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

TLDR

Massively parallel reporter assays are widely used to study noncoding variation, yet analysis methods are largely ad‑hoc and comparative performance across datasets has been underexplored. The authors introduce mpralm, a calibrated and powerful method for differential activity analysis in MPRAs. mpralm models MPRA data using linear mixed‑effects models, and the authors evaluate both theoretical properties and real‑data effects of barcode summarization strategies. mpralm outperforms existing statistical methods, demonstrates that barcode summarization choice can significantly affect results, and shows that increasing replicates to at least four markedly boosts power, with the method available as an R package on Bioconductor.

Abstract

Massively parallel reporter assays (MPRAs) have emerged as a popular means for understanding noncoding variation in a variety of conditions. While a large number of experiments have been described in the literature, analysis typically uses ad-hoc methods. There has been little attention to comparing performance of methods across datasets. We present the mpralm method which we show is calibrated and powerful, by analyzing its performance on multiple MPRA datasets. We show that it outperforms existing statistical methods for analysis of this data type, in the first comprehensive evaluation of statistical methods on several datasets. We investigate theoretical and real-data properties of barcode summarization methods and show an unappreciated impact of summarization method for some datasets. Finally, we use our model to conduct a power analysis for this assay and show substantial improvements in power by performing up to 6 replicates per condition, whereas sequencing depth has smaller impact; we recommend to always use at least 4 replicates. An R package is available from the Bioconductor project. Together, these results inform recommendations for differential analysis, general group comparisons, and power analysis and will help improve design and analysis of MPRA experiments.