TY - JOUR T1 - Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis JF - Life Science Alliance JO - Life Sci. Alliance DO - 10.26508/lsa.202201591 VL - 5 IS - 12 SP - e202201591 AU - Yusuke Imoto AU - Tomonori Nakamura AU - Emerson G Escolar AU - Michio Yoshiwaki AU - Yoji Kojima AU - Yukihiro Yabuta AU - Yoshitaka Katou AU - Takuya Yamamoto AU - Yasuaki Hiraoka AU - Mitinori Saitou Y1 - 2022/12/01 UR - https://www.life-science-alliance.org/content/5/12/e202201591.abstract N2 - 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 genes, realizing precise delineation of cell fate transitions and identification of rare cells with all gene information. Compared with representative imputation methods, RECODE employs different principles and exhibits superior overall performance in cell-clustering, expression value recovery, and single-cell–level analysis. The RECODE algorithm is parameter-free, data-driven, deterministic, and high-speed, and its applicability can be predicted based on the variance normalization performance. We propose RECODE as a powerful strategy for preprocessing noisy high-dimensional data. ER -