FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets

High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique...

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Published inNature protocols Vol. 15; no. 2; pp. 398 - 420
Main Authors Ko, Melissa E., Williams, Corey M., Fread, Kristen I., Goggin, Sarah M., Rustagi, Rohit S., Fragiadakis, Gabriela K., Nolan, Garry P., Zunder, Eli R.
Format Journal Article
LanguageEnglish
Published London Nature Publishing Group UK 01.02.2020
Nature Publishing Group
Subjects
631/114
631/1647/1407/1492
631/1647/514/1949
631/1647/794
631/553
Algorithms
Analysis
Analytical Chemistry
Biological Techniques
Biomedical and Life Sciences
Cell differentiation
Comparative analysis
Computational Biology/Bioinformatics
Computer Graphics
Datasets
Differentiation (biology)
Drug resistance
Ectoderm
Embryo cells
Embryonic stem cells
Endoderm
Flow cytometry
Flow mapping
Gene sequencing
Graphical user interface
Layouts
Life Sciences
Mesoderm
Microarrays
Organic Chemistry
Protocol
R (Programming language)
Ribonucleic acid
RNA
Single-Cell Analysis - methods
Software
Software development tools
Source code
Statistics
Stem cell transplantation
Stem cells
Technology application
Tumorigenesis
Unicellular organisms
User interface
User-Computer Interface
United States
Online AccessGet full text
ISSN1754-2189
1750-2799
1750-2799
DOI10.1038/s41596-019-0246-3

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Abstract High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete. This protocol describes FLOW-MAP, a graph-based algorithm for visualizing cellular trajectories in single-cell time course datasets. The R package can be operated via its GUI or using text-based commands.
AbstractList High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.This protocol describes FLOW-MAP, a graph-based algorithm for visualizing cellular trajectories in single-cell time course datasets. The R package can be operated via its GUI or using text-based commands.
High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.
High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete.
High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it challenging to perform comprehensive analysis. One particular challenge is the analysis of single-cell time course datasets: how to identify unique cell populations and track how they change across time points. To facilitate this analysis, we developed FLOW-MAP, a graphical user interface (GUI)-based software tool that uses graph layout analysis with sequential time ordering to visualize cellular trajectories in high-dimensional single-cell datasets obtained from flow cytometry, mass cytometry or single-cell RNA sequencing (scRNAseq) experiments. Here we provide a detailed description of the FLOW-MAP algorithm and how to use the open-source R package FLOWMAPR via its GUI or with text-based commands. This approach can be applied to many dynamic processes, including in vitro stem cell differentiation, in vivo development, oncogenesis, the emergence of drug resistance and cell signaling dynamics. To demonstrate our approach, we perform a step-by-step analysis of a single-cell mass cytometry time course dataset from mouse embryonic stem cells differentiating into the three germ layers: endoderm, mesoderm and ectoderm. In addition, we demonstrate FLOW-MAP analysis of a previously published scRNAseq dataset. Using both synthetic and experimental datasets for comparison, we perform FLOW-MAP analysis side by side with other single-cell analysis methods, to illustrate when it is advantageous to use the FLOW-MAP approach. The protocol takes between 30 min and 1.5 h to complete. This protocol describes FLOW-MAP, a graph-based algorithm for visualizing cellular trajectories in single-cell time course datasets. The R package can be operated via its GUI or using text-based commands.
Audience Academic
Author Goggin, Sarah M.
Fread, Kristen I.
Fragiadakis, Gabriela K.
Nolan, Garry P.
Zunder, Eli R.
Williams, Corey M.
Ko, Melissa E.
Rustagi, Rohit S.
AuthorAffiliation 6 These authors contributed equally: Melissa E. Ko, Corey M. Williams
5 Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
3 Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
1 Cancer Biology Program, Stanford School of Medicine, Stanford, CA, USA
2 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
4 Neuroscience Graduate Program, University of Virginia, Charlottesville, VA, USA
AuthorAffiliation_xml – name: 3 Robert M. Berne Cardiovascular Research Center, University of Virginia, Charlottesville, VA, USA
– name: 2 Department of Biomedical Engineering, University of Virginia, Charlottesville, VA, USA
– name: 1 Cancer Biology Program, Stanford School of Medicine, Stanford, CA, USA
– name: 4 Neuroscience Graduate Program, University of Virginia, Charlottesville, VA, USA
– name: 5 Department of Microbiology and Immunology, Stanford University, Stanford, CA, USA
– name: 6 These authors contributed equally: Melissa E. Ko, Corey M. Williams
Author_xml – sequence: 1
  givenname: Melissa E.
  surname: Ko
  fullname: Ko, Melissa E.
  organization: Cancer Biology Program, Stanford School of Medicine
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  fullname: Williams, Corey M.
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  surname: Goggin
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  surname: Rustagi
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  organization: Department of Microbiology and Immunology, Stanford University
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  givenname: Eli R.
  surname: Zunder
  fullname: Zunder, Eli R.
  email: ezunder@virginia.edu
  organization: Department of Biomedical Engineering, University of Virginia
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31932774$$D View this record in MEDLINE/PubMed
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Issue 2
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content type line 14
content type line 23
E.R.Z. conceptualized the FLOW-MAP algorithm. E.R.Z., G.K.F., and G.P.N. designed the mESC differentiation experiment. E.R.Z. and G.K.F. performed the mESC differentiation experiment and collected cell samples. E.R.Z. performed antibody staining and mass cytometry measurement. M.E.K., E.R.Z., S.M.G., C.M.W. and R.S.R. wrote the FLOW-MAP code. M.E.K., C.M.W., K.I.F. and E.R.Z. analyzed and interpreted the data. M.E.K., C.M.W. and E.R.Z. wrote the manuscript. All authors edited, read and approved the manuscript.
Author contributions
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Snippet High-dimensional single-cell technologies present new opportunities for biological discovery, but the complex nature of the resulting datasets makes it...
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631/1647/1407/1492
631/1647/514/1949
631/1647/794
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Algorithms
Analysis
Analytical Chemistry
Biological Techniques
Biomedical and Life Sciences
Cell differentiation
Comparative analysis
Computational Biology/Bioinformatics
Computer Graphics
Datasets
Differentiation (biology)
Drug resistance
Ectoderm
Embryo cells
Embryonic stem cells
Endoderm
Flow cytometry
Flow mapping
Gene sequencing
Graphical user interface
Layouts
Life Sciences
Mesoderm
Microarrays
Organic Chemistry
Protocol
R (Programming language)
Ribonucleic acid
RNA
Single-Cell Analysis - methods
Software
Software development tools
Source code
Statistics
Stem cell transplantation
Stem cells
Technology application
Tumorigenesis
Unicellular organisms
User interface
User-Computer Interface
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Title FLOW-MAP: a graph-based, force-directed layout algorithm for trajectory mapping in single-cell time course datasets
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