globalSeq

globalSeq applies a negative binomial random-effects omnibus test to evaluate associations between RNA-Seq data and other genomic datasets while accounting for overdispersion and high-dimensional explanatory variables.

Key Features:

• Negative binomial random-effects omnibus test: Applies a negative binomial distribution with a random-effects model to form an omnibus test for association.
• RNA-Seq overdispersion modeling: Models overdispersed RNA-Seq count data using the negative binomial distribution.
• High-dimensional predictor support: Accommodates scenarios where the number of explanatory variables exceeds the sample size.
• Regression-based overall significance testing: Implements a regression analysis framework to test overall significance of associations.
• Detection of genetic and epigenetic influences: Enables detection of genetic and epigenetic alterations that influence gene expression levels.
• Integration of genomic datasets: Integrates RNA-Seq data with other genomic datasets to assess multifaceted regulatory interactions.
• Regulatory mechanism examination: Facilitates examination of regulatory mechanisms governing gene expression.

Scientific Applications:

• Association testing: Tests associations between RNA-Seq data and other genomic datasets.
• Genetic and epigenetic alteration detection: Identifies genetic and epigenetic alterations associated with changes in gene expression.
• Regulatory mechanism analysis: Investigates regulatory mechanisms and multifaceted interactions driving gene expression patterns.
• Analysis under overdispersion and high dimensionality: Performs regression-based significance testing for overdispersed responses and high-dimensional explanatory variable settings.

Methodology:

globalSeq uses a regression framework that models RNA-Seq counts with a negative binomial distribution combined with a random-effects model to construct an omnibus test for overall significance in overdispersed and high-dimensional settings.