Zero-preserving imputation of single-cell RNA-seq data.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 35017482.
- Also identified by DOI 10.1038/s41467-021-27729-z and PMC identifier 8752663.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
A key challenge in analyzing single cell RNA-sequencing data is the large number of false zeros, where genes actually expressed in a given cell are incorrectly measured as unexpressed. We present a method based on low-rank matrix approximation which imputes these values while preserving biologically non-expressed genes (true biological zeros) at zero expression levels. We provide theoretical justification for this denoising approach and demonstrate its advantages relative to other methods on simulated and biological datasets.
Medical subject headings
- Algorithms
- RNA
- Sequence Analysis, RNA