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김현Hyun Kim

기초과학연구원 (IBS)

Nat. Commun., Apr 27 2024, 15 (1) | https://doi.org/10.1038/s41467-024-47884-3 scLENS: data-driven signal detection for unbiased scRNA-seq data analysis

Hyun Kim ¹, Won Chang ², Seok Joo Chae ^1,3, Jong-Eun Park ⁴, Minseok Seo⁵& Jae Kyoung Kim ^1,3,*

¹Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon 34126, Republic of Korea.
²Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH 45221, USA.
³Department of Mathematical Sciences, KAIST, Daejeon 34141, Republic of Korea.
⁴Graduate School of Medical Science and Engineering, KAIST, Daejeon 34141, Republic of Korea.
⁵Department of Computer and Information Science, Korea University, Sejong 30019, Republic of Korea.

^*Corresponding author: correspondence to Jae Kyoung Kim

Abstract

High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.

논문정보

형식Research article
게재일2024년 04월 (BRIC 등록일 2024-04-30)
연구진국내(교신)+국외 연구진
분야 생명과학 > 세포생물학

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