Linear models enable powerful differential activity analysis in massively parallel reporter assays.

Area of Science:

• Genomics and Bioinformatics
• Molecular Biology

Background:

• Massively parallel reporter assays (MPRAs) are widely used to study noncoding genetic variations.
• Current MPRA data analysis often relies on non-standardized, ad-hoc methods.
• There is a lack of comprehensive comparisons between different MPRA analysis techniques.

Purpose of the Study:

• To introduce and evaluate a novel statistical method, mpralm, for MPRA data analysis.
• To compare the performance of mpralm against existing statistical methods.
• To investigate the impact of barcode summarization techniques and provide recommendations for MPRA experimental design.

Main Methods:

• Development of the mpralm statistical method for MPRA data.
• Performance evaluation of mpralm across multiple MPRA datasets.
• Analysis of barcode summarization methods and power analysis for MPRA experiments.

Main Results:

• The mpralm method demonstrates calibrated and powerful performance on diverse MPRA datasets.
• mpralm shows superior performance compared to existing statistical methods in a comprehensive evaluation.
• Barcode summarization significantly impacts MPRA results; using at least 4 replicates substantially improves statistical power, more so than sequencing depth.

Conclusions:

• The mpralm method offers improved statistical rigor for MPRA data analysis.
• Findings provide evidence-based recommendations for differential analysis, group comparisons, and power analysis in MPRA studies.
• The study aims to enhance the design and analytical strategies for future MPRA experiments.