SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data

Christos Maniatis, Catalina A. Vallejos, Guido Sanguinetti

Abstract: Author summary Single-cell multi-omics assays offer unprecedented opportunities to explore epigenetic regulation at cellular level. However, high levels of noise frequently hide genomics regions with strong epigenetic regulation or produce misleading results. By carefully addressing this common problem SCRaPL aims become a useful tool in the hands of practitioners seeking to understand the role of particular genomic regions in the epigenetic landscape. Using different single cell multi-omics datasets, we have demonstrated that SCRaPL can increase detection rates up to five times compared to standard practices. This can improve performance of tools used for post experimental analysis, but more importantly it can indicate currently unknown genomic regions worth to further investigate.