SCRaPL: A Bayesian hierarchical framework for detecting technical associates in single cell multiomics data.
PLoS Comput Biol
; 18(6): e1010163, 2022 06.
Article
em En
| MEDLINE
| ID: mdl-35727848
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.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Epigênese Genética
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article