metaSeq

metaSeq analyzes high-throughput genomic and molecular biology data within the Bioconductor/R ecosystem to provide statistical integration and interpretation of gene expression profiles and sequencing datasets.

Key Features:

• Integration with Bioconductor: Leverages the Bioconductor ecosystem in R, including access to 934 interoperable packages for high-throughput data analysis.
• Statistical Methods: Combines probabilities using one-sided NOISeq with Fisher's method or Stouffer's method to integrate results from multiple tests.
• Open Development and Collaboration: Operates within Bioconductor's community-driven, open-development model to support collaborative package development.
• Quality Assurance: Relies on Bioconductor's formal initial review and continuous automated testing processes for package validation.

Scientific Applications:

• Differential expression analysis: Supports detection of differentially expressed genes from gene expression profiles and sequencing data.
• Identification of biomarkers: Facilitates identification of biomarkers by integrating statistical evidence across multiple tests.
• Functional annotation of genomic regions: Aids functional annotation of genomic regions using integrated statistical analysis of high-throughput datasets.
• Integration of multi-omics data: Enables integration of multi-omics datasets via combined probability methods and Bioconductor interoperability.

Methodology:

Combines one-sided NOISeq probabilities across tests using Fisher's method or Stouffer's method.