Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs
Current visualizations in microbiome research rely on aggregations in taxonomic classifications or do not show less abundant taxa. We introduce Snowflake: a new visualization method that creates a clear overview of the microbiome composition in collected samples without losing any information due to...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-02-01
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| Series: | Frontiers in Bioinformatics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fbinf.2024.1331043/full |
| _version_ | 1827357853366616064 |
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| author | Jannes Peeters Daniël M. Bot Gustavo Rovelo Ruiz Jan Aerts |
| author_facet | Jannes Peeters Daniël M. Bot Gustavo Rovelo Ruiz Jan Aerts |
| author_sort | Jannes Peeters |
| collection | DOAJ |
| description | Current visualizations in microbiome research rely on aggregations in taxonomic classifications or do not show less abundant taxa. We introduce Snowflake: a new visualization method that creates a clear overview of the microbiome composition in collected samples without losing any information due to classification or neglecting less abundant reads. Snowflake displays every observed OTU/ASV in the microbiome abundance table and provides a solution to include the data’s hierarchical structure and additional information obtained from downstream analysis (e.g., alpha- and beta-diversity) and metadata. Based on the value-driven ICE-T evaluation methodology, Snowflake was positively received. Experts in microbiome research found the visualizations to be user-friendly and detailed and liked the possibility of including and relating additional information to the microbiome’s composition. Exploring the topological structure of the microbiome abundance table allows them to quickly identify which taxa are unique to specific samples and which are shared among multiple samples (i.e., separating sample-specific taxa from the core microbiome), and see the compositional differences between samples. An R package for constructing and visualizing Snowflake microbiome composition graphs is available at https://gitlab.com/vda-lab/snowflake. |
| first_indexed | 2024-03-08T05:54:02Z |
| format | Article |
| id | doaj.art-d3c4276faee5436181c93eb17066ace2 |
| institution | Directory Open Access Journal |
| issn | 2673-7647 |
| language | English |
| last_indexed | 2024-03-08T05:54:02Z |
| publishDate | 2024-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioinformatics |
| spelling | doaj.art-d3c4276faee5436181c93eb17066ace22024-02-05T04:54:28ZengFrontiers Media S.A.Frontiers in Bioinformatics2673-76472024-02-01410.3389/fbinf.2024.13310431331043Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphsJannes Peeters0Daniël M. Bot1Gustavo Rovelo Ruiz2Jan Aerts3Data Science Institute, Hasselt University, Diepenbeek, BelgiumData Science Institute, Hasselt University, Diepenbeek, BelgiumExpertise Center for Digital Media, Hasselt University—Flanders Make, Diepenbeek, BelgiumVisual Data Analysis Lab, Department of Biosystems, KU Leuven, Leuven, BelgiumCurrent visualizations in microbiome research rely on aggregations in taxonomic classifications or do not show less abundant taxa. We introduce Snowflake: a new visualization method that creates a clear overview of the microbiome composition in collected samples without losing any information due to classification or neglecting less abundant reads. Snowflake displays every observed OTU/ASV in the microbiome abundance table and provides a solution to include the data’s hierarchical structure and additional information obtained from downstream analysis (e.g., alpha- and beta-diversity) and metadata. Based on the value-driven ICE-T evaluation methodology, Snowflake was positively received. Experts in microbiome research found the visualizations to be user-friendly and detailed and liked the possibility of including and relating additional information to the microbiome’s composition. Exploring the topological structure of the microbiome abundance table allows them to quickly identify which taxa are unique to specific samples and which are shared among multiple samples (i.e., separating sample-specific taxa from the core microbiome), and see the compositional differences between samples. An R package for constructing and visualizing Snowflake microbiome composition graphs is available at https://gitlab.com/vda-lab/snowflake.https://www.frontiersin.org/articles/10.3389/fbinf.2024.1331043/fullmicrobiome compositiontaxonomymetagenomicsvisualization methodvisualization application |
| spellingShingle | Jannes Peeters Daniël M. Bot Gustavo Rovelo Ruiz Jan Aerts Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs Frontiers in Bioinformatics microbiome composition taxonomy metagenomics visualization method visualization application |
| title | Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs |
| title_full | Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs |
| title_fullStr | Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs |
| title_full_unstemmed | Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs |
| title_short | Snowflake: visualizing microbiome abundance tables as multivariate bipartite graphs |
| title_sort | snowflake visualizing microbiome abundance tables as multivariate bipartite graphs |
| topic | microbiome composition taxonomy metagenomics visualization method visualization application |
| url | https://www.frontiersin.org/articles/10.3389/fbinf.2024.1331043/full |
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