{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Imoto Y"],"funding":["AMED-CREST","JST PREST","JSPS Grant-in-Aid for Specially Promoted Research","MEXT Grant-in-Aid for Transformative Research Areas B","JSPS Grant-in-Aid for Early-Career Scientists","JST MIRAI Program Grant","World Premier International Research Center Initiative","JST CREST Mathematics Grant"],"pagination":["e202201591"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9363502"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["5(12)"],"pubmed_abstract":["Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous individual cells simultaneously, promoting progress in the biomedical sciences. However, scRNA-seq data are high-dimensional with substantial technical noise, including dropouts. During analysis of scRNA-seq data, such noise engenders a statistical problem known as the curse of dimensionality (COD). Based on high-dimensional statistics, we herein formulate a noise reduction method, RECODE (resolution of the curse of dimensionality), for high-dimensional data with random sampling noise. We show that RECODE consistently resolves COD in relevant scRNA-seq data with unique molecular identifiers. RECODE does not involve dimension reduction and recovers expression values for all genes, including lowly expressed gene"],"journal":["Life science alliance"],"pubmed_title":["Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis."],"pmcid":["PMC9363502"],"funding_grant_id":["22682401","JPMJPR2021","JP19gm1310002h","20H05761","15656429","22H04920","17H06098","18K14714"],"pubmed_authors":["Imoto Y","Escolar EG","Yabuta Y","Kojima Y","Yamamoto T","Yoshiwaki M","Nakamura T","Hiraoka Y","Katou Y","Saitou M"],"additional_accession":[]},"is_claimable":false,"name":"Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis.","description":"Single-cell RNA sequencing (scRNA-seq) can determine gene expression in numerous individual cells simultaneously, promoting progress in the biomedical sciences. However, scRNA-seq data are high-dimensional with substantial technical noise, including dropouts. During analysis of scRNA-seq data, such noise engenders a statistical problem known as the curse of dimensionality (COD). Based on high-dimensional statistics, we herein formulate a noise reduction method, RECODE (resolution of the curse of dimensionality), for high-dimensional data with random sampling noise. We show that RECODE consistently resolves COD in relevant scRNA-seq data with unique molecular identifiers. RECODE does not involve dimension reduction and recovers expression values for all genes, including lowly expressed gene","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Aug","modification":"2026-05-09T19:35:08.342Z","creation":"2025-04-21T14:49:14.43Z"},"accession":"S-EPMC9363502","cross_references":{"pubmed":["35944930"],"doi":["10.26508/lsa.202201591"]}}