RSEQNORM

RSEQNORM normalizes RNA-sequencing data for FFPE samples using MIXnorm and SMIXnorm to adjust for technical variation while providing computational efficiency.

Key Features:

• MIXnorm: Existing method for FFPE samples with superior performance relative to traditional techniques for fresh frozen (FF) samples.
• SMIXnorm: Simplified two-component mixture model combining a normal distribution and a zero-inflated Poisson distribution, fitted via a nested Expectation-Maximization algorithm for computational efficiency.

Scientific Applications:

• FFPE RNA-seq analysis: Normalization of FFPE RNA-seq data to adjust for technical variations and improve biological interpretation.

Methodology:

SMIXnorm employs a simplified two-component mixture model with a normal component and a zero-inflated Poisson component and is fitted using a nested Expectation-Maximization algorithm.