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

Single-cell multi-omics assays offer unprecedented opportunities to explore epigenetic regulation at cellular level. However, high levels of technical noise and data sparsity frequently lead to a lack of statistical power in correlative analyses, identifying very few, if any, significant associations between different molecular layers. Here we propose SCRaPL, a novel computational tool that increases power by carefully modelling noise in the experimental systems. We show on real and simulated multi-omics single-cell data sets that SCRaPL achieves higher sensitivity and better robustness in identifying correlations, while maintaining a similar level of false positives as standard analyses based on Pearson and Spearman correlation. Author summarySingle-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.

Automated summary

Single-cell multi-omics assays provide unprecedented opportunities to explore epigenetic regulation at the cellular level. However, high levels of technical noise and data sparsity often result in a lack of statistical power in correlative analyses. SCRaPL is a novel computational tool that addresses this issue by carefully modeling noise in the experimental systems.