BBSeq

BBSeq models RNA-Seq transcriptional count data using a beta-binomial generalized linear model and mean–variance penalization to improve statistical inference for differential expression and gene-level variability.

Key Features:

• Beta-binomial GLM: Implements a beta-binomial generalized linear model tailored for RNA-Seq count data to explicitly represent mean–variance relationships.
• Penalization and shrinkage: Adapts penalization and shrinkage techniques from microarray gene expression analysis to stabilize estimates for RNA-Seq data.
• Mean–variance modeling: Models overdispersion as a function of the mean to capture biological variability across genes.
• Borrowing information across genes: Shares information across genes to improve robustness of statistical inference.
• Design effects and covariates: Integrates discrete experimental factors and continuous covariates into the modeling framework.
• Outlier detection and testing: Incorporates straightforward outlier detection and hypothesis testing approaches within the analysis.
• Comparison with alternatives: Evaluated against other RNA-Seq methods, showing advantages in scenarios with small sample sizes where penalized methods perform well.

Scientific Applications:

• Differential expression analysis: Improves inference of gene-level differential expression from RNA-Seq transcriptional counts.
• Gene regulation studies: Supports investigation of gene regulatory patterns by modeling count variability and overdispersion.
• Small-sample RNA-Seq studies: Suited for studies with limited replicates where penalization and shrinkage enhance statistical power.
• Genomic investigations: Applicable to broader genomic analyses that require precise modeling of transcriptional count data.

Methodology:

Uses a beta-binomial generalized linear model; applies penalization and shrinkage adapted from microarray analysis; extends mean–variance modeling by modeling overdispersion as a function of the mean; borrows information across genes; integrates design effects and continuous covariates; and applies outlier detection and testing approaches.