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Quasi-universality in single-cell sequencing data

Luis Aparicio, Mykola Bordyuh, Andrew J. Blumberg, Raul Rabadan
doi: https://doi.org/10.1101/426239
Luis Aparicio
1Department of Systems Biology and Columbia University, New York, NY10032, US
2Department of Biomedical Informatics and Columbia University, New York, NY10032, US
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Mykola Bordyuh
1Department of Systems Biology and Columbia University, New York, NY10032, US
2Department of Biomedical Informatics and Columbia University, New York, NY10032, US
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Andrew J. Blumberg
3Department of Mathematics University of Texas, Austin, US
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Raul Rabadan
1Department of Systems Biology and Columbia University, New York, NY10032, US
2Department of Biomedical Informatics and Columbia University, New York, NY10032, US
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  • For correspondence: rr2579@cumc.columbia.edu
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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.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted October 05, 2018.
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Quasi-universality in single-cell sequencing data
Luis Aparicio, Mykola Bordyuh, Andrew J. Blumberg, Raul Rabadan
bioRxiv 426239; doi: https://doi.org/10.1101/426239
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Quasi-universality in single-cell sequencing data
Luis Aparicio, Mykola Bordyuh, Andrew J. Blumberg, Raul Rabadan
bioRxiv 426239; doi: https://doi.org/10.1101/426239

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