{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Islam MT"],"funding":["Google","NCI NIH HHS","NIH"],"pagination":["7142"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9681852"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["Single cell RNA sequencing is a promising technique to determine the states of individual cells and classify novel cell subtypes. In current sequence data analysis, however, genes with low expressions are omitted, which leads to inaccurate gene counts and hinders downstream analysis. Recovering these omitted expression values presents a challenge because of the large size of the data. Here, we introduce a data-driven gene expression recovery framework, referred to as self-consistent expression recovery machine (SERM), to impute the missing expressions. Using a neural network, the technique first learns the underlying data distribution from a subset of the noisy data. It then recovers the overall expression data by imposing a self-consistency on the expression matrix, thus ensuring that the"],"journal":["Nature communications"],"pubmed_title":["Leveraging data-driven self-consistency for high-fidelity gene expression recovery."],"pmcid":["PMC9681852"],"funding_grant_id":["Faculty Research Award","R01 CA227713","R01 CA223667"],"pubmed_authors":["Ren H","Li X","Shen L","Yu L","Xing L","Sang S","Zhao W","Khuzani MB","Wang JY","Islam MT"],"additional_accession":[]},"is_claimable":false,"name":"Leveraging data-driven self-consistency for high-fidelity gene expression recovery.","description":"Single cell RNA sequencing is a promising technique to determine the states of individual cells and classify novel cell subtypes. In current sequence data analysis, however, genes with low expressions are omitted, which leads to inaccurate gene counts and hinders downstream analysis. Recovering these omitted expression values presents a challenge because of the large size of the data. Here, we introduce a data-driven gene expression recovery framework, referred to as self-consistent expression recovery machine (SERM), to impute the missing expressions. Using a neural network, the technique first learns the underlying data distribution from a subset of the noisy data. It then recovers the overall expression data by imposing a self-consistency on the expression matrix, thus ensuring that the","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Nov","modification":"2026-06-03T10:40:51.48Z","creation":"2025-04-06T08:38:32.697Z"},"accession":"S-EPMC9681852","cross_references":{"pubmed":["36414658"],"doi":["10.1038/s41467-022-34595-w"]}}