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Bilous M, Hérault L, Gabriel AA, Teleman M, Gfeller D. Building and analyzing metacells in single-cell genomics data. Mol Syst Biol 2024; 20:744-766. [PMID: 38811801 PMCID: PMC11220014 DOI: 10.1038/s44320-024-00045-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Revised: 05/03/2024] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
The advent of high-throughput single-cell genomics technologies has fundamentally transformed biological sciences. Currently, millions of cells from complex biological tissues can be phenotypically profiled across multiple modalities. The scaling of computational methods to analyze and visualize such data is a constant challenge, and tools need to be regularly updated, if not redesigned, to cope with ever-growing numbers of cells. Over the last few years, metacells have been introduced to reduce the size and complexity of single-cell genomics data while preserving biologically relevant information and improving interpretability. Here, we review recent studies that capitalize on the concept of metacells-and the many variants in nomenclature that have been used. We further outline how and when metacells should (or should not) be used to analyze single-cell genomics data and what should be considered when analyzing such data at the metacell level. To facilitate the exploration of metacells, we provide a comprehensive tutorial on the construction and analysis of metacells from single-cell RNA-seq data ( https://github.com/GfellerLab/MetacellAnalysisTutorial ) as well as a fully integrated pipeline to rapidly build, visualize and evaluate metacells with different methods ( https://github.com/GfellerLab/MetacellAnalysisToolkit ).
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Affiliation(s)
- Mariia Bilous
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Léonard Hérault
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Aurélie Ag Gabriel
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - Matei Teleman
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland
| | - David Gfeller
- Department of Oncology, Ludwig Institute for Cancer Research Lausanne, University of Lausanne, 1011, Lausanne, Switzerland.
- Agora Cancer Research Centre, 1011, Lausanne, Switzerland.
- Swiss Cancer Center Leman (SCCL), Lausanne, Switzerland.
- Swiss Institute of Bioinformatics (SIB), 1015, Lausanne, Switzerland.
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2
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Singh R, Wu AP, Mudide A, Berger B. Causal gene regulatory analysis with RNA velocity reveals an interplay between slow and fast transcription factors. Cell Syst 2024; 15:462-474.e5. [PMID: 38754366 DOI: 10.1016/j.cels.2024.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Revised: 08/25/2023] [Accepted: 04/18/2024] [Indexed: 05/18/2024]
Abstract
Single-cell expression dynamics, from differentiation trajectories or RNA velocity, have the potential to reveal causal links between transcription factors (TFs) and their target genes in gene regulatory networks (GRNs). However, existing methods either overlook these expression dynamics or necessitate that cells be ordered along a linear pseudotemporal axis, which is incompatible with branching trajectories. We introduce Velorama, an approach to causal GRN inference that represents single-cell differentiation dynamics as a directed acyclic graph of cells, constructed from pseudotime or RNA velocity measurements. Additionally, Velorama enables the estimation of the speed at which TFs influence target genes. Applying Velorama, we uncover evidence that the speed of a TF's interactions is tied to its regulatory function. For human corticogenesis, we find that slow TFs are linked to gliomas, while fast TFs are associated with neuropsychiatric diseases. We expect Velorama to become a critical part of the RNA velocity toolkit for investigating the causal drivers of differentiation and disease.
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Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.
| | - Alexander P Wu
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Anish Mudide
- Phillips Exeter Academy, Exeter, NH 03883, USA; Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, MIT, Cambridge, MA 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory and Department of Mathematics, MIT, Cambridge, MA 02139, USA.
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Hao Y, Stuart T, Kowalski MH, Choudhary S, Hoffman P, Hartman A, Srivastava A, Molla G, Madad S, Fernandez-Granda C, Satija R. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nat Biotechnol 2024; 42:293-304. [PMID: 37231261 PMCID: PMC10928517 DOI: 10.1038/s41587-023-01767-y] [Citation(s) in RCA: 226] [Impact Index Per Article: 226.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/28/2023] [Indexed: 05/27/2023]
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.
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Affiliation(s)
- Yuhan Hao
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Tim Stuart
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Madeline H Kowalski
- New York Genome Center, New York, NY, USA
- Institute for System Genetics, NYU Langone Medical Center, New York, NY, USA
| | - Saket Choudhary
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Paul Hoffman
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Austin Hartman
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
| | - Avi Srivastava
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | | | - Shaista Madad
- Center for Genomics and Systems Biology, New York University, New York, NY, USA
- New York Genome Center, New York, NY, USA
| | - Carlos Fernandez-Granda
- Center for Data Science, New York University, New York, NY, USA
- Courant Institute of Mathematical Sciences, New York University, New York, NY, USA
| | - Rahul Satija
- Center for Genomics and Systems Biology, New York University, New York, NY, USA.
- New York Genome Center, New York, NY, USA.
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Yuan Z, Yao J. Harnessing computational spatial omics to explore the spatial biology intricacies. Semin Cancer Biol 2023; 95:25-41. [PMID: 37400044 DOI: 10.1016/j.semcancer.2023.06.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 05/09/2023] [Accepted: 06/19/2023] [Indexed: 07/05/2023]
Abstract
Spatially resolved transcriptomics (SRT) has unlocked new dimensions in our understanding of intricate tissue architectures. However, this rapidly expanding field produces a wealth of diverse and voluminous data, necessitating the evolution of sophisticated computational strategies to unravel inherent patterns. Two distinct methodologies, gene spatial pattern recognition (GSPR) and tissue spatial pattern recognition (TSPR), have emerged as vital tools in this process. GSPR methodologies are designed to identify and classify genes exhibiting noteworthy spatial patterns, while TSPR strategies aim to understand intercellular interactions and recognize tissue domains with molecular and spatial coherence. In this review, we provide a comprehensive exploration of SRT, highlighting crucial data modalities and resources that are instrumental for the development of methods and biological insights. We address the complexities and challenges posed by the use of heterogeneous data in developing GSPR and TSPR methodologies and propose an optimal workflow for both. We delve into the latest advancements in GSPR and TSPR, examining their interrelationships. Lastly, we peer into the future, envisaging the potential directions and perspectives in this dynamic field.
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Affiliation(s)
- Zhiyuan Yuan
- Center for Medical Research and Innovation, Shanghai Pudong Hospital, Fudan University Pudong Medical Center, Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
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5
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DeMeo B, Berger B. SCA: recovering single-cell heterogeneity through information-based dimensionality reduction. Genome Biol 2023; 24:195. [PMID: 37626411 PMCID: PMC10464206 DOI: 10.1186/s13059-023-02998-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2022] [Accepted: 06/28/2023] [Indexed: 08/27/2023] Open
Abstract
Dimensionality reduction summarizes the complex transcriptomic landscape of single-cell datasets for downstream analyses. Current approaches favor large cellular populations defined by many genes, at the expense of smaller and more subtly defined populations. Here, we present surprisal component analysis (SCA), a technique that newly leverages the information-theoretic notion of surprisal for dimensionality reduction to promote more meaningful signal extraction. For example, SCA uncovers clinically important cytotoxic T-cell subpopulations that are indistinguishable using existing pipelines. We also demonstrate that SCA substantially improves downstream imputation. SCA's efficient information-theoretic paradigm has broad applications to the study of complex biological tissues in health and disease.
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Affiliation(s)
- Benjamin DeMeo
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, 02139, MA, USA
- Department of Biomedical Informatics, Harvard University, Cambridge, 02138, MA, USA
- Department of Mathematics, MIT, Cambridge, 02139, MA, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, 02139, MA, USA.
- Department of Mathematics, MIT, Cambridge, 02139, MA, USA.
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Berger B, Yu YW. Navigating bottlenecks and trade-offs in genomic data analysis. Nat Rev Genet 2023; 24:235-250. [PMID: 36476810 DOI: 10.1038/s41576-022-00551-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/27/2022] [Indexed: 12/12/2022]
Abstract
Genome sequencing and analysis allow researchers to decode the functional information hidden in DNA sequences as well as to study cell to cell variation within a cell population. Traditionally, the primary bottleneck in genomic analysis pipelines has been the sequencing itself, which has been much more expensive than the computational analyses that follow. However, an important consequence of the continued drive to expand the throughput of sequencing platforms at lower cost is that often the analytical pipelines are struggling to keep up with the sheer amount of raw data produced. Computational cost and efficiency have thus become of ever increasing importance. Recent methodological advances, such as data sketching, accelerators and domain-specific libraries/languages, promise to address these modern computational challenges. However, despite being more efficient, these innovations come with a new set of trade-offs, both expected, such as accuracy versus memory and expense versus time, and more subtle, including the human expertise needed to use non-standard programming interfaces and set up complex infrastructure. In this Review, we discuss how to navigate these new methodological advances and their trade-offs.
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Affiliation(s)
- Bonnie Berger
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
| | - Yun William Yu
- Department of Computer and Mathematical Sciences, University of Toronto Scarborough, Toronto, Ontario, Canada
- Tri-Campus Department of Mathematics, University of Toronto, Toronto, Ontario, Canada
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Song D, Xi NM, Li JJ, Wang L. scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data. Bioinformatics 2022; 38:3126-3127. [PMID: 35426898 PMCID: PMC9991884 DOI: 10.1093/bioinformatics/btac271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Revised: 03/02/2022] [Accepted: 04/12/2022] [Indexed: 02/07/2023] Open
Abstract
SUMMARY The number of cells measured in single-cell transcriptomic data has grown fast in recent years. For such large-scale data, subsampling is a powerful and often necessary tool for exploratory data analysis. However, the easiest random subsampling is not ideal from the perspective of preserving rare cell types. Therefore, diversity-preserving subsampling is required for fast exploration of cell types in a large-scale dataset. Here, we propose scSampler, an algorithm for fast diversity-preserving subsampling of single-cell transcriptomic data. AVAILABILITY AND IMPLEMENTATION scSampler is implemented in Python and is published under the MIT source license. It can be installed by "pip install scsampler" and used with the Scanpy pipline. The code is available on GitHub: https://github.com/SONGDONGYUAN1994/scsampler. An R interface is available at: https://github.com/SONGDONGYUAN1994/rscsampler. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Dongyuan Song
- Bioinformatics Interdepartmental Ph.D. Program, University of California, Los Angeles, CA 90095-7246, USA
| | - Nan Miles Xi
- Department of Mathematics and Statistics, Loyola University Chicago, Chicago, IL 60660-1601, USA
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA 90095-1554, USA
| | - Lin Wang
- Department of Statistics, The George Washington University, Washington, DC 20052-0086, USA
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8
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Baker DN, Dyjack N, Braverman V, Hicks SC, Langmead B. Fast and memory-efficient scRNA-seq k-means clustering with various distances. ACM-BCB ... ... : THE ... ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE. ACM CONFERENCE ON BIOINFORMATICS, COMPUTATIONAL BIOLOGY AND BIOMEDICINE 2021; 2021:24. [PMID: 34778889 PMCID: PMC8586878 DOI: 10.1145/3459930.3469523] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Single-cell RNA-sequencing (scRNA-seq) analyses typically begin by clustering a gene-by-cell expression matrix to empirically define groups of cells with similar expression profiles. We describe new methods and a new open source library, minicore, for efficient k-means++ center finding and k-means clustering of scRNA-seq data. Minicore works with sparse count data, as it emerges from typical scRNA-seq experiments, as well as with dense data from after dimensionality reduction. Minicore's novel vectorized weighted reservoir sampling algorithm allows it to find initial k-means++ centers for a 4-million cell dataset in 1.5 minutes using 20 threads. Minicore can cluster using Euclidean distance, but also supports a wider class of measures like Jensen-Shannon Divergence, Kullback-Leibler Divergence, and the Bhattachaiyya distance, which can be directly applied to count data and probability distributions. Further, minicore produces lower-cost centerings more efficiently than scikit-learn for scRNA-seq datasets with millions of cells. With careful handling of priors, minicore implements these distance measures with only minor (<2-fold) speed differences among all distances. We show that a minicore pipeline consisting of k-means++, localsearch++ and mini-batch k-means can cluster a 4-million cell dataset in minutes, using less than 10GiB of RAM. This memory-efficiency enables atlas-scale clustering on laptops and other commodity hardware. Finally, we report findings on which distance measures give clusterings that are most consistent with known cell type labels.
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Affiliation(s)
- Daniel N Baker
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Nathan Dyjack
- Department of Biostatistics, Johns Hopkins University, Bloomberg, School of Public Health, Baltimore, MD, USA
| | - Vladimir Braverman
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins University, Bloomberg, School of Public Health, Baltimore, MD, USA
| | - Ben Langmead
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
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Wu AP, Peng J, Berger B, Cho H. Bayesian information sharing enhances detection of regulatory associations in rare cell types. Bioinformatics 2021; 37:i349-i357. [PMID: 34252956 PMCID: PMC8275330 DOI: 10.1093/bioinformatics/btab269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
MOTIVATION Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge; this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective. RESULTS We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell type-specific gene regulation in the rapidly growing compendium of scRNA-seq datasets. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. AVAILABILITY AND IMPLEMENTATION The code for ShareNet is available at http://sharenet.csail.mit.edu and https://github.com/alexw16/sharenet.
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Affiliation(s)
- Alexander P Wu
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Jian Peng
- Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL 61801, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA.,Department of Mathematics, MIT, Cambridge, MA 02139, USA.,Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Hyunghoon Cho
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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Singh R, Hie BL, Narayan A, Berger B. Schema: metric learning enables interpretable synthesis of heterogeneous single-cell modalities. Genome Biol 2021; 22:131. [PMID: 33941239 PMCID: PMC8091541 DOI: 10.1186/s13059-021-02313-2] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 03/12/2021] [Indexed: 02/08/2023] Open
Abstract
A complete understanding of biological processes requires synthesizing information across heterogeneous modalities, such as age, disease status, or gene expression. Technological advances in single-cell profiling have enabled researchers to assay multiple modalities simultaneously. We present Schema, which uses a principled metric learning strategy that identifies informative features in a modality to synthesize disparate modalities into a single coherent interpretation. We use Schema to infer cell types by integrating gene expression and chromatin accessibility data; demonstrate informative data visualizations that synthesize multiple modalities; perform differential gene expression analysis in the context of spatial variability; and estimate evolutionary pressure on peptide sequences.
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Affiliation(s)
- Rohit Singh
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
| | - Brian L Hie
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ashwin Narayan
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Bonnie Berger
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
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