siggenes

siggenes performs statistical identification of differentially expressed genes and estimates False Discovery Rate (FDR) using Significance Analysis of Microarrays (SAM) and Empirical Bayes Analyses of Microarrays (EBAM) for microarray expression and high-dimensional categorical SNP data.

Key Features:

• Differential Gene Expression Analysis: Uses Significance Analysis of Microarrays (SAM) to identify genes with significant expression changes between experimental conditions in microarray data.
• Empirical Bayes Framework: Implements Empirical Bayes Analyses of Microarrays (EBAM) to improve detection of associated features, originally for continuous predictors and adapted for categorical data.
• Multiple Testing Control: Estimates False Discovery Rate (FDR) to address multiple testing problems inherent in large-scale genomic and SNP analyses.
• Categorical SNP Analysis: Provides a modified EBAM approach tailored for high-dimensional categorical SNP data to assess associations with covariates such as case-control status or cancer type.
• Interaction and Genotype Testing: Produces posterior probabilities from EBAM to quantify the importance of SNP interactions and genotypes.

Scientific Applications:

• Whole-genome association studies: Identification of SNPs associated with traits or diseases in genome-wide datasets.
• Cancer genomics: Analysis of SNP associations and expression differences related to cancer type.
• Gene expression microarray studies: Detection of differentially expressed genes across experimental conditions.
• HapMap data analysis: Demonstrated applicability to subsets of HapMap data for both continuous expression and categorical SNP analyses.
• Genetic association studies: Assessment of SNP interactions and complex genetic architectures to inform association analyses.

Methodology:

Uses Significance Analysis of Microarrays (SAM), Empirical Bayes Analyses of Microarrays (EBAM) including a modified EBAM for categorical SNP data, estimation of False Discovery Rate (FDR), and computation of posterior probabilities to test SNP interactions and genotypes.