nudge

nudge detects differentially expressed genes from cDNA microarray data by estimating probabilities of differential expression while addressing multiple testing.

Key Features:

• NUDGE method: Implements the Normal Uniform Differential Gene Expression (NUDGE) detection method using a univariate normal-uniform mixture model.
• Normalization: Applies normalization adjustments for both spread and mean, building upon lowess normalization (Dudoit et al., 2002).
• Probabilistic output: Produces probabilities of differential expression and intrinsically accounts for multiple testing.
• Data and designs: Operates on cDNA microarray data for single-slide, replicated, and single-replicate experimental designs.
• Benchmarking: Validated on three datasets and compared against unadjusted and Bonferroni-adjusted t-tests, Significance Analysis of Microarrays (SAM), and EBarrays with Gamma-Gamma and Lognormal-Normal models.
• Performance: Assigns high probabilities to a priori suspected differentially expressed genes and reduces false positives and false negatives relative to several established methods.

Scientific Applications:

• Differential expression detection: Identification of differentially expressed genes in cDNA microarray experiments.
• Single-slide analysis: Analysis of single-slide microarray experiments including cases with single replicates.
• Replicated experiment analysis: Analysis of replicated microarray experiments to detect expression differences across conditions.
• Method benchmarking: Comparative evaluation and benchmarking against t-tests, SAM, and EBarrays models.

Methodology:

Uses the Normal Uniform Differential Gene Expression (NUDGE) detection method based on a univariate normal-uniform mixture model, incorporates normalization for spread and mean built on lowess normalization (Dudoit et al., 2002), and outputs probabilities of differential expression while accounting for multiple testing.