SVAPLSseq

SVAPLSseq models and adjusts latent biological and technical variation in RNA-seq data to improve detection of differentially expressed genes between two sample groups.

Key Features:

• Hidden Variability Adjustment: Captures latent variables in RNA-seq data that are not removed by standard normalization methods.
• Regression Framework Integration: Integrates inferred hidden variability into a regression framework to re-estimate primary signals for differential expression.
• Enhanced Statistical Power: Reduces residual expression heterogeneity to increase the power of statistical tests for detecting differentially expressed genes.

Scientific Applications:

• Gene Expression Profiling: Refines differential gene expression profiles by mitigating distortions from technical artifacts and unwanted biological variability in RNA-seq experiments.
• Research Validation: Applies to both simulated and real RNA-seq datasets for validating differential expression findings and comparing performance with existing techniques.

Methodology:

SVAPLSseq captures traces of hidden variability through a regression-based approach that re-estimates primary signals to identify truly differentially expressed genes.