Disentangling single-cell omics representation with a power spectral density-based feature extraction.

Autor:	Zandavi SM; School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia.; Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA.; Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA.; Department of Pediatrics, Harvard Medical School, Boston, MA, USA., Koch FC; School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia., Vijayan A; School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia., Zanini F; Prince of Wales Clinical School, UNSW Sydney, Australia.; Cellular Genomics Future Institute, UNSW Sydney, Australia., Mora FV; Children's Cancer Institute, Lowy Cancer Research Centre, UNSW Sydney, Australia.; School of Women's and Children's Health, Faculty of Medicine, UNSW, Sydney, Australia., Ortega DG; School of Biomedical Engineering, University of Technology Sydney (UTS), Australia., Vafaee F; School of Biotechnology and Biomolecular Sciences, University of New South Wales (UNSW Sydney), Australia.; Cellular Genomics Future Institute, UNSW Sydney, Australia.; UNSW Data Science Hub (uDASH), UNSW Sydney, Australia.
Jazyk:	angličtina
Zdroj:	Nucleic acids research [Nucleic Acids Res] 2022 Jun 10; Vol. 50 (10), pp. 5482-5492.
DOI:	10.1093/nar/gkac436
Abstrakt:	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. (© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.)
Databáze:	MEDLINE
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