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Disentangling single-cell omics representation with a power spectral density-based feature extraction
Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obsc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178020/ https://www.ncbi.nlm.nih.gov/pubmed/35639509 http://dx.doi.org/10.1093/nar/gkac436 |
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author | Zandavi, Seid Miad Koch, Forrest C Vijayan, Abhishek Zanini, Fabio Mora, Fatima Valdes Ortega, David Gallego Vafaee, Fatemeh |
author_facet | Zandavi, Seid Miad Koch, Forrest C Vijayan, Abhishek Zanini, Fabio Mora, Fatima Valdes Ortega, David Gallego Vafaee, Fatemeh |
author_sort | Zandavi, Seid Miad |
collection | PubMed |
description | Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obscure discriminatory information, mask cellular subtype variations and complicate downstream analyses which can limit our understanding of cell function and tissue heterogeneity. Here, we present a novel pre-processing method (scPSD) inspired by power spectral density analysis that enhances the accuracy for cell subtype separation from large-scale single-cell omics data. We comprehensively benchmarked our method on a wide range of single-cell RNA-sequencing datasets and showed that scPSD pre-processing, while being fast and scalable, significantly reduces data complexity, enhances cell-type separation, and enables rare cell identification. Additionally, we applied scPSD to transcriptomics and chromatin accessibility cell atlases and demonstrated its capacity to discriminate over 100 cell types across the whole organism and across different modalities of single-cell omics data. |
format | Online Article Text |
id | pubmed-9178020 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-91780202022-06-09 Disentangling single-cell omics representation with a power spectral density-based feature extraction Zandavi, Seid Miad Koch, Forrest C Vijayan, Abhishek Zanini, Fabio Mora, Fatima Valdes Ortega, David Gallego Vafaee, Fatemeh Nucleic Acids Res Computational Biology Emerging single-cell technologies provide high-resolution measurements of distinct cellular modalities opening new avenues for generating detailed cellular atlases of many and diverse tissues. The high dimensionality, sparsity, and inaccuracy of single cell sequencing measurements, however, can obscure discriminatory information, mask cellular subtype variations and complicate downstream analyses which can limit our understanding of cell function and tissue heterogeneity. Here, we present a novel pre-processing method (scPSD) inspired by power spectral density analysis that enhances the accuracy for cell subtype separation from large-scale single-cell omics data. We comprehensively benchmarked our method on a wide range of single-cell RNA-sequencing datasets and showed that scPSD pre-processing, while being fast and scalable, significantly reduces data complexity, enhances cell-type separation, and enables rare cell identification. Additionally, we applied scPSD to transcriptomics and chromatin accessibility cell atlases and demonstrated its capacity to discriminate over 100 cell types across the whole organism and across different modalities of single-cell omics data. Oxford University Press 2022-05-25 /pmc/articles/PMC9178020/ /pubmed/35639509 http://dx.doi.org/10.1093/nar/gkac436 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Computational Biology Zandavi, Seid Miad Koch, Forrest C Vijayan, Abhishek Zanini, Fabio Mora, Fatima Valdes Ortega, David Gallego Vafaee, Fatemeh Disentangling single-cell omics representation with a power spectral density-based feature extraction |
title | Disentangling single-cell omics representation with a power spectral density-based feature extraction |
title_full | Disentangling single-cell omics representation with a power spectral density-based feature extraction |
title_fullStr | Disentangling single-cell omics representation with a power spectral density-based feature extraction |
title_full_unstemmed | Disentangling single-cell omics representation with a power spectral density-based feature extraction |
title_short | Disentangling single-cell omics representation with a power spectral density-based feature extraction |
title_sort | disentangling single-cell omics representation with a power spectral density-based feature extraction |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9178020/ https://www.ncbi.nlm.nih.gov/pubmed/35639509 http://dx.doi.org/10.1093/nar/gkac436 |
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