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Visualizing ’omic feature rankings and log-ratios using Qurro
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Abstract
Many tools for dealing with compositional ‘ ’omics’ data produce feature-wise values that can be ranked in order to describe features’ associations with some sort of variation. These values include differentials (which describe features’ associations with specified covariates) and feature loadings (which describe features’ associations with variation along a given axis in a biplot). Although prior work has discussed the use of these ‘rankings’ as a starting point for exploring the log-ratios of particularly high- or low-ranked features, such exploratory analyses have previously been done using custom code to visualize feature rankings and the log-ratios of interest. This approach is laborious, prone to errors and raises questions about reproducibility. To address these problems we introduce Qurro, a tool that interactively visualizes a plot of feature rankings (a ‘rank plot’) alongside a plot of selected features’ log-ratios within samples (a ‘sample plot’). Qurro’s interface includes various controls that allow users to select features from along the rank plot to compute a log-ratio; this action updates both the rank plot (through highlighting selected features) and the sample plot (through displaying the current log-ratios of samples). Here, we demonstrate how this unique interface helps users explore feature rankings and log-ratios simply and effectively.
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Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
The SILVA ribosomal RNA gene database project: improved data processing and web-based tools
Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P., Peplies, J., & Glöckner, F. O. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1219 · Cited by 33,055
Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2
Bolyen, E., Rideout, J. R., Dillon, M. R., Bokulich, N. A., Abnet, C. C., Al-Ghalith, G. A., Alexander, H., Alm, E. J., Arumugam, M., Asnicar, F., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0209-9 · Cited by 23,742
Self-citationMicrobiome Datasets Are Compositional: And This Is Not Optional
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., & Egozcue, J. J. (2017) · Frontiers in Microbiology · doi:10.3389/fmicb.2017.02224 · Cited by 3,044
Apprill, A., McNally, S., Parsons, R., & Weber, L. (2015) · Aquatic Microbial Ecology · doi:10.3354/ame01753 · Cited by 2,707
Deblur Rapidly Resolves Single-Nucleotide Community Sequence Patterns
Amir, A., McDonald, D., Navas-Molina, J. A., Kopylova, E., Morton, J. T., Zech Xu, Z., Kightley, E. P., Thompson, L. R., Hyde, E. R., Gonzalez, A., et al. (2017) · mSystems · doi:10.1128/msystems.00191-16 · Cited by 1,881
Self-citationFernandes, A. D., Reid, J. N., Macklaim, J. M., McMurrough, T. A., Edgell, D. R., & Gloor, G. B. (2014) · Microbiome · doi:10.1186/2049-2618-2-15 · Cited by 1,329
Research works printed but not linked: 23
- Morton, 2019, Establishing microbial composition measurement standards with reference frames, Nat. Commun., 10, 2719, 10.1038/s41467-019-10656-5 · DOI · Paper page
- Martino, 2019, A Novel Sparse Compositional Technique Reveals Microbial Perturbations, mSystems, 4, e00016-19, 10.1128/mSystems.00016-19 · DOI · Paper page
- Aitchison, 2002, Biplots of compositional data, J. R. Stat. Soc. C.-Appl., 51, 375, 10.1111/1467-9876.00275 · DOI · Paper page
- Becker, 1987, Brushing Scatterplots, Technometrics., 29, 127, 10.1080/00401706.1987.10488204 · DOI · Paper page
- Satyanarayan, 2016, Vega-Lite: A Grammar of Interactive Graphics, IEEE. T. Vis. Comput. Gr., 23, 341, 10.1109/TVCG.2016.2599030 · DOI · Paper page
- VanderPlas, 2018, Altair: Interactive Statistical Visualizations for Python, J. Open Source Softw., 3, 1057, 10.21105/joss.01057 · DOI · Paper page
- McDonald, 2012, The Biological Observation Matrix (BIOM) format or: how I learned to stop worrying and love the ome-ome, GigaScience., 1, 7, 10.1186/2047-217X-1-7 · DOI · Paper page
- Van Der Walt, 2011, The NumPy Array: A Structure for Efficient Numerical Computation, Comput. Sci. Eng., 13, 22, 10.1109/MCSE.2011.37 · DOI · Paper page
- McKinney, 2010, Data Structures for Statistical Computing in Python, 51, 10.25080/Majora-92bf1922-00a · DOI · Paper page
- Satyanarayan, 2015, Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization, IEEE. T. Vis. Comput. Gr., 22, 659, 10.1109/TVCG.2015.2467091 · DOI · Paper page
- Minich, 2019, Temporal, environmental, and biological drivers of the mucosal microbiome in a wild marine fish, Scomber japonicus, 10.1101/721555 · doi:10.1101/721555
- Kluyver, 2016, Jupyter Notebooks-a publishing format for reproducible computational workflows, 87
- Minich, 2018, KatharoSeq enables high-throughput microbiome analysis from low-biomass samples, mSystems, 3, e00218-17, 10.1128/mSystems.00218-17 · DOI
- Caporaso, 2010, QIIME allows analysis of high-throughput community sequencing data, Nat. Methods., 7, 335, 10.1038/nmeth.f.303 · DOI · Paper page
- Gonzalez, 2018, Qiita: rapid, web-enabled microbiome meta-analysis, Nat. Methods., 15, 796, 10.1038/s41592-018-0141-9 · DOI · Paper page
- Bokulich, 2018, Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin, Microbiome., 6, 90, 10.1186/s40168-018-0470-z · DOI · Paper page
- Parada, 2016, Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples, Environ. Microbiol., 18, 1403, 10.1111/1462-2920.13023 · DOI · Paper page
- Pedregosa, 2011, Scikit-learn: machine learning in Python, J. Mach. Learn. Res., 12, 2825
- Pratte, 2018, The gills of reef fish support a distinct microbiome influenced by host-specific factors, Appl. Environ. Microb., 84, e00063-18, 10.1128/AEM.00063-18 · DOI
- Scanlan, 2009, Ecological Genomics of Marine Picocyanobacteria, Microbiol. Mol. Biol. Rev., 73, 249, 10.1128/MMBR.00035-08 · DOI · Paper page
- Rivera-Pinto, 2018, Balances: a New Perspective for Microbiome Analysis, mSystems, 3, e00053-18, 10.1128/mSystems.00053-18 · DOI · Paper page
- Quinn, 2020, Amalgams: data-driven amalgamation for the reference-free dimensionality reduction of zero-laden compositional data, 10.1101/2020.02.27.968677 · doi:10.1101/2020.02.27.968677
- Steegen, 2016, Increasing transparency through a multiverse analysis, Perspect. Psychol. Sci., 11, 702, 10.1177/1745691616658637 · DOI · Paper page
Cited by
Taylor, B. C., Lejzerowicz, F., Poirel, M., Shaffer, J. P., Jiang, L., Aksenov, A., Litwin, N., Humphrey, G., Martino, C., Miller-Montgomery, S., et al. (2020) · mSystems · doi:10.1128/msystems.00901-19 · Cited by 203
Self-citationNonalcoholic Steatohepatitis and HCC in a Hyperphagic Mouse Accelerated by Western Diet
Ganguly, S., Muench, G. A., Shang, L., Rosenthal, S. B., Rahman, G., Wang, R., Wang, Y., Kwon, H. C., Diomino, A. M., Kisseleva, T., et al. (2021) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2021.05.010 · Cited by 50
Self-citationLee, S. M., Dong, T. S., Krause-Sorio, B., Siddarth, P., Milillo, M. M., Lagishetty, V., Datta, T., Aguilar-Faustino, Y., Jacobs, J. P., & Lavretsky, H. (2022) · International Psychogeriatrics · doi:10.1017/s1041610221000120 · Cited by 43
Metadata
- Authors
- Fedarko, M. W., Martino, C., Morton, J. T., González, A., Rahman, G., Marotz, C. A., Minich, J. J., Allen, E. E., & Knight, R.
- Venue
- NAR Genomics and Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-04-28
- Volume
- 2
- Issue
- 2
- Pages
- lqaa023-lqaa023
- ISSN
- 2631-9268 (ISSN-L)
- OpenAlex
- W3022006686
- Cited by
- 161
- References
- 7
- FWCI
- 8.09
- Type
- journal article
- Language
- en
- Open access
- gold
- Funders
- IBM Research AI
- Joint University Microelectronics Program (GI18518)
- University of California
- National Science Foundation (GRFP DGE-1144086)
- National Institute of Dental and Craniofacial Research (1F31DE028478)
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- Fedarko, M. W., Martino, C., Morton, J. T., González, A., Rahman, G., Marotz, C. A., Minich, J. J., Allen, E. E., & Knight, R. (2020). Visualizing ’omic feature rankings and log-ratios using Qurro. NAR Genomics and Bioinformatics. https://doi.org/10.1093/nargab/lqaa023
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