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

ABSTRACT: 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.

SUBMITTER: Maniatis C

PROVIDER: S-EPMC9249169 | biostudies-literature |

REPOSITORIES: biostudies-literature

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