Experimental validation of methods for differential gene expression analysis and sample pooling in RNA-seq.

Area of Science:

• Genomics
• Bioinformatics
• Molecular Biology

Background:

• RNA-sequencing (RNA-seq) is increasingly used for gene expression profiling, surpassing microarrays.
• Biologists face challenges in selecting appropriate differentially expressed gene (DEG) analysis methods and evaluating sample pooling strategies for RNA-seq.

Purpose of the Study:

• To experimentally validate DEG analysis methods (Cuffdiff2, edgeR, DESeq2, TSPM) for RNA-seq data.
• To assess the validity of sample pooling strategies by comparing pooled versus individual RNA samples.

Main Methods:

• RNA-seq experiment on mice amygdalae micro-punches.
• High-throughput quantitative PCR (qPCR) for independent validation of DEGs.
• Comparison of DEG analysis between pooled and individual RNA samples.

Main Results:

• Cuffdiff2 exhibited a high false-positive rate; DESeq2 and TSPM showed high false-negative rates.
• edgeR demonstrated relatively high sensitivity and specificity among the tested DEG analysis methods.
• Sample pooling introduced bias, leading to low positive predictive values for identified DEGs.

Conclusions:

• The combined use of sensitive DEG analysis methods and high-throughput validation is crucial for RNA-seq experiments.
• Sample pooling strategies have limited utility in similar RNA-seq setups.
• Increasing the number of biological replicate samples is recommended for robust RNA-seq analysis.