Quasi-universality in single-cell sequencing data

TL;DR

This paper reveals that most eigenvalues of single-cell sequencing data follow universal distributions, identifies localized eigenvectors related to biological signals, and proposes a denoising strategy based on these findings.

Contribution

It introduces a novel application of Random Matrix Theory to single-cell data, enabling improved noise reduction and biological signal detection.

Findings

95% of eigenvalues follow universal distributions

Localization of eigenvectors indicates biological signals

Proposed denoising method outperforms standard techniques

Abstract

The development of single-cell technologies provides the opportunity to identify new cellular states and reconstruct novel cell-to-cell relationships. Applications range from understanding the transcriptional and epigenetic processes involved in metazoan development to characterizing distinct cells types in heterogeneous populations like cancers or immune cells. However, analysis of the data is impeded by its unknown intrinsic biological and technical variability together with its sparseness; these factors complicate the identification of true biological signals amidst artifact and noise. Here we show that, across technologies, roughly 95% of the eigenvalues derived from each single-cell data set can be described by universal distributions predicted by Random Matrix Theory. Interestingly, 5% of the spectrum shows deviations from these distributions and present a phenomenon known as eigenvector localization, where information tightly concentrates in groups of cells. Some of the localized eigenvectors reflect underlying biological signal, and some are simply a consequence of the sparsity of single cell data; roughly 3% is artifactual. Based on the universal distributions and a technique for detecting sparsity induced localization, we present a strategy to identify the residual 2% of directions that encode biological information and thereby denoise single-cell data. We demonstrate the effectiveness of this approach by comparing with standard single-cell data analysis techniques in a variety of examples with marked cell populations.