Analysis of gene expression dynamics and differential expression in viral infections using generalized linear models and quasi-likelihood methods.

ABSTRACT:

Introduction

Our study undertakes a detailed exploration of gene expression dynamics within human lung organ tissue equivalents (OTEs) in response to Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3) infections. Through the analysis of RNA-Seq data from 19,671 genes, we aim to identify differentially expressed genes under various infection conditions, elucidating the complexities of virus-host interactions.

Methods

We employ Generalized Linear Models (GLMs) with Quasi-Likelihood (QL) F-tests (GLMQL) and introduce the novel Magnitude-Altitude Score (MAS) and Relaxed Magnitude-Altitude Score (RMAS) algorithms to navigate the intricate landscape of RNA-Seq data. This approach facilitates the precise identification of potential biomarker

SUBMITTER: Rezapour M 

PROVIDER: S-EPMC11037428 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature