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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Vydáno v:	Nature biotechnology Ročník 42; číslo 2; s. 293 - 304
Hlavní autoři:	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:	angličtina
Vydáno:	New York Nature Publishing Group US 01.02.2024 Nature Publishing Group
Témata:	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
On-line přístup:	Získat plný text
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Abstract	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.
AbstractList	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. 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. 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.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. 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.
Author	Kowalski, Madeline H. Hartman, Austin Satija, Rahul Molla, Gesmira Madad, Shaista Srivastava, Avi Hoffman, Paul Hao, Yuhan Choudhary, Saket Stuart, Tim Fernandez-Granda, Carlos
Author_xml	– sequence: 1 givenname: Yuhan surname: Hao fullname: Hao, Yuhan organization: Center for Genomics and Systems Biology, New York University, New York Genome Center – sequence: 2 givenname: Tim orcidid: 0000-0002-3044-0897 surname: Stuart fullname: Stuart, Tim organization: Center for Genomics and Systems Biology, New York University, New York Genome Center – sequence: 3 givenname: Madeline H. surname: Kowalski fullname: Kowalski, Madeline H. organization: New York Genome Center, Institute for System Genetics, NYU Langone Medical Center – sequence: 4 givenname: Saket orcidid: 0000-0001-5202-7633 surname: Choudhary fullname: Choudhary, Saket organization: Center for Genomics and Systems Biology, New York University, New York Genome Center – sequence: 5 givenname: Paul orcidid: 0000-0002-7693-8957 surname: Hoffman fullname: Hoffman, Paul organization: Center for Genomics and Systems Biology, New York University – sequence: 6 givenname: Austin surname: Hartman fullname: Hartman, Austin organization: Center for Genomics and Systems Biology, New York University – sequence: 7 givenname: Avi surname: Srivastava fullname: Srivastava, Avi organization: Center for Genomics and Systems Biology, New York University, New York Genome Center – sequence: 8 givenname: Gesmira surname: Molla fullname: Molla, Gesmira organization: New York Genome Center – sequence: 9 givenname: Shaista surname: Madad fullname: Madad, Shaista organization: Center for Genomics and Systems Biology, New York University, New York Genome Center – sequence: 10 givenname: Carlos surname: Fernandez-Granda fullname: Fernandez-Granda, Carlos organization: Center for Data Science, New York University, Courant Institute of Mathematical Sciences, New York University – sequence: 11 givenname: Rahul orcidid: 0000-0001-9448-8833 surname: Satija fullname: Satija, Rahul email: rsatija@nygenome.org organization: Center for Genomics and Systems Biology, New York University, New York Genome Center
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/37231261$$D View this record in MEDLINE/PubMed
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SubjectTerms	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
Title	Dictionary learning for integrative, multimodal and scalable single-cell analysis
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Volume	42
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