biostudies-literatureImoto YAMED-CRESTJST PRESTJSPS Grant-in-Aid for Specially Promoted ResearchMEXT Grant-in-Aid for Transformative Research Areas BJSPS Grant-in-Aid for Early-Career ScientistsJST MIRAI Program GrantWorld Premier International Research Center InitiativeJST CREST Mathematics Grante202201591https://www.ebi.ac.uk/biostudies/studies/S-EPMC9363502biostudies-literatureUnknown5(12)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 geneLife science allianceResolution of the curse of dimensionality in single-cell RNA sequencing data analysis.PMC936350222682401JPMJPR2021JP19gm1310002h20H057611565642922H0492017H0609818K14714Imoto YEscolar EGYabuta YKojima YYamamoto TYoshiwaki MNakamura THiraoka YKatou YSaitou MfalseResolution of the curse of dimensionality in single-cell RNA sequencing data analysis.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 gene2022-01-01T00:00:00Z2022 Aug2026-05-09T19:35:08.342Z2025-04-21T14:49:14.43ZS-EPMC93635023594493010.26508/lsa.202201591