fdrame

fdrame performs false discovery rate (FDR) multiple-testing correction and visualization for high-throughput genomic expression data to control false discoveries in large-scale hypothesis testing.

Key Features:

• fdr.ma Function: Processes normalized expression data arrays together with experimental design information and computes FDR-adjusted p-values for multiple hypothesis testing.
• FDR Adjustment Methods: Implements false discovery rate (FDR) multiple-testing correction to control the expected proportion of false positives among rejected hypotheses.
• Outputs: Produces FDR-adjusted p-values and plots for visualizing statistical significance across numerous tests.
• fdr.gui Function: Provides an alternative front-end that invokes fdr.ma functionality to access the package's analysis routines.
• Integration with Bioconductor and R: Operates within the Bioconductor framework and the R statistical programming environment.

Scientific Applications:

• Gene expression profiling: Applies FDR adjustment to large-scale transcriptomic datasets to identify differentially expressed genes while controlling false discoveries.
• Genome-wide association studies (GWAS): Applies multiple-testing correction across many genetic variants to reduce type I error rates in association testing.
• Other omics technologies: Supports high-throughput omics datasets, such as proteomics or metabolomics, that require multiple hypothesis testing correction.
• Genomics and molecular biology research: Facilitates reliable inference from large-scale experimental data by controlling expected false discovery rates.

Methodology:

Processes normalized expression data arrays with experimental design metadata, computes adjusted p-values using FDR multiple-testing correction methods, and generates plots of FDR-adjusted p-values via the fdr.ma (and invoked fdr.gui) functions.