PT  - JOURNAL ARTICLE
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
TI  - Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis
AID  - 10.26508/lsa.202201591
DP  - 2022 Dec 01
TA  - Life Science Alliance
PG  - e202201591
VI  - 5
IP  - 12
4099  - https://www.life-science-alliance.org/content/5/12/e202201591.short
4100  - https://www.life-science-alliance.org/content/5/12/e202201591.full
SO  - Life Sci. Alliance2022 Dec 01; 5
AB  - 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.