Assessing the impact of parental linear gene normalization on the performance of statistical models for circular RNA differential expression analysis.

Incorporating linear and circular transcript information with filtering improves circRNA differential expression analysis, with edgeR's filterByExpr() providing the most reliable results across datasets.

• Why it matters: Accurate circRNA differential expression analysis is crucial for biomarker discovery, but the impact of filtering and normalization strategies remains underexplored, limiting reproducibility.
• What they did: The study evaluated various filtering and normalization strategies using experimental and simulated datasets, highlighting the importance of filtering thresholds and linear-aware methods like CIRI-DE.
• The result: Automated filtering combined with linear-aware normalization enhances sensitivity and reproducibility in circRNA DEA, supporting more reliable biomarker identification.