Modelling RNA-Seq data with a zero-inflated mixture Poisson linear model.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• RNA sequencing (RNA-Seq) generates extensive genomic data, including nucleotide sequences and read counts.
• RNA-Seq count data often exhibit non-uniformity due to systematic sequencing biases.
• Current models, like the Poisson linear model, may not fully capture the complexity of RNA-Seq count distributions.

Purpose of the Study:

• To address limitations of existing models for RNA-Seq count data.
• To propose a novel statistical model that accounts for mixture structures and zero-inflation in RNA-Seq counts.
• To develop an efficient algorithm for parameter estimation in the proposed model.

Main Methods:

• Development of a zero-inflated mixture Poisson linear model tailored for RNA-Seq count data.
• Derivation of a fast expectation-maximization (EM) algorithm for parameter estimation.
• Evaluation of the proposed method through numerical studies.

Main Results:

• The proposed zero-inflated mixture Poisson model effectively handles the complexity of RNA-Seq count data.
• The derived EM algorithm provides an efficient approach for parameter estimation.
• Numerical studies demonstrate the superior performance of the new model compared to existing methods.

Conclusions:

• The zero-inflated mixture Poisson linear model offers a more accurate approach for analyzing RNA-Seq count data.
• This method enhances the understanding of genomic variations and biases in sequencing data.
• The developed algorithm facilitates robust analysis of large-scale genomic datasets.