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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.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1010163","type":"journal-article","created":{"date-parts":[[2022,6,21]],"date-time":"2022-06-21T13:41:30Z","timestamp":1655818890000},"page":"e1010163","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":3,"title":["SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics 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