Dictionary learning for integrative, multimodal and scalable single-cell analysis

Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Her...

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Vydané v:	Nature biotechnology Ročník 42; číslo 2; s. 293 - 304
Hlavní autori:	Hao, Yuhan, Stuart, Tim, Kowalski, Madeline H., Choudhary, Saket, Hoffman, Paul, Hartman, Austin, Srivastava, Avi, Molla, Gesmira, Madad, Shaista, Fernandez-Granda, Carlos, Satija, Rahul
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Nature Publishing Group US 01.02.2024 Nature Publishing Group
Predmet:	631/208/177 631/208/212 Agriculture Bioinformatics Biomedical and Life Sciences Biomedical Engineering/Biotechnology Biomedicine Biotechnology Chromatin Cytometry Datasets Dictionaries DNA methylation Epigenetics Gene expression Gene Expression Profiling - methods Gene sequencing Histones Humans Immune system Learning Life Sciences Mapping Proteomics Sequence Analysis, RNA - methods Single-Cell Analysis - methods Software Transcriptome Transcriptomics
ISSN:	1087-0156, 1546-1696, 1546-1696
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Popis
Shrnutí:	Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce ‘bridge integration’, a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a ‘dictionary’, which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit ( http://www.satijalab.org/seurat ), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities. Reference mapping is extended beyond scRNA-seq to single-cell epigenetic and proteomic data.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1087-0156 1546-1696 1546-1696
DOI:	10.1038/s41587-023-01767-y