What if we ignore the random effects when analyzing RNA-seq data in a multifactor experiment.

Area of Science:

• Bioinformatics
• Statistical Genetics
• Genomics

Background:

• RNA-sequencing (RNA-seq) is vital for identifying differentially expressed (DE) genes.
• Complex experimental designs (e.g., split-plot, repeated measures) are increasingly common in RNA-seq studies.
• Traditional analysis often simplifies complex designs, potentially compromising accuracy.

Purpose of the Study:

• To evaluate the impact of ignoring random effects in RNA-seq data analysis.
• To compare generalized linear mixed models (GLMM) with methods that disregard random effects.

Main Methods:

• Simulation studies were conducted to assess performance.
• Real-world RNA-seq data analysis was performed for validation.
• Standard GLMM was compared against simplified approaches.

Main Results:

• Ignoring random effects in multi-factor experiments increases false positives among top genes.
• Methods disregarding random effects can lead to reduced statistical power.
• GLMM provides a more robust analysis for complex RNA-seq designs.

Conclusions:

• Properly accounting for random effects using GLMM is essential for accurate RNA-seq analysis in complex designs.
• Simplified methods risk erroneous gene identification.
• Adopting GLMM ensures reliable identification of DE genes.