Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Year: 2018
Type: article
Abstract: Dropout events in single-cell RNA sequencing (scRNA-seq) cause many transcripts to go undetected and induce an excess of zero read counts, leading to power issues in differential expression (DE) analysis. This has triggered the development of bespoke scRNA-seq DE methods to cope with zero inflation. Recent evaluations, however, have shown that dedicated scRNA-seq tools provide no advantage compared to traditional bulk RNA-seq tools. We introduce a weighting strategy, based on a zero-inflated negative binomial model, that identifies excess zero counts and generates gene- and cell-specific weights to unlock bulk RNA-seq DE pipelines for zero-inflated data, boosting performance for scRNA-seq.
Source: Genome biology
Institutions: Ghent University, University of California System, University of California, Berkeley, SIB Swiss Institute of Bioinformatics, University of Zurich, University of North Carolina at Chapel Hill, Weill Cornell Medicine, Inserm, Université Paris Sciences et Lettres, École Normale Supérieure - PSL, Département de mathématiques et applications, Centre de Bio-Informatique, École Nationale Supérieure des Mines de Paris, Institut Curie
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,490
Funders: Adolph C. and Mary Sprague Miller Institute for Basic Research in Science, University of California Berkeley, Agence Nationale de la Recherche, Fonds Wetenschappelijk Onderzoek, Universiteit Gent, Universität Zürich, Vlaamse regering, National Institutes of Health, European Research Council, National Institute of Mental Health, National Cancer Institute, FP7 Ideas: European Research Council
Awards: G1237558972, G2298855974, G3375519774, G4465247343, Statistical Methods for Cancer Clinical Trials, Classification of Cortical Neurons by Single Cell Transcriptomics, G4950536974, Statistical machine learning for complex biological data, Solutions Algorithmiques, Bioinformatiques et Logicielles pour le Séquençage Haut Débit, G9000034599, G9000265112
OpenAlex ID: W2788908087