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A Random Matrix Theory Approach to Denoise Single-Cell Data
Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and de...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660363/ https://www.ncbi.nlm.nih.gov/pubmed/33205104 http://dx.doi.org/10.1016/j.patter.2020.100035 |
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author | Aparicio, Luis Bordyuh, Mykola Blumberg, Andrew J. Rabadan, Raul |
author_facet | Aparicio, Luis Bordyuh, Mykola Blumberg, Andrew J. Rabadan, Raul |
author_sort | Aparicio, Luis |
collection | PubMed |
description | Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition, we explain how sparsity can cause spurious eigenvector localization, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory, about 3% is spurious signal induced by sparsity, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations. |
format | Online Article Text |
id | pubmed-7660363 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-76603632020-11-16 A Random Matrix Theory Approach to Denoise Single-Cell Data Aparicio, Luis Bordyuh, Mykola Blumberg, Andrew J. Rabadan, Raul Patterns (N Y) Article Single-cell technologies provide the opportunity to identify new cellular states. However, a major obstacle to the identification of biological signals is noise in single-cell data. In addition, single-cell data are very sparse. We propose a new method based on random matrix theory to analyze and denoise single-cell sequencing data. The method uses the universal distributions predicted by random matrix theory for the eigenvalues and eigenvectors of random covariance/Wishart matrices to distinguish noise from signal. In addition, we explain how sparsity can cause spurious eigenvector localization, falsely identifying meaningful directions in the data. We show that roughly 95% of the information in single-cell data is compatible with the predictions of random matrix theory, about 3% is spurious signal induced by sparsity, and only the last 2% reflects true biological signal. We demonstrate the effectiveness of our approach by comparing with alternative techniques in a variety of examples with marked cell populations. Elsevier 2020-05-04 /pmc/articles/PMC7660363/ /pubmed/33205104 http://dx.doi.org/10.1016/j.patter.2020.100035 Text en © 2020 The Author(s) http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Article Aparicio, Luis Bordyuh, Mykola Blumberg, Andrew J. Rabadan, Raul A Random Matrix Theory Approach to Denoise Single-Cell Data |
title | A Random Matrix Theory Approach to Denoise Single-Cell Data |
title_full | A Random Matrix Theory Approach to Denoise Single-Cell Data |
title_fullStr | A Random Matrix Theory Approach to Denoise Single-Cell Data |
title_full_unstemmed | A Random Matrix Theory Approach to Denoise Single-Cell Data |
title_short | A Random Matrix Theory Approach to Denoise Single-Cell Data |
title_sort | random matrix theory approach to denoise single-cell data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7660363/ https://www.ncbi.nlm.nih.gov/pubmed/33205104 http://dx.doi.org/10.1016/j.patter.2020.100035 |
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