VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies
dc.contributor.author | Chen, Mengjie | |
dc.contributor.author | Zhou, Xiang | |
dc.date.accessioned | 2018-11-18T04:25:05Z | |
dc.date.available | 2018-11-18T04:25:05Z | |
dc.date.issued | 2018-11-12 | |
dc.identifier.citation | Genome Biology. 2018 Nov 12;19(1):196 | |
dc.identifier.uri | https://doi.org/10.1186/s13059-018-1575-1 | |
dc.identifier.uri | https://hdl.handle.net/2027.42/146264 | |
dc.description.abstract | Abstract We develop a method, VIPER, to impute the zero values in single-cell RNA sequencing studies to facilitate accurate transcriptome quantification at the single-cell level. VIPER is based on nonnegative sparse regression models and is capable of progressively inferring a sparse set of local neighborhood cells that are most predictive of the expression levels of the cell of interest for imputation. A key feature of our method is its ability to preserve gene expression variability across cells after imputation. We illustrate the advantages of our method through several well-designed real data-based analytical experiments. | |
dc.title | VIPER: variability-preserving imputation for accurate gene expression recovery in single-cell RNA sequencing studies | |
dc.type | Article | en_US |
dc.description.bitstreamurl | https://deepblue.lib.umich.edu/bitstream/2027.42/146264/1/13059_2018_Article_1575.pdf | |
dc.language.rfc3066 | en | |
dc.rights.holder | The Author(s). | |
dc.date.updated | 2018-11-18T04:25:08Z | |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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