Effect of high variation in transcript expression on identifying differentially expressed genes in RNA-seq analysis

Area of Science:

• Genomics and Bioinformatics
• Cancer Research
• Transcriptomics

Background:

• RNA sequencing (RNA-seq) is crucial for differential expression (DE) analysis.
• Establishing appropriate thresholds for fold change and adjusted p-value in DE gene (DEG) identification remains challenging.
• Current consensus often favors stringent thresholds for enhanced reliability.

Purpose of the Study:

• To investigate the impact of adjusted p-value and fold change thresholds on DE analysis reproducibility.
• To evaluate the influence of sample size on DEG identification reliability using RNA-seq data.
• To provide recommendations for optimal DEG screening in RNA-seq studies.

Main Methods:

• Analysis of RNA-seq data from three cancer types in The Cancer Genome Atlas (TCGA) database.
• Systematic evaluation of varying adjusted p-value and fold change thresholds.
• Assessment of DEG overlap rates across different sample sizes and threshold stringencies.
• Raw read count analysis to examine transcript expression variability.

Main Results:

• More stringent thresholds led to poorer reproducibility of DE results for a given sample size.
• Smaller sample sizes exhibited lower overlap rates of DEGs compared to larger sample sizes, irrespective of threshold.
• High transcript expression variability within and between sample groups caused significant fluctuations in fold change and adjusted p-values.

Conclusions:

• Stringent thresholds do not guarantee more reliable DEGs due to inherent transcript expression variations.
• DEG identification reliability is particularly susceptible to expression variability in small sample sizes.
• Less stringent thresholds and larger sample sizes are recommended for robust DEG screening in RNA-seq experiments.