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G2S3: A gene graph-based imputation method for single-cell RNA sequencing data
2021
PLoS Computational Biology
Single-cell RNA sequencing technology provides an opportunity to study gene expression at single-cell resolution. However, prevalent dropout events result in high data sparsity and noise that may obscure downstream analyses in single-cell transcriptomic studies. We propose a new method, G2S3, that imputes dropouts by borrowing information from adjacent genes in a sparse gene graph learned from gene expression profiles across cells. We applied G2S3 and ten existing imputation methods to eight
doi:10.1371/journal.pcbi.1009029
pmid:34003861
pmcid:PMC8189489
fatcat:f4gjthqvjnaxjnznlss4bhbdfy