq2, optionally applies RUV-seq for removal of unwanted variation, supports merging technical replicates and paired analyses, evaluates fold change stability and FDR across algorithms, and generates diagnostic plots.