geva

geva evaluates variations of differential expression between multiple biological conditions using fold-changes and p-values from previous differential expression results derived from large-scale data such as microarray and RNA-seq.

Key Features:

• Weighted Summarization: Combines fold-changes and p-values from prior differential expression results to generate integrated gene scores.
• Quantile Detection: Identifies threshold-based gene subsets based on distribution characteristics of the summarized data.
• Cluster Analysis: Groups genes by similar differential expression patterns across multiple biological conditions.
• ANOVA Tests: Assesses statistical significance of gene expression variation dependence on biological factors.

Scientific Applications:

• Cross-Experiment Gene Response Evaluation: Identifies genes that react in response to distinct experiments across multiple biological conditions.
• Biological Factor Association: Classifies relevant genes whose differential expression is similar or dependent on certain biological factors.
• Large-Scale Transcriptomic Data Integration: Analyzes differential expression results from microarray and RNA-seq data to evaluate gene expression variation.

Methodology:

The tool applies a pipeline of statistical methods including weighted summarization, quantile detection, cluster analysis, and ANOVA tests to classify relevant genes based on their differential expression patterns across conditions.