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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Bibliographic Details
Published in:Nature biotechnology Vol. 42; no. 2; pp. 293 - 304
Main Authors: Hao, Yuhan, Stuart, Tim, Kowalski, Madeline H., Choudhary, Saket, Hoffman, Paul, Hartman, Austin, Srivastava, Avi, Molla, Gesmira, Madad, Shaista, Fernandez-Granda, Carlos, Satija, Rahul
Format: Journal Article
Language:English
Published: New York Nature Publishing Group US 01.02.2024
Nature Publishing Group
Subjects:
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
Online Access:Get full text
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Description
Summary: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.
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ISSN:1087-0156
1546-1696
1546-1696
DOI:10.1038/s41587-023-01767-y