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Kim H, Chang W, Chae SJ, Park JE, Seo M, Kim JK. scLENS: data-driven signal detection for unbiased scRNA-seq data analysis. Nat Commun 2024; 15:3575. [PMID: 38678050 DOI: 10.1038/s41467-024-47884-3] [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: 10/18/2023] [Accepted: 04/14/2024] [Indexed: 04/29/2024] Open
Abstract
High dimensionality and noise have limited the new biological insights that can be discovered in scRNA-seq data. While dimensionality reduction tools have been developed to extract biological signals from the data, they often require manual determination of signal dimension, introducing user bias. Furthermore, a common data preprocessing method, log normalization, can unintentionally distort signals in the data. Here, we develop scLENS, a dimensionality reduction tool that circumvents the long-standing issues of signal distortion and manual input. Specifically, we identify the primary cause of signal distortion during log normalization and effectively address it by uniformizing cell vector lengths with L2 normalization. Furthermore, we utilize random matrix theory-based noise filtering and a signal robustness test to enable data-driven determination of the threshold for signal dimensions. Our method outperforms 11 widely used dimensionality reduction tools and performs particularly well for challenging scRNA-seq datasets with high sparsity and variability. To facilitate the use of scLENS, we provide a user-friendly package that automates accurate signal detection of scRNA-seq data without manual time-consuming tuning.
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Affiliation(s)
- Hyun Kim
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
| | - Won Chang
- Division of Statistics and Data Science, University of Cincinnati, Cincinnati, OH, 45221, USA
| | - Seok Joo Chae
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea
| | - Jong-Eun Park
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, 34141, Republic of Korea
| | - Minseok Seo
- Department of Computer and Information Science, Korea University, Sejong, 30019, Republic of Korea
| | - Jae Kyoung Kim
- Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea.
- Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea.
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2
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Luecken MD, Gigante S, Burkhardt DB, Cannoodt R, Strobl DC, Markov NS, Zappia L, Palla G, Lewis W, Dimitrov D, Vinyard ME, Magruder DS, Andersson A, Dann E, Qin Q, Otto DJ, Klein M, Botvinnik OB, Deconinck L, Waldrant K, Bloom JM, Pisco AO, Saez-Rodriguez J, Wulsin D, Pinello L, Saeys Y, Theis FJ, Krishnaswamy S. Defining and benchmarking open problems in single-cell analysis. RESEARCH SQUARE 2024:rs.3.rs-4181617. [PMID: 38645152 PMCID: PMC11030530 DOI: 10.21203/rs.3.rs-4181617/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
With the growing number of single-cell analysis tools, benchmarks are increasingly important to guide analysis and method development. However, a lack of standardisation and extensibility in current benchmarks limits their usability, longevity, and relevance to the community. We present Open Problems, a living, extensible, community-guided benchmarking platform including 10 current single-cell tasks that we envision will raise standards for the selection, evaluation, and development of methods in single-cell analysis.
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Affiliation(s)
- Malte D Luecken
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Institute of Lung Health & Immunity, Helmholtz Munich; Member of the German Center for Lung Research (DZL), Munich, Germany
| | | | | | - Robrecht Cannoodt
- Data Intuitive, Lebbeke, Belgium
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | - Daniel C Strobl
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Institute of Clinical Chemistry and Pathobiochemistry, School of Medicine, Technical University of Munich, Munich, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany
| | - Nikolay S Markov
- Division of Pulmonary and Critical Care Medicine, Feinberg School of Medicine, Northwestern University
| | - Luke Zappia
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
| | - Giovanni Palla
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- TUM School of Life Sciences Weihenstephan, Technical University of Munich, Germany
| | - Wesley Lewis
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
| | - Daniel Dimitrov
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | - Michael E Vinyard
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - D S Magruder
- Department of Computer Science, Yale University, New Haven CT, USA
| | - Alma Andersson
- Genentech Inc
- Royal Institute of Technology (KTH), Gene Technology
- Science for Life Laboratory (SciLifeLab)
| | - Emma Dann
- Wellcome Sanger Institute, Wellcome Genome Campus, Cambridge, UK
| | - Qian Qin
- Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Dominik J Otto
- Basic Sciences Division, Fred Hutchinson Cancer Center, Seattle WA
- Computational Biology Program, Public Health Sciences Division, Seattle WA
- Translational Data Science IRC, Fred Hutchinson Cancer Center, Seattle WA
| | | | - Olga Borisovna Botvinnik
- Data Sciences Platform, Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158
- Bridge Bio Pharma, 3160 Porter Drive, Suite 250, Palo Alto, CA, 94304
| | - Louise Deconinck
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
| | | | | | - Angela Oliveira Pisco
- Data Sciences Platform, Chan Zuckerberg Biohub, 499 Illinois St, San Francisco, CA 94158
- Insitro, South San Francisco
| | - Julio Saez-Rodriguez
- Heidelberg University, Faculty of Medicine, and Heidelberg University Hospital, Institute for Computational Biomedicine, Heidelberg, Germany
| | | | - Luca Pinello
- Molecular Pathology Unit, Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA
| | - Yvan Saeys
- Data Mining and Modelling for Biomedicine group, VIB Center for Inflammation Research, Ghent, Belgium
- Department of Applied Mathematics, Computer Science, and Statistics, Ghent University, Ghent, Belgium
- VIB Center for AI & Computational Biology (VIB.AI), Gent, Belgium
| | - Fabian J Theis
- Institute of computational Biology, Helmholtz Munich, Neuherberg, Germany
- Department of Mathematics, School of Computing, Information and Technology, Technical University of Munich, Munich, Germany
- Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, UK (associated faculty)
| | - Smita Krishnaswamy
- Interdepartmental Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06511, USA
- Department of Computer Science, Yale University, New Haven CT, USA
- Department of Genetics, Yale University, New Haven CT, USA
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3
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Zhang K, Zemke NR, Armand EJ, Ren B. A fast, scalable and versatile tool for analysis of single-cell omics data. Nat Methods 2024; 21:217-227. [PMID: 38191932 PMCID: PMC10864184 DOI: 10.1038/s41592-023-02139-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 11/23/2023] [Indexed: 01/10/2024]
Abstract
Single-cell omics technologies have revolutionized the study of gene regulation in complex tissues. A major computational challenge in analyzing these datasets is to project the large-scale and high-dimensional data into low-dimensional space while retaining the relative relationships between cells. This low dimension embedding is necessary to decompose cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Traditional dimensionality reduction techniques, however, face challenges in computational efficiency and in comprehensively addressing cellular diversity across varied molecular modalities. Here we introduce a nonlinear dimensionality reduction algorithm, embodied in the Python package SnapATAC2, which not only achieves a more precise capture of single-cell omics data heterogeneities but also ensures efficient runtime and memory usage, scaling linearly with the number of cells. Our algorithm demonstrates exceptional performance, scalability and versatility across diverse single-cell omics datasets, including single-cell assay for transposase-accessible chromatin using sequencing, single-cell RNA sequencing, single-cell Hi-C and single-cell multi-omics datasets, underscoring its utility in advancing single-cell analysis.
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Affiliation(s)
- Kai Zhang
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Westlake Laboratory of Life Sciences and Biomedicine, School of Life Sciences, Westlake University, Hangzhou, China
| | - Nathan R Zemke
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA
| | - Ethan J Armand
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA, USA
| | - Bing Ren
- Department of Cellular and Molecular Medicine, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Center for Epigenomics, University of California, San Diego School of Medicine, La Jolla, CA, USA.
- Ludwig Institute for Cancer Research, La Jolla, CA, USA.
- Institute for Genomic Medicine, University of California, San Diego, La Jolla, CA, USA.
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4
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Cao Y, Tran A, Kim H, Robertson N, Lin Y, Torkel M, Yang P, Patrick E, Ghazanfar S, Yang J. Thinking process templates for constructing data stories with SCDNEY. F1000Res 2023; 12:261. [PMID: 38434622 PMCID: PMC10905113 DOI: 10.12688/f1000research.130623.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 12/08/2023] [Indexed: 03/05/2024] Open
Abstract
Background Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery. Methods We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more. Results Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression. Conclusions Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term 'living'.
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Affiliation(s)
- Yue Cao
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Andy Tran
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Hani Kim
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Nick Robertson
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Yingxin Lin
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Marni Torkel
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Pengyi Yang
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
- Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia
| | - Ellis Patrick
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Shila Ghazanfar
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Jean Yang
- Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China
- Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia
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5
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Zhang K, Zemke NR, Armand EJ, Ren B. SnapATAC2: a fast, scalable and versatile tool for analysis of single-cell omics data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.11.557221. [PMID: 37745443 PMCID: PMC10515871 DOI: 10.1101/2023.09.11.557221] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Single-cell omics technologies have ushered in a new era for the study of dynamic gene regulation in complex tissues during development and disease pathogenesis. A major computational challenge in analyzing these datasets is to project the large-scale and high dimensional data into low-dimensional space while retaining the relative relationships between cells in order to decompose the cellular heterogeneity and reconstruct cell-type-specific gene regulatory programs. Conventional dimensionality reduction methods suffer from computational inefficiency, difficulty to capture the full spectrum of cellular heterogeneity, or inability to apply across diverse molecular modalities. Here, we report a fast and nonlinear dimensionality reduction algorithm that not only more accurately captures the heterogeneities of single-cell omics data, but also features runtime and memory usage that is computational efficient and linearly proportional to cell numbers. We implement this algorithm in a Python package named SnapATAC2, and demonstrate its superior performance, remarkable scalability and general adaptability using an array of single-cell omics data types, including single-cell ATAC-seq, single-cell RNA-seq, single-cell Hi-C, and single-cell multiomics datasets.
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6
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Raimundo F, Prompsy P, Vert JP, Vallot C. A benchmark of computational pipelines for single-cell histone modification data. Genome Biol 2023; 24:143. [PMID: 37340307 PMCID: PMC10280832 DOI: 10.1186/s13059-023-02981-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Accepted: 06/07/2023] [Indexed: 06/22/2023] Open
Abstract
BACKGROUND Single-cell histone post translational modification (scHPTM) assays such as scCUT&Tag or scChIP-seq allow single-cell mapping of diverse epigenomic landscapes within complex tissues and are likely to unlock our understanding of various mechanisms involved in development or diseases. Running scHTPM experiments and analyzing the data produced remains challenging since few consensus guidelines currently exist regarding good practices for experimental design and data analysis pipelines. RESULTS We perform a computational benchmark to assess the impact of experimental parameters and data analysis pipelines on the ability of the cell representation to recapitulate known biological similarities. We run more than ten thousand experiments to systematically study the impact of coverage and number of cells, of the count matrix construction method, of feature selection and normalization, and of the dimension reduction algorithm used. This allows us to identify key experimental parameters and computational choices to obtain a good representation of single-cell HPTM data. We show in particular that the count matrix construction step has a strong influence on the quality of the representation and that using fixed-size bin counts outperforms annotation-based binning. Dimension reduction methods based on latent semantic indexing outperform others, and feature selection is detrimental, while keeping only high-quality cells has little influence on the final representation as long as enough cells are analyzed. CONCLUSIONS This benchmark provides a comprehensive study on how experimental parameters and computational choices affect the representation of single-cell HPTM data. We propose a series of recommendations regarding matrix construction, feature and cell selection, and dimensionality reduction algorithms.
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Affiliation(s)
- Félix Raimundo
- Google Research, Brain team, 75009, Paris, France
- Translational Research Department, Institut Curie, PSL Research University, 75005, Paris, France
| | - Pacôme Prompsy
- Translational Research Department, Institut Curie, PSL Research University, 75005, Paris, France
- CNRS UMR3244, Institut Curie, PSL Research University, 75005, Paris, France
| | - Jean-Philippe Vert
- Google Research, Brain team, 75009, Paris, France.
- Owkin, Inc, NY, New York, USA.
| | - Céline Vallot
- Translational Research Department, Institut Curie, PSL Research University, 75005, Paris, France.
- CNRS UMR3244, Institut Curie, PSL Research University, 75005, Paris, France.
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7
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Treppner M, Binder H, Hess M. Interpretable generative deep learning: an illustration with single cell gene expression data. Hum Genet 2022; 141:1481-1498. [PMID: 34988661 PMCID: PMC9360114 DOI: 10.1007/s00439-021-02417-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 08/06/2021] [Indexed: 11/26/2022]
Abstract
Deep generative models can learn the underlying structure, such as pathways or gene programs, from omics data. We provide an introduction as well as an overview of such techniques, specifically illustrating their use with single-cell gene expression data. For example, the low dimensional latent representations offered by various approaches, such as variational auto-encoders, are useful to get a better understanding of the relations between observed gene expressions and experimental factors or phenotypes. Furthermore, by providing a generative model for the latent and observed variables, deep generative models can generate synthetic observations, which allow us to assess the uncertainty in the learned representations. While deep generative models are useful to learn the structure of high-dimensional omics data by efficiently capturing non-linear dependencies between genes, they are sometimes difficult to interpret due to their neural network building blocks. More precisely, to understand the relationship between learned latent variables and observed variables, e.g., gene transcript abundances and external phenotypes, is difficult. Therefore, we also illustrate current approaches that allow us to infer the relationship between learned latent variables and observed variables as well as external phenotypes. Thereby, we render deep learning approaches more interpretable. In an application with single-cell gene expression data, we demonstrate the utility of the discussed methods.
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Affiliation(s)
- Martin Treppner
- Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center, University of Freiburg, Stefan-Meier-Str. 26, Freiburg, 79104, Germany.
| | - Harald Binder
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, 79104, Germany
| | - Moritz Hess
- Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, 79104, Germany
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8
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Dlamini Z, Skepu A, Kim N, Mkhabele M, Khanyile R, Molefi T, Mbatha S, Setlai B, Mulaudzi T, Mabongo M, Bida M, Kgoebane-Maseko M, Mathabe K, Lockhat Z, Kgokolo M, Chauke-Malinga N, Ramagaga S, Hull R. AI and precision oncology in clinical cancer genomics: From prevention to targeted cancer therapies-an outcomes based patient care. INFORMATICS IN MEDICINE UNLOCKED 2022. [DOI: 10.1016/j.imu.2022.100965] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
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9
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Agostinis F, Romualdi C, Sales G, Risso D. NewWave: a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA-seq data. Bioinformatics 2022; 38:2648-2650. [PMID: 35266509 PMCID: PMC9048694 DOI: 10.1093/bioinformatics/btac149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 02/06/2022] [Accepted: 03/08/2022] [Indexed: 11/30/2022] Open
Abstract
Summary We present NewWave, a scalable R/Bioconductor package for the dimensionality reduction and batch effect removal of single-cell RNA sequencing data. To achieve scalability, NewWave uses mini-batch optimization and can work with out-of-memory data, enabling users to analyze datasets with millions of cells. Availability and implementation NewWave is implemented as an open-source R package available through the Bioconductor project at https://bioconductor.org/packages/NewWave/ Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Federico Agostinis
- Department of Biology, Università degli Studi di Padova, Padova, 35100, Italy
| | - Chiara Romualdi
- Department of Biology, Università degli Studi di Padova, Padova, 35100, Italy
| | - Gabriele Sales
- Department of Biology, Università degli Studi di Padova, Padova, 35100, Italy
| | - Davide Risso
- Department of Statisical Science, Università degli studi di Padova, Padova, 35100, Italy
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10
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Dong X, Bacher R. Data-driven assessment of dimension reduction quality for single-cell omics data. PATTERNS 2022; 3:100465. [PMID: 35510193 PMCID: PMC9058902 DOI: 10.1016/j.patter.2022.100465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Dimension reduction (DR) techniques have become synonymous with single-cell omics data due to their ability to generate attractive visualizations and enable analyses of high-dimensional data. In this issue of Patterns, Johnsona et al. develop a statistical approach to assist in selecting high-quality reduced representations to improve analyses and biological interpretations.
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11
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Morelli L, Giansanti V, Cittaro D. Nested Stochastic Block Models applied to the analysis of single cell data. BMC Bioinformatics 2021; 22:576. [PMID: 34847879 PMCID: PMC8630903 DOI: 10.1186/s12859-021-04489-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 11/19/2021] [Indexed: 12/30/2022] Open
Abstract
Single cell profiling has been proven to be a powerful tool in molecular biology to understand the complex behaviours of heterogeneous system. The definition of the properties of single cells is the primary endpoint of such analysis, cells are typically clustered to underpin the common determinants that can be used to describe functional properties of the cell mixture under investigation. Several approaches have been proposed to identify cell clusters; while this is matter of active research, one popular approach is based on community detection in neighbourhood graphs by optimisation of modularity. In this paper we propose an alternative and principled solution to this problem, based on Stochastic Block Models. We show that such approach not only is suitable for identification of cell groups, it also provides a solid framework to perform other relevant tasks in single cell analysis, such as label transfer. To encourage the use of Stochastic Block Models, we developed a python library, schist, that is compatible with the popular scanpy framework.
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Affiliation(s)
- Leonardo Morelli
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milan, Italy
- Università Vita-Salute San Raffaele, Milan, Italy
| | - Valentina Giansanti
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milan, Italy
- Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy
| | - Davide Cittaro
- Center for Omics Sciences, IRCCS San Raffaele Institute, Milan, Italy.
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12
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Li Z, Kuppe C, Ziegler S, Cheng M, Kabgani N, Menzel S, Zenke M, Kramann R, Costa IG. Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen. Nat Commun 2021; 12:6386. [PMID: 34737275 PMCID: PMC8568974 DOI: 10.1038/s41467-021-26530-2] [Citation(s) in RCA: 44] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 10/04/2021] [Indexed: 12/18/2022] Open
Abstract
A major drawback of single-cell ATAC-seq (scATAC-seq) is its sparsity, i.e., open chromatin regions with no reads due to loss of DNA material during the scATAC-seq protocol. Here, we propose scOpen, a computational method based on regularized non-negative matrix factorization for imputing and quantifying the open chromatin status of regulatory regions from sparse scATAC-seq experiments. We show that scOpen improves crucial downstream analysis steps of scATAC-seq data as clustering, visualization, cis-regulatory DNA interactions, and delineation of regulatory features. We demonstrate the power of scOpen to dissect regulatory changes in the development of fibrosis in the kidney. This identifies a role of Runx1 and target genes by promoting fibroblast to myofibroblast differentiation driving kidney fibrosis.
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Affiliation(s)
- Zhijian Li
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany
| | - Christoph Kuppe
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, 52074, Aachen, Germany
| | - Susanne Ziegler
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany
| | - Mingbo Cheng
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany
| | - Nazanin Kabgani
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany
| | - Sylvia Menzel
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany
| | - Martin Zenke
- Department of Cell Biology, Institute of Biomedical Engineering, RWTH Aachen University Medical School, 52074, Aachen, Germany
- Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany
| | - Rafael Kramann
- Institute of Experimental Medicine and Systems Biology, RWTH Aachen University Medical School, 52074, Aachen, Germany.
- Division of Nephrology and Clinical Immunology, RWTH Aachen University, 52074, Aachen, Germany.
- Department of Internal Medicine, Nephrology and Transplantation, Erasmus Medical Center, 3015GD, Rotterdam, The Netherlands.
| | - Ivan G Costa
- Institute for Computational Genomics, Joint Research Center for Computational Biomedicine, RWTH Aachen University Medical School, 52074, Aachen, Germany.
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Moehlin J, Mollet B, Colombo BM, Mendoza-Parra MA. Inferring biologically relevant molecular tissue substructures by agglomerative clustering of digitized spatial transcriptomes with multilayer. Cell Syst 2021; 12:694-705.e3. [PMID: 34159899 DOI: 10.1016/j.cels.2021.04.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 01/08/2021] [Accepted: 04/13/2021] [Indexed: 01/04/2023]
Abstract
Spatially resolved transcriptomics (SrT) can investigate organ or tissue architecture from the angle of gene programs that define their molecular complexity. However, computational methods to analyze SrT data underexploit their spatial signature. Inspired by contextual pixel classification strategies applied to image analysis, we developed MULTILAYER to stratify maps into functionally relevant molecular substructures. MULTILAYER applies agglomerative clustering within contiguous locally defined transcriptomes (gene expression elements or "gexels") combined with community detection methods for graphical partitioning. MULTILAYER resolves molecular tissue substructures within a variety of SrT data with superior performance to commonly used dimensionality reduction strategies and still detects differentially expressed genes on par with existing methods. MULTILAYER can process high-resolution as well as multiple SrT data in a comparative mode, anticipating future needs in the field. MULTILAYER provides a digital image perspective for SrT analysis and opens the door to contextual gexel classification strategies for developing self-supervised molecular diagnosis solutions. A record of this paper's transparent peer review process is included in the supplemental information.
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Affiliation(s)
- Julien Moehlin
- Génomique métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Bastien Mollet
- Génomique métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France; École Normale Supérieure de Lyon, Université Claude Bernard - Lyon 1, Université de Lyon, 69342 Lyon Cedex 07, France
| | - Bruno Maria Colombo
- Génomique métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France
| | - Marco Antonio Mendoza-Parra
- Génomique métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Evry, Université Paris-Saclay, 91057, Evry, France.
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