baySeq

baySeq implements an empirical Bayesian framework to identify differential expression in high-throughput count data from next-generation sequencing.

Key Features:

• Empirical Bayes Approach: Uses an empirical Bayesian algorithm to detect patterns of differential expression across sequencing samples.
• Estimated Posterior Likelihoods: Calculates estimated posterior likelihoods for differential expression and other specified hypotheses.
• Negative Binomial Distribution Assumption: Models count data with a negative binomial distribution to account for overdispersion typical of RNA-seq.
• Empirically Determined Prior Distribution: Derives prior distributions empirically from the entire dataset.
• Support for Complex Designs: Handles pairwise comparisons and experimental designs involving multiple sample groups and complex hypotheses.

Scientific Applications:

• RNA-seq differential expression: Identifies differentially expressed genes or features in RNA-seq count data.
• Transcriptomic and genomic analyses: Applies to transcriptomic and other high-throughput genomic count datasets derived from next-generation sequencing.
• Comparative group studies: Supports analyses of pairwise comparisons and multi-group experimental designs to find differentially expressed elements.

Methodology:

Implements an empirical Bayesian framework that models count data with a negative binomial distribution, derives empirical priors from the dataset, and estimates posterior likelihoods of differential expression.