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bioRxiv · 10.1101/2021.05.13.443959

SCRaPL: hierarchical Bayesian modelling of associations in single cell multi-omics data

Maniatis, C.·Vallejos, C. A.·Sanguinetti, G.
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Abstract

Single-cell multi-omics assays offer unprecedented opportunities to explore gene 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 correlation.

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Maniatis, C., Vallejos, C. A., Sanguinetti, G.. 2021-05-14. SCRaPL: hierarchical Bayesian modelling of associations in single cell multi-omics data. https://doi.org/10.1101/2021.05.13.443959

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