SCIBER: a simple method for removing batch effects from single-cell RNA-sequencing data
Abstract
Integrative analysis of multiple single-cell RNA-sequencing datasets allows for more comprehensive characterizations of cell types, but systematic technical differences between datasets, known as ‘batch effects’, need to be removed before integration to avoid misleading interpretation of the data. Although many batch-effect-removal methods have been developed, there is still a large room for improvement: most existing methods only give dimension-reduced data instead of expression data of individual genes, are based on computationally demanding models and are black-box models and thus difficult to interpret or tune.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 22, 2022
- Source ID
- 10.1093/bioinformatics/btac819
Entities
People
- Dailin Gan
- Jun Li
Organizations
- National Institutes of Health
- University of Notre Dame