deGPS

deGPS performs differential expression analysis of RNA-Seq data using generalized Poisson distribution normalization and permutation-based testing to control type I error and false discovery rates, including in datasets with abnormally high sequence read counts.

Key Features:

• Normalization Methodology: Employs generalized Poisson distribution modeling to normalize RNA-Seq count data and handle sequencing variability.
• Differential Expression Detection: Implements permutation-based tests to identify differentially expressed genes with statistical rigor.
• Error Control: Controls type I error and false discovery rates effectively, including under conditions of high sequence read counts.
• Comprehensive Evaluation: Validated on simulated datasets from TCGA RNA-Seq projects, unbiased benchmark data from the compcodeR package, and real RNA-Seq data from Drosophila development transcriptomes.
• Versatility: Applicable to other high-throughput sequencing platforms such as ChIP-Seq, MBD-Seq, and RIP-Seq.
• Parallel Computation: Supports parallel computations for efficient processing of large datasets.

Scientific Applications:

• Transcriptomics differential expression: Detects expression changes between biological or disease conditions in RNA-Seq studies.
• Gene regulation and disease studies: Provides differential expression results that inform gene regulation, disease mechanisms, and developmental processes.
• Large-scale RNA-Seq projects: Maintains stringent error control in large-scale datasets such as TCGA RNA-Seq projects.

Methodology:

Normalization using generalized Poisson distribution models followed by permutation-based differential expression testing.