Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns

We presented a novel workflow for detecting distribution patterns in cell populations based on single-cell transcriptome study. With the fast adoption of single-cell analysis, a challenge to researchers is how to effectively extract gene features to meaningfully separate the cell population. Conside...

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Main Authors: Zhi Han, Travis Johnson, Jie Zhang, Xuan Zhang, Kun Huang
Format: Article
Language:English
Published: Hindawi Limited 2017-01-01
Series:BioMed Research International
Online Access:http://dx.doi.org/10.1155/2017/3035481
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spelling doaj-3c7352d5f9d643418766a92b360c9d7a2020-11-24T23:24:09ZengHindawi LimitedBioMed Research International2314-61332314-61412017-01-01201710.1155/2017/30354813035481Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression PatternsZhi Han0Travis Johnson1Jie Zhang2Xuan Zhang3Kun Huang4College of Software, Nankai University, Tianjin, ChinaDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USADepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USACollege of Software, Nankai University, Tianjin, ChinaDepartment of Biomedical Informatics, The Ohio State University, Columbus, OH, USAWe presented a novel workflow for detecting distribution patterns in cell populations based on single-cell transcriptome study. With the fast adoption of single-cell analysis, a challenge to researchers is how to effectively extract gene features to meaningfully separate the cell population. Considering that coexpressed genes are often functionally or structurally related and the number of coexpressed modules is much smaller than the number of genes, our workflow uses gene coexpression modules as features instead of individual genes. Thus, when the coexpressed modules are summarized into eigengenes, not only can we interactively explore the distribution of cells but also we can promptly interpret the gene features. The interactive visualization is aided by a novel application of spatial statistical analysis to the scatter plots using a clustering index parameter. This parameter helps to highlight interesting 2D patterns in the scatter plot matrix (SPLOM). We demonstrated the effectiveness of the workflow using two large single-cell studies. In the Allen Brain scRNA-seq dataset, the visual analytics suggested a new hypothesis such as the involvement of glutamate metabolism in the separation of the brain cells. In a large glioblastoma study, a sample with a unique cell migration related signature was identified.http://dx.doi.org/10.1155/2017/3035481
collection DOAJ
language English
format Article
sources DOAJ
author Zhi Han
Travis Johnson
Jie Zhang
Xuan Zhang
Kun Huang
spellingShingle Zhi Han
Travis Johnson
Jie Zhang
Xuan Zhang
Kun Huang
Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns
BioMed Research International
author_facet Zhi Han
Travis Johnson
Jie Zhang
Xuan Zhang
Kun Huang
author_sort Zhi Han
title Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns
title_short Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns
title_full Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns
title_fullStr Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns
title_full_unstemmed Functional Virtual Flow Cytometry: A Visual Analytic Approach for Characterizing Single-Cell Gene Expression Patterns
title_sort functional virtual flow cytometry: a visual analytic approach for characterizing single-cell gene expression patterns
publisher Hindawi Limited
series BioMed Research International
issn 2314-6133
2314-6141
publishDate 2017-01-01
description We presented a novel workflow for detecting distribution patterns in cell populations based on single-cell transcriptome study. With the fast adoption of single-cell analysis, a challenge to researchers is how to effectively extract gene features to meaningfully separate the cell population. Considering that coexpressed genes are often functionally or structurally related and the number of coexpressed modules is much smaller than the number of genes, our workflow uses gene coexpression modules as features instead of individual genes. Thus, when the coexpressed modules are summarized into eigengenes, not only can we interactively explore the distribution of cells but also we can promptly interpret the gene features. The interactive visualization is aided by a novel application of spatial statistical analysis to the scatter plots using a clustering index parameter. This parameter helps to highlight interesting 2D patterns in the scatter plot matrix (SPLOM). We demonstrated the effectiveness of the workflow using two large single-cell studies. In the Allen Brain scRNA-seq dataset, the visual analytics suggested a new hypothesis such as the involvement of glutamate metabolism in the separation of the brain cells. In a large glioblastoma study, a sample with a unique cell migration related signature was identified.
url http://dx.doi.org/10.1155/2017/3035481
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