ScLENS: data-driven signal detection for unbiased scRNA-seq data analysis.

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

SUBMITTER: Kim H 

PROVIDER: S-EPMC11519519 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature