Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis

Imoto, Y.; Nakamura, T.; Escolar, E. G.; Yoshiwaki, M.; Kojima, Y.; Yabuta, Y.; Katou, Y.; Yamamoto, T.; Hiraoka, Y.; Saitou, M.

doi:10.1101/2022.05.02.490246

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bioRxiv · 10.1101/2022.05.02.490246

Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis

Imoto, Y.·Nakamura, T.·Escolar, E. G.·Yoshiwaki, M.·Kojima, Y.·Yabuta, Y.·Katou, Y.·Yamamoto, T.·Hiraoka, Y.·Saitou, M.

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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 eliminates 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 to other representative imputation methods, RECODE employs different principles and exhibits superior overall performance in cell-clustering and single-cell level analysis. The RECODE algorithm is parameter-free, data-driven, deterministic, and high-speed, and notably, its applicability can be predicted based on the variance normalization performance. We propose RECODE as a general strategy for preprocessing noisy high-dimensional data.

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Imoto, Y., Nakamura, T., Escolar, E. G., Yoshiwaki, M., Kojima, Y., Yabuta, Y., Katou, Y., Yamamoto, T., Hiraoka, Y., Saitou, M.. 2022-05-02. Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis. https://doi.org/10.1101/2022.05.02.490246

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